a

GAO

United States Government Accountability Office

Report to Congressional Requesters

May 2005 SECURITIES 

MARKETS

Decimal Pricing Has 

Contributed to Lower 

Trading Costs and a 

More Challenging 

Trading Environment

GAO-05-535
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

www.gao.gov/cgi-bin/getrpt?GAO-05-535.

To view the full product, including the scope 

and methodology, click on the link above. 

For more information, contact Richard J. 

Hillman at (202) 512-8678 or 

hillmanr@gao.gov. 

Highlights of GAO-05-535, a report to 

congressional requesters 

May 2005

SECURITIES MARKETS

Decimal Pricing Has Contributed to 

Lower Trading Costs and a More 

Challenging Trading Environment 

Trading costs, a key measure of market quality, have declined significantly 

for retail and institutional investors since the implementation of decimal 

pricing in 2001. Retail investors now pay less when they buy and receive 

more when they sell stock because of the substantially reduced spreads—

the difference between the best quoted prices to buy or sell. GAO’s analysis 

of data from firms that analyze institutional investor trades indicated that 

trading costs for large investors have also declined, falling between 30 to 53 

percent. Further, 87 percent of the 23 institutional investor firms we 

contacted reported that their trading costs had either declined or remained 

the same since decimal pricing began. Although trading is less costly, the 

move to the 1-cent tick has reduced market transparency. Fewer shares are 

now generally displayed as available for purchase or sale in U.S. markets. 

However, large investors have adapted by breaking up large orders into 

smaller lots and increasing their use of electronic trading technologies and 

alternative trading venues. 

 

Although conditions in the securities industry overall have improved 

recently, market intermediaries, particularly exchange specialists and 

NASDAQ market makers, have faced more challenging operating conditions 

since 2001. From 2000 to 2004, the revenues of the broker-dealers acting as 

New York Stock Exchange specialists declined over 50 percent, revenues for 

firms making markets on NASDAQ fell over 70 percent, and the number of 

firms conducting such activities shrank from almost 500 to about 260. 

However, factors other than decimal pricing have also contributed to these 

conditions, including the sharp decline in overall stock prices since 2000, 

increased electronic trading, and heightened competition from trading 

venues. 

Average Quoted Spreads Before and After Decimal Pricing Implemented in Cents per Share, 

February 2000 through November 2004 

0

5

10

15

20

25

 Oct.

 Mar.

 July

 Nov.

 Sept.

 Feb.

 June

 Aug.

 Oct.

 Dec.

 Jan.

 Apr.

 Aug.

 Dec. Jan.

 May

 June

 Feb.

 Apr.

Quoted spread (in cents)

Sample week (year and month)

Source: GAO.

Nasdaq

NYSE

Markets

decimalized

2000 2001 2002 2003 2004

In early 2001, U.S. stock and option 

markets began quoting prices in 

decimal increments rather than 

fractions of a dollar. At the same 

time, the minimum price 

increment, or tick size, was 

reduced to a penny on the stock 

markets and to 10 cents and 5 cents 

on the option markets. Although 

many believe that decimal pricing 

has benefited small individual 

(retail) investors, concerns have 

been raised that the smaller tick 

sizes have made trading more 

challenging and costly for large 

institutional investors, including 

mutual funds and pension plans. In 

addition, there is concern that the 

financial livelihood of market 

intermediaries, such as the brokerdealers that trade on floor-based 

and electronic markets, has been 

negatively affected by the lower 

ticks, potentially altering the roles 

these firms play in the U.S. capital 

market. GAO assessed the effect of

decimal pricing on retail and 

institutional investors and on 

market intermediaries. 

What GAO Recommends

GAO observes that the goals for 

implementing decimal pricing have 

been met and that investors have 

adapted to the new environment 

and continue to trade large 

numbers of shares at lower cost. 
Page i GAO-05-535 Securities Markets

Contents

Letter 1

Background 3

Results in Brief 6

Investors’ Trading Costs Have Declined Since Decimalization, but 

Reduced Market Transparency Has Caused Firms to Adopt New 

Trading Strategies 8

Some Stock Intermediaries Have Experienced Lower Profits since 

Decimalization, but Other Factors Have Contributed to the 

Declines 44

Decimal Pricing Has Had a Limited Impact on the Options Markets, 

but Other Factors Have Helped Improve Market Quality 60

Observations 71

Agency Comments 72

Appendixes

Appendix I: Scope and Methodology 74

Methodology for Assessing Impact on Institutional Investors 74

Methodology for Assessing Impact on Market Intermediaries 75

Methodology for Assessing Impact on Options Markets 76

Appendix II: Methodology for GAO Analysis of Trade and Quotes Data 77

Appendix III: Measurement of Institutional Investors’ Trading Costs in 

Basis Points Shows Decline since Decimal Pricing 

Implemented 96

Appendix IV: Additional Analysis Using Trade and Quotes Data 106

Appendix V: GAO Contacts and Staff Acknowledgments 110

111

116

Tables Table 1: Average Quoted Spreads Before and After Decimalization, 

2000–2004 (cents per share) 11

Table 2: Average Effective Spreads Before and After 

Decimalization, 2000–2004 (cents per share) 12

Table 3: Volume-weighted Average Effective Spreads Before and 

After Decimalization for Selected NYSE and NASDAQ 

Stocks, by Market Capitalization (cents per share) 13

Table 4: Institutional Investor Positions on Changes to Trading 

Costs After Decimalization 28
Contents

Page ii GAO-05-535 Securities Markets

Table 5: Price Change Volatility for NYSE and NASDAQ Stocks 

Before and After Decimalization 31

Table 6: Average Number of Shares Displayed at the Best Quoted 

Prices Reported by NYSE and NASDAQ in Studies of Their 

Markets Before and After Decimalization 32

Table 7: Average Trade Size for NYSE and NASDAQ, 1999–2004 (in 

shares) 37

Table 8: NYSE Specialist Firm Revenues and Profits, 1999–2004 (in 

millions of dollars) 47

Table 9: NYSE Reported Trades, Average Daily Volume, and 

Average Trade Size, 1999–2004 49

Table 10: NYSE Member Broker-Dealer Revenues from NASDAQ 

Market Making Activities, 1999–2004 (in millions of 

dollars) 50

Table 11: NASDAQ Average Trade Size, and Average Daily Volume, 

1999–2004 51

Table 12: Number of Specialist Firms Operating on Selected Stock 

Markets, 1999–2004 51

Table 13: Consolidation among NASDAQ Market Makers, 

1999–2004 52

Table 14: Pre- and Postdecimalization Sample Weeks 81

Table 15: Price Characteristics of NYSE-Listed and NASDAQ 

Stocks 86

Table 16: Volume Characteristics of NYSE-Listed and NASDAQ 

Stocks 88

Table 17: Average Quoted Spreads Before and After Decimalization, 

2000–2004 (basis points) 106

Table 18: Average Effective Spreads Before and After 

Decimalization, 2000–2004 (basis points) 107

Figures Figure 1: Average Quoted Spreads Before and After Decimalization 

(cents per share) 10

Figure 2: Total Trading Costs from a Trade Analytics Firm for NYSE 

and NASDAQ Stocks, 1999–2003 (cents per share) 17

Figure 3: Trading Cost Components from a Trade Analytics Firm 

for NYSE and NASDAQ Stocks, 2001 and 2003 (cents per 

share) 19

Figure 4: Total Trading Costs from Two Trade Analytics Firms for 

NYSE, 1998–2004 (cents per share) 21

Figure 5: Total Trading Costs from Two Trade Analytics Firms for 

NASDAQ Stocks, 1998–2004 (cents per share) 22
Contents

Page iii GAO-05-535 Securities Markets

Figure 6: Trading Cost Components from Two Trade Analytics 

Firms for NYSE Stocks, 2001 and 2004 (cents per 

share) 23

Figure 7: Trading Cost Components from Two Trade Analytics 

Firms for NASDAQ Stocks, 2001 and 2004 (cents per 

share) 24

Figure 8: Volume-weighted Average Number of Shares Displayed at 

the Best Quoted Prices on the NYSE and NASDAQ Before 

and After Decimalization, Sample Weeks from February 

2000–November 2004 33

Figure 9: Proportion of Total Share Trading Volume NASDAQ and 

NYSE Stocks by ECNs, 1996–2003 40

Figure 10: Securities Industry Total Revenues and Net Income, 

1994–2004 46

Figure 11: NYSE Specialist Participation Rates, 1999-2004, in 

Percent of Trades 48

Figure 12: Securities Industry Revenues and Net Income as 

Compared to the Performance of the S&P 500 Stock 

Index, 1994–2004 54

Figure 13: Number of IPOs and Dollars Raised, 1994–2004 58

Figure 14: Total Contract Trading Volumes for Stock Options, 2000–

2004 (Volume in millions) 63

Figure 15: Distribution of Average Daily Closing Prices for Full 

Sample of Matching Stocks and 300 Matched-Pairs 

Sample 87

Figure 16: Distribution of Average Daily Trading Volume for Full 

Sample of Matching Stocks and 300 Matched-Pairs 

Sample 89

Figure 17: Total Trading Costs from a Trade Analytics Firm for NYSE 

and NASDAQ Stocks, 1999–2004 (basis points) 98

Figure 18: Trading Cost Components from One Trade Analytics Firm 

for NYSE and NASDAQ, 2001–2003 (basis points) 99

Figure 19: Total Trading Costs from Two Trade Analytics Firms for 

NYSE Stocks, 2001–2004 (basis points) 101

Figure 20: Total Trading Costs from Two Trade Analytics Firms for 

NASDAQ Stocks, 2001–2004 (basis points) 102

Figure 21: Trading Cost Components from Two Trade Analytics 

Firms for NYSE Stocks, 2001 and 2004 (basis points) 103

Figure 22: Trading Cost Components from Two Trade Analytics 

Firms for NASDAQ Stocks, 2001 and 2004 (basis 

points) 104

Figure 23: Quote Clustering After Decimalization, 2001-2004 108
Contents

Page iv GAO-05-535 Securities Markets

Figure 24: Quote Clustering After Decimalization, by Sample Week, 

2001-2004 109
Contents

Page v GAO-05-535 Securities Markets

Abbreviations

Amex American Stock Exchange

ATS alternative trading system

BOX Boston Options Exchange

bps basis points

CBOE Chicago Board Options Exchange

CQ Consolidated Quotes

CMS composite match score

ECN electronic communication network

FOCUS Financial and Operational Combined Uniform Single

GAO Government Accountability Office

IBM International Business Machines

IPO initial public offering

ISE International Securities Exchange

MMID market maker identification

mps messages per second

NASD National Association of Securities Dealers

NASDAQ National Association of Securities Dealers Automated 

Quotations 

NBB national best bid

NBO national best offer

NBBO national best bid and offer

NMS National Market System

NYSE New York Stock Exchange

OPRA Options Price Reporting Authority

OTC over-the-counter

OTCBB over-the-counter bulletin board

PCX Pacific Exchange

Phlx Philadelphia Stock Exchange

SEC Securities and Exchange Commission

SIA Securities Industry Association

TAQ Trade and Quote 

VWAP volume weighted average price

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Page 1 GAO-05-535 Securities Markets

United States Government Accountability Office

Washington, D.C. 20548

A

May 31, 2005 Letr

The Honorable Michael Enzi

United States Senate

The Honorable Rick Santorum

United States Senate

With encouragement from Congress, in 2000 the Securities and Exchange 

Commission (SEC) ordered U.S. stock and option markets to begin quoting 

prices in decimal increments rather than fractions of a dollar.1

 As U.S. 

markets implemented decimal pricing in early 2001, they also reduced the 

minimum price increment, or tick size, at which prices could be quoted. 

The minimum tick on the stock markets generally fell from 1/16 of a dollar 

to a penny and on the option markets from 1/8 and 1/16 of a dollar to 10 

cents and 5 cents, respectively.2

 The United States had been one of the last 

countries to use fractions on its markets, and decimal pricing was expected 

to simplify securities pricing for investors, help lower investors’ trading 

costs and align U.S. pricing standards with those of other markets. 

Many market participants and others who have observed the markets 

believe that decimal pricing has benefited small retail investors seeking to 

buy or sell a few hundred shares of stock.3

 But concerns have been raised 

that the smaller tick size has made trading more challenging and costly for 

large institutional investors, including mutual funds and pension plans, that 

trade large blocks of shares.4

 In addition, concerns exist over whether 

trading in 1-cent ticks has negatively affected the financial livelihood of 

market intermediaries, such as the broker-dealers that trade on floor-based 

1

A stock is a security that signifies an ownership position in a company. An option contract 

provides the purchaser the right to buy (call) or sell (put) a fixed amount of a given security 

at a specified price within a limited period of time. 

2

For option contracts priced $3 and above, the tick size was reduced from 1/8 of a dollar 

(12.5 cents) to 10 cents, and the tick size for contracts priced below $3 was reduced from 

1/16 of a dollar (6.25 cents) to 5 cents. 

3

As used in this report, retail investors are individuals who buy or sell securities for their 

own accounts. 

4

Institutional investors are entities, such as mutual funds, insurance companies, pension 

plans, or charitable organizations, that invest on behalf of themselves or others. Such 

investors typically have large pools of assets and buy and sell securities in large quantities or 

blocks. The stock markets classify block trades as those involving 10,000 shares or more.
Page 2 GAO-05-535 Securities Markets

and electronic markets, potentially altering the roles these firms play in the 

U.S. capital market. 

This report responds to your February 12, 2004, request that we study the 

impact of decimal pricing on the trading of U.S. stocks and options. As 

agreed with your staffs, our objectives were to study the impact of decimal 

pricing on (1) retail and institutional investors, (2) market intermediaries, 

and (3) options market investors and intermediaries.

To determine the effect of decimalization on retail and institutional 

investors in securities, we analyzed a comprehensive database of all trades 

conducted on U.S. stock markets from February 2000 to November 2004 to 

identify changes to key characteristics of stock markets, such as spreads, 

liquidity, trading volumes, and price volatility.5

 We also analyzed data on 

institutional investors’ trading costs that were provided by three trade 

analytics firms in order to identify trends in these costs before and after 

decimalization. In addition, we reviewed relevant academic, industry, and 

regulatory studies that address the effects of decimal pricing on the stock 

markets. Finally, we interviewed almost 70 market participants, including 

securities traders, broker-dealers, and institutional investors such as 

pension and mutual fund investment managers, as well as representatives 

of regulatory agencies, stock markets, electronic trading systems, and 

industry associations. To determine decimalization’s effect on 

intermediaries in U.S. stock markets, we reviewed studies and data on 

market participants’ revenue and profitability and interviewed a variety of 

intermediaries, including broker-dealers, market makers, regional and 

national exchange specialists, and traders. We sought the perspectives of 

other market participants, including representatives from regulatory 

agencies, stock markets, industry associations, and institutional investors. 

To determine the effect of decimal pricing and the tick size reductions on 

investors and intermediaries in the options market, we reviewed studies by 

options exchanges; interviewed representatives of all six U.S. options 

markets, as well as broker-dealers and hedge funds that trade options; and 

reviewed comment letters that SEC received on potential changes in 

options market regulations. Appendixes I and II contain a full description 

of our scope and methodology. We conducted our work in Baltimore, 

Boston, Chicago, Los Angeles, New York, Philadelphia, San Francisco, and 

5

This analysis used the Trade and Quotes (TAQ) database maintained by the New York Stock 

Exchange. The TAQ contains records of all trades and price quotes from all U.S. exchanges 

and the NASDAQ Stock Market. 
Page 3 GAO-05-535 Securities Markets

Washington, D.C., between May 2004 and May 2005 in accordance with 

generally accepted government auditing standards. 

Background In implementing decimal pricing, regulators hoped to improve the quality 

of U.S. stock and option markets. The quality of a market can be assessed 

using various characteristics, but the trading costs that investors incur 

when they execute orders are a key aspect of market quality. Trading costs 

are generally measured differently for retail and institutional investors. In 

addition to the commission charges to paid broker-dealers that execute 

trades, the other primary trading cost for retail investors, who typically 

trade no more than a few hundred shares at a time, is measured by the 

spread, which is the difference between the best quoted “bid” and “ask” 

prices that prevail at the time the order is executed. The bid price is the 

best price at which market participants are willing to buy shares, and the 

ask price is the best price at which market participants are willing to sell 

shares.6

 The spread represents the cost of trading for small orders because 

if an investor buys shares at the ask price and then immediately sells them 

at the bid price, the resulting loss or cost is represented by the size of the 

spread. 

Because institutional orders are generally much larger than retail orders 

and completing one order can require multiple trades executed at varying 

prices, spreads are not generally used to measure institutional investors’ 

trading costs. Instead, the components of trading costs for large 

institutional investors, who often seek to buy or sell large blocks of shares 

such as 50,000 or 1 million shares, include the order’s market impact, 

broker commissions paid, and exchange fees incurred, among other things. 

An order’s market impact is the extent to which the security changes in 

price after the investor begins trading. For example, if the price of a stock 

begins to rise in reaction to the increased demand after an investor begins 

executing trades to complete a large order, the average price at which the 

investor’s total order is executed will be higher than the stock’s price would 

have been without the order. 

6

In general, for a given stock transaction the best bid (ask) price represents the highest 

(lowest) price available from all U.S. market venues providing quotes to sellers (buyers) of 

the stock. This is known as the national best bid and offer, or NBBO.
Page 4 GAO-05-535 Securities Markets

In addition to trading costs, decimal pricing may have affected several 

other aspects of market quality, including liquidity, transparency, and price 

volatility. 

Liquidity. Liquid markets have many buyers and sellers willing to trade 

and have sufficient shares to execute trades quickly without markedly 

affecting share prices. Generally, the more liquid the overall market or 

markets for particular stocks are, the lower the market impact of any 

individual orders. Small orders for very liquid stocks will have minimal 

market impact and lower trading costs. However, larger orders, particularly 

for less liquid stocks, can affect prices more and thus have greater market 

impact and higher trading costs.

Transparency. When markets are transparent, the number and prices of 

available shares are readily disclosed to all market participants, and prices 

and volumes of executed trades are promptly disseminated. A key factor 

that can affect market participants’ perceptions of market transparency is 

the volume of shares publicly displayed as available at the best quoted bid 

and ask prices, as well as at points around these prices—known as market 

depth. Markets with small numbers of shares displayed in comparison to 

the size of investors’ typical orders seem less transparent to investors 

because they have less information that can help them specify the price and 

size of their own orders so as to execute trades with minimal trading costs. 

Price volatility. Price volatility is a measure of the frequency of price 

changes as well as a measure of the amount by which prices change over a 

period of time. Highly volatile markets typically disadvantage investors that 

execute trades with less certainty of the prices they will receive. 

Conversely, market intermediaries, such as broker-dealers, can benefit 

from highly volatile markets because they may be able to earn more 

revenue from trading more frequently as prices rise and fall. 

The trading that occurs on U.S. securities markets is facilitated by brokerdealers that act as market intermediaries. These intermediaries perform 

different functions depending on the type of trading that occurs in each 

market. On markets that use centrally located trading floors to conduct 

trading, such as the New York Stock Exchange (NYSE), trading occurs 

primarily through certain broker-dealer firms that have been designated as 

specialists for particular stocks. These specialists are obligated to maintain 

fair and orderly markets by buying shares from or selling shares to the 

other broker-dealers who present orders from customers on the trading 

floor or through the electronic order routing systems used by the exchange. 
Page 5 GAO-05-535 Securities Markets

Interacting with the specialists on the trading floor are employees from 

large broker-dealer firms that receive orders routed from these firms’ 

offices around the country. In addition, specialists receive orders from staff 

from small, independent broker-dealer firms who work only on the floor. 

In contrast, trading of the stocks listed on the NASDAQ Stock Market 

(NASDAQ), which does not have a central physical trading location, is 

conducted through electronic systems operated by broker-dealers acting as 

market makers or by alternative trading venues. For particular stocks, 

market makers enter quotes indicating the prices at which these firms are 

simultaneously willing to buy from or sell shares to other broker-dealers 

into NASDAQ’s electronic system. The NASDAQ system displays these 

quotes to all other broker-dealers that are registered to trade on that 

market. Much of the trading in NASDAQ stocks now also takes place in 

alternative trading venues, including electronic communication networks 

(ECN), which are registered as broker-dealers and electronically match the 

orders they receive from their customers, much like an exchange. 

At the same time that decimal pricing was being implemented, other 

changes were also occurring in the marketplace. For example, in 1997, SEC 

enacted new rules regarding how market makers and specialists must 

handle the orders they received from their customers, including requiring 

firms to display these orders to the market when their prices are better 

than those currently offered by that broker.7

 These rules facilitated the 

growth of additional trading venues such as the ECNs, which compete with 

the established markets, such as NYSE and NASDAQ, for trading volumes. 

The increased use of computerized trading has also provided alternative 

mechanisms for trading and reduced the role of specialists, market makers, 

and other intermediaries in the trading process. In addition, after rising 

significantly during the late 1990s, U.S. stock prices experienced several 

years of declines, affecting trading costs and market intermediary profits. 

Facing lower investment returns, institutional investors and professional 

traders have focused more on reducing trading costs to improve those 

returns. Regulators also began placing greater emphasis on institutional

7

Securities Exchange Act Release No. 37619A (Sept. 6, 1996), 61 FR 48290 (Sept. 12, 1996).
Page 6 GAO-05-535 Securities Markets

investors’ duty to obtain the best execution for their trades, further 

increasing the pressure on these firms to better manage their trading costs.8

Results in Brief Trading costs, a key measure of market quality, have declined significantly 

for retail and institutional investors since the implementation of decimal 

pricing in 2001. Retail investors are now able to trade small orders that 

execute in one trade more cheaply as a result of the substantially reduced 

spreads that prevail in the stock markets. Data from firms that analyze 

institutional investors’ trading costs and academic studies also showed that 

trading costs for large investors have also declined. Further, 20 of the 23 

institutional investor firms we contacted (representing about 31 percent of 

assets managed by the top 300 U.S. money management firms) reported 

that their trading costs had either declined or remained the same since 

decimal pricing began. The extent to which decimal pricing is responsible 

for these improvements is not clear because other factors, including the 

multiyear downturn in stock prices that began in 2000, may have also 

contributed to the reduced trading costs. Although trading is less costly, the 

move to the 1-cent tick appears to have reduced market transparency as 

the number of shares that are generally displayed as available for purchase 

or sale in U.S. stock markets shrank. In part, institutional investors became 

less willing to display large orders to the markets because the 1-cent tick 

lowered the financial risks for other traders seeking to “step ahead” of 

these larger orders by entering orders priced just a penny better. 

Institutional investors told us that they had adapted to these new 

conditions by breaking up large orders into smaller lots and using 

electronic trading technologies to execute these smaller orders in the 

markets. In addition, they reported increasing their use of alternative 

trading venues, such as ECNs and crossing networks that anonymously 

match large institutional investor orders. Through these adaptations, 

institutional investors have been able to continue executing large orders at 

reduced costs.

8

For example, the Chartered Financial Analyst Institute, which sets standards for 

investment professionals, issued guidelines on trade management that emphasize the need 

for investment managers to seek to achieve best execution for their clients. In addition, a 

top SEC examination official noted in a speech in 2002 that firms should increase their 

efforts to better ensure that the broker-dealers they use are achieving the best executions 

for their trades. 
Page 7 GAO-05-535 Securities Markets

Although investors appear to have benefited since decimal pricing began, 

some market intermediaries have faced more challenging operating 

conditions. Despite overall improving conditions in the securities industry 

since 2001, broker-dealers acting as exchange specialists and NASDAQ 

market makers have seen their profits fall, forcing some to merge with 

other firms or to leave the industry. Between 2000 and 2004, the exchange 

specialist broker-dealers that match investor orders and buy and sell shares 

on the trading floors of various exchanges experienced reduced revenues 

and profits. For example, in 2004 NYSE exchange specialists reported 

aggregate revenues of $902 million, down by more than 50 percent from the 

$2.1 billion such firms earned in 2000. Broker-dealers that make markets in 

NASDAQ and other non-exchange listed stocks appear to have been 

affected even more by the lower spreads and reductions in displayed 

liquidity that have accompanied decimal pricing. According to data from 

the Securities Industry Association, aggregate revenues for these firms 

declined more than 70 percent between 2000 and 2004, falling from $9 

billion to $2.5 billion. Since 2001, market intermediaries conducting certain 

activities have consolidated. For example, the number of NYSE specialist 

firms fell from 25 in 1999 to 7 in 2004, and the number of NASDAQ market 

makers declined from almost 500 in 2000 to about 260 in 2004. However, 

factors other than decimal pricing have also contributed to these 

conditions, including the sharp decline in overall stock prices since 2000, 

reduced revenues from customers’ increasing use of electronic trading 

strategies, and heightened competition from ECNs and other electronic 

trading venues. Market participants noted that these trends had been in 

place before decimalization. We found that market intermediaries had 

attempted to adapt to the new conditions by changing their business 

practices. For example, NASDAQ market makers had begun charging 

commissions on trades, broker-dealers had invested heavily in 

technological trading devices and data management systems, and other 

firms had reduced the sizes of their trading staffs. These conditions and the 

perceived decline in displayed liquidity in U.S. stock markets has caused a 

proposal to be made to conduct a pilot study of the use of higher minimum 

ticks for stock trading. Such a pilot was favored by most of the market 

intermediaries we contacted but by only about half of the institutional 

investors interviewed, and some of those that were open to testing larger 

tick sizes for trading saw them as being useful primarily for less liquid 

stocks rather than for all stocks.

The effect of decimal pricing for options trading has been less significant. 

In part, options markets were less affected because the tick sizes that 

accompanied decimal pricing did not represent large changes from those 
Page 8 GAO-05-535 Securities Markets

previously in use. Nevertheless, the quality of U.S. options markets, as 

measured by their trading costs, liquidity, and increased trading volumes, 

has improved since 2001. However, options markets participants attributed 

these improvements primarily to other changes, including the increased 

competition arising from multilisting (the trading of options on the same 

securities on multiple exchanges), which began in 1999, and the 

establishment of new electronic exchanges and trading systems. Decimal 

pricing’s effect on options market intermediaries such as market makers 

and specialists has been mixed, with market participants indicating that 

floor-based firms have experienced declining revenues and profitability and 

electronic-based firms are seeing increased trading revenues and 

profitability. A 2004 SEC release sought industry comments on a range of 

issues pertaining to options markets, including whether these markets 

should use 1-cent ticks. However, officials of options exchanges and firms 

we contacted and virtually all of those providing comments to SEC were 

strongly opposed to lowering minimum price increments to one penny for 

options. Many were concerned that penny ticks would generate large 

numbers of price quote messages that would overwhelm the transmission 

and processing capacity of the existing market and data vendor systems. 

They also feared that lower intermediary revenues and more price points 

would reduce liquidity in the options markets. 

In their comments on a draft of this report, staff from SEC’s Division of 

Market Regulation and Office of Economic Analysis said that, overall, the 

report accurately depicted conditions in the markets after the 

implementation of decimal pricing. 

Investors’ Trading 

Costs Have Declined 

Since Decimalization, 

but Reduced Market 

Transparency Has 

Caused Firms to Adopt 

New Trading Strategies

Trading costs for both retail and institutional investors fell after the 

implementation of decimal pricing and the corresponding reduction in tick 

size. While decimalization appears to have helped to lower these costs, 

other factors—such as the multiyear downturn in stock prices—also likely 

contributed to these cost reductions. Although trading costs and other 

market quality measures improved after decimal pricing’s implementation, 

another measure—the transparency of U.S. stock markets—declined 

following the reduction in tick size in 2001 because fewer shares were 

displayed as available for trading. However, most market participants we 

interviewed reported they have been able to continue to execute large 

orders by using electronic trading tools to submit a larger volume of 

smaller orders and making greater use of alternative trading venues.
Page 9 GAO-05-535 Securities Markets

Decimal Pricing Reduced 

Trading Costs for Retail 

Investors

In ordering U.S. markets to convert to decimal pricing, SEC had several 

goals.9 These included making securities pricing easier for investors to 

understand and aligning U.S. markets’ pricing conventions with those of 

foreign securities markets. Decimalization appears to have succeeded in 

meeting these goals. In addition, SEC hoped that decimal pricing would 

result in lower investor trading costs, as lower tick sizes would spur 

competition that would lead to reduced spreads. Narrower spreads benefit 

retail investors because retail size orders generally execute in one trade at 

one price. Prior to being ordered to implement decimal pricing, U.S. stock 

markets had voluntarily reduced their minimum ticks from 1/8 to 1/16 of a 

dollar, and studies of these actions found that spreads declined as a result. 

Following decimalization and the implementation of the 1-cent tick in 2001, 

retail investor trading costs declined further as spreads were narrowed 

even more substantially.10 To analyze the effects of decimal pricing, we 

selected a sample of 300 pairs of NYSE-listed and NASDAQ stocks with 

similar characteristics (like share price and trading activity).11 We 

examined several weeks before and after the implementation of decimal 

pricing and found that spreads declined after decimal prices were 

implemented and remained low through 2004. Our study considered 12 

weeklong sample periods from February 2000 to January 2001 (our 

predecimalization period) and 12 weeklong sample periods from April 2001 

through November 2004 (our postdecimalization period). As shown in 

figure 1, quoted spreads continued a steady decline on both NYSE and 

9

SEC, Division of Market Regulation: Order Directing the Exchanges and NASD to Submit a 

Decimalization Implementation Plan, Exchange Act Release No. 42360 (January 28, 2000), 

65 Fed. Reg. 5003 (2000). 

10Another component of small investors’ trading costs is the commission they pay to brokerdealers for executing their trades. The move to decimal pricing was not expected to change 

retail commissions and thus have not been included in our analysis of retail investor costs.

11For this analysis, we selected pairs of NYSE and NASDAQ stocks by matching stocks with 

similar trading and stock characteristics. By generating these pairs, we attempted to prevent 

our results from being influenced by the differences between stocks’ characteristics so as to 

better isolate the impact of decimal pricing alone. By selecting pairs of NYSE and NASDAQ 

stocks, our sample may be biased because the smallest NASDAQ stocks are not generally 

comparable in characteristics to NYSE stocks; this bias may tend to overstate the benefits of 

decimalization such as reductions in spreads and thus caution should be used in 

generalizing our results. However, our matched pairs also tended to underrepresent stocks 

with higher daily trading volume, which likely would bias our results toward understating 

spread reductions.
Page 10 GAO-05-535 Securities Markets

NASDAQ following the implementation of decimal pricing, falling to levels 

well below those that existed before the conversion to decimal pricing. 

Figure 1: Average Quoted Spreads Before and After Decimalization (cents per share)

Note: The figure presents the average spread for the stocks in our sample from a 5-day period (a 

trading week) in each of the above listed months. Our sample weeks exclude any from February and 

March 2001 because not all stocks were trading using decimal prices during the transition period. The 

change in spread for each stock in this analysis was weighted by its trading volume relative to the total 

trading volume. See appendix II for a detailed explanation of the methodology for this analysis. 

Our analysis of the TAQ data also found that quoted spreads declined for 

stocks with varying levels of trading volume. As shown in table 1, quoted 

spreads declined significantly after decimal pricing began for the most 

actively traded stocks, those with medium levels of trading volume, and 

also for those with the lowest amount of daily trading activity, with the 

0

5

10

15

20

25

Nov. (1-5)

 July (19-23)

 Mar. (8-12)

 Oct. (20-24)

 June (2-6)

 Feb. (24-28)

 Sept. (23-27)

 May (6-10)

 Jan. (7-11)

 Dec. (10-14)

 Aug. (20-24)

 Apr. (23-27)

 Jan. (22-26)

 Dec. (18-22)

 Nov. (6-10)

 Oct. (23-27)

 Sept. (18-22)

 Aug. (21-25)

 July (10-14)

 June (19-23)

 May (8-12)

 Apr. (10-14)

 Mar. (20-24)

 Feb. (7-11)

Quoted spread (in cents)

2000 2001 2002 2003 2004

Sample week (year, month, and days)

Markets

decimalized

Source: GAO.

Nasdaq

NYSE
Page 11 GAO-05-535 Securities Markets

average quoted spread falling 73 percent for NYSE stocks and 68 percent 

for NASDAQ stocks. 

Table 1: Average Quoted Spreads Before and After Decimalization, 2000–2004 (cents per share)

Source: GAO analysis of TAQ data.

Note: Quoted spreads in the table represent the volume-weighted average quoted spread (i.e., stocks 

and weeks with more total trading volume have greater weight) over 12 sample weeks during the 

predecimals period (February 2000–January 2001) and 12 sample weeks during the postdecimals 

period (April 2001–November 2004) for our sample of stocks. Stocks were segregated by volume 

according to the following categories:

• High volume stocks were those in our sample of stocks with average daily trading volumes 

exceeding 500,000 shares.

• Medium volume stocks were those in our sample of stocks with average daily trading volumes 

between 100,000 and 499,999 shares.

• Low volume stocks were those in our sample of stocks with average daily trading volumes of less 

than 100,000 shares.

Quoted spreads are time-weighted across quotes (quotes in effect longer have greater weights) and 

volume-weighted across stocks (stocks with more shares traded have greater weight).

While the quoted spread measure is useful for illustrative purposes, a better 

measure of the cost associated with the bid-ask spread is the effective 

spread, which is twice the difference between the price at which an 

investor’s trade is executed and the midpoint between the quoted bid and 

ask prices that prevailed at the time the order was executed.12 Thus, the 

effective spread measures the actual costs of trades occurring rather than 

just the difference between the best quoted prices at the time of the trade. 

As shown in table 2, effective spreads declined by 62 percent for our NYSE 

NYSE quoted spread NASDAQ quoted spread

Stocks by average 

daily volume of shares 

traded 

Average

spread in cents

before

decimals

Average

spread in cents

after decimals Percent change

Average

spread in cents

before

decimals

Average

spread in cents

after decimals Percent change

 High 14.93 2.77 -81% 12.95 2.74 -79%

 Medium 14.94 3.78 -75 15.58 4.47 -71

 Low 16.25 5.26 -68 18.97 7.69 -59

All stocks 15.39 4.18 -73 16.96 5.39 -68

12For example, the effective spread for a trade executed for an investor at a price of $10.03 

for stock that was purchased when the bid-ask prices were $10.01 (bid) and $10.03 (ask) 

would be 2 cents per share. 
Page 12 GAO-05-535 Securities Markets

sample stocks and 59 percent for our NASDAQ sample stocks between the 

periods after decimal pricing was implemented.

Table 2: Average Effective Spreads Before and After Decimalization, 2000–2004 (cents per share)

Source: GAO analysis of TAQ data.

Note: Effective quoted spreads (the difference between the price at which a trade is executed and the 

midpoint between the prevailing quoted bid and ask prices) in the table represent the volume-weighted 

average effective spread (i.e., stocks and weeks with more total trading volume have greater weight) 

over 12 sample weeks during the predecimals period (February 2000–January 2001) and 12 sample 

weeks during the postdecimals period (April 2001–November 2004) for our sample of stocks. Stocks 

were segregated by volume according to the following categories:

• High volume stocks were those in our sample of stocks with average daily trading volumes 

exceeding 500,000 shares.

• Medium volume stocks were those in our sample of stocks with average daily trading volumes 

between 100,000 and 499,999 shares.

• Low volume stocks were those in our sample of stocks with average daily trading volumes of less 

than 100,000 shares.

NYSE effective spreads NASDAQ effective spreads

Stocks by average 

daily volume of shares 

traded 

Average

spread in cents

before

decimals

Average

spread in cents

after decimals

Percent

change

Average

spread in cents

before

decimals

Average

spread in cents

after decimals Percent change

 High 14.85 4.71 -68% 15.85 4.86 -69%

 Medium 13.17 4.95 -62 15.14 6.15 -59

 Low 12.86 6.37 -50 16.00 8.27 -48

All stocks 13.36 5.05 -62 15.66 6.48 -59
Page 13 GAO-05-535 Securities Markets

In addition, several academic and industry studies found similar results. 

For example, one academic study examined differences in trade execution 

cost and market quality measures in 300 NYSE stocks and 300 NASDAQ 

stocks (matched on market capitalization) for several weeks before 

decimal pricing was fully implemented on NYSE stocks and after both 

markets converted to decimal pricing. As shown in table 3, the study found 

that average effective spreads declined by 41 percent for the NYSE stocks 

and by 54 percent for the NASDAQ stocks from the predecimalization 

sample period (January 8–26, 2001) to the postdecimalization sample 

period (April 9–August 31, 2001).13 As the table also shows, the study found 

that spreads declined the most for NYSE stocks with the largest market 

capitalizations and for NASDAQ stocks with the smallest market 

capitalizations.14

Table 3: Volume-weighted Average Effective Spreads Before and After Decimalization for Selected NYSE and NASDAQ Stocks, 

by Market Capitalization (cents per share)

Source: Hendrik Bessembinder.

13Hendrik Bessembinder, “Trade Execution Costs and Market Quality after Decimalization,”

Journal of Financial and Quantitative Analysis, vol. 38, no. 4, 760.

14Market capitalization is a company’s share price multiplied by the number of shares 

outstanding. 

NYSE effective spreads NASDAQ effective spreads

Stocks by market 

capitalization

Before decimals

(cents)

After decimals

(cents)

Percent

change

Before decimals

(cents)

After decimals

(cents)

Percent

change

 Large 12.51 6.93 -45% 12.55 5.61 -55%

 Medium 11.78 9.76 -17 14.76 8.97 -39

 Small 17.05 12.50 -27 18.89 7.56 -60

All stocks 12.67 7.45 -41 12.66 5.78 -54
Page 14 GAO-05-535 Securities Markets

Similar declines in spreads were also reported in studies that SEC required 

the various markets to conduct as part of its order directing them to 

implement decimal pricing. For example, in its impact study, NYSE 

reported that share-weighted average effective spreads declined 43 percent 

for all 2,466 NYSE-listed securities trading in the pre- and 

postdecimalization sample periods the exchange selected.15 NASDAQ’s 

study found that effective spreads declined between its sample periods by 

an average of 46 percent for the 4,766 NASDAQ securities that converted to 

penny increments on April 9, 2001.16 In addition, an official at a major U.S. 

stock market told us that all the research studies that he reviewed on the 

impact of decimal pricing concluded that spreads narrowed overall in 

response to the reduction in tick size.

Many market participants we interviewed also indicated that retail 

investors benefited from the narrower spreads that followed 

decimalization and the adoption of 1-cent ticks. For example, a 

representative of a firm that analyzes trading activities of large investors 

told us that investors trading 100 shares are better off following 

decimalization because small trades can be executed at the now lower best 

quoted prices. Representatives from two broker-dealers stated that the 

narrower spreads that prevailed following decimalization meant that more 

money stayed with the buyers and sellers of stock rather than going to 

market intermediaries such as brokers-dealers and market makers. 

Furthermore, the chief financial officer of a small broker-dealer told us that 

retail investors had benefited from the adoption of the 1-cent tick because 

their orders can generally be executed with one transaction at a single 

price unlike those of institutional investors, which are typically larger than 

the number of shares displayed as available at the best prices.

15New York Stock Exchange, Inc., Decimalization of Trading on the New York Stock 

Exchange: A Report to the Securities and Exchange Commission, September 7, 2001. For 

the predecimalization sample period, the NYSE used the 19 trading days from August 1, 

2000, through August 25, 2000. The postdecimalization sample period is composed of the 21 

trading days in June 2001.

16The NASDAQ Stock Market, Inc., The Impact of Decimalization on the NASDAQ Stock 

Market: Final Report to the SEC, June 11, 2001. The predecimalization sample period 

NASDAQ used included the 2 weeks before and the 2 weeks after the final date all NASDAQ 

securities had converted to penny increments on April 9, 2001.
Page 15 GAO-05-535 Securities Markets

Institutional Investors’ 

Trading Costs Have Also 

Declined Since 

Decimalization

Analysis of the multiple sources of data that we collected generally 

indicated that institutional investors’ trading costs had declined since 

decimal prices were implemented. We obtained data from three leading 

firms that collect and analyze information about institutional investors’ 

trading costs. These trade analytics firms (Abel/Noser, Elkins/McSherry, 

and Plexus Group) obtain trade data directly from institutional investors 

and brokerage firms and then calculate trading costs, including market 

impact costs, typically for the purpose of helping investors and traders 

limit costs of trading.17 These firms also aggregate client data in order to 

approximate total average trading costs for all their institutional investor 

clients. Generally, the client base represented in these firms’ aggregate 

trade cost data is broad enough to be sufficiently representative of all 

institutional investors. For example, officials at one firm told us that its 

data captured 80 to 90 percent of all institutional investors and covers 

trading for every stock listed on the major U.S. stock markets.18 An official 

of a major U.S. stock market told us that these firms are well regarded and 

that their information is particularly informative because these firms 

measure costs from the point the customer makes the decision to trade by 

using the price at which stocks are trading at that time, which is data that 

exchanges and markets generally do not have.

17ITG, another trade analytics firm, did not begin to measure institutional investors’ trading 

costs until January 2003, after the implementation of decimal pricing and 1-cent ticks.

18Specifically, Abel/Noser captures data from about 50 large investment management firms 

that in some years represent over 500 institutional investors and well over 1,000 unique 

portfolio managers. In addition, Abel/Noser claims its data represent nearly $3 trillion in 

principal traded each year. Elkins/McSherry captures trade data from about 1,400 

investment managers and 2,000 brokers worldwide, capturing about 20 percent of all dollars 

traded on NYSE and NASDAQ. The Plexus Group collects data from money managers 

representing as many as 100 institutional investors.
Page 16 GAO-05-535 Securities Markets

Although these firms use different methodologies, their data uniformly 

showed that costs had declined since decimal pricing was implemented. 

Our analysis of data from the Plexus Group showed that costs declined on 

both NYSE and NASDAQ in the 2 years after these markets converted to 

decimal pricing. Plexus Group analyzes various components of 

institutional investor trading costs, including the market impact of 

investors’ trading.19 Total trading costs declined by about 53 percent for 

NYSE stocks, falling from about 33 cents per share in early 2001 to about 

15.5 cents (fig. 2). For NASDAQ stocks, the decline was about 44 percent, 

from about 25.7 cents to about 14.4 cents. The decline in trading costs, 

shown in figure 2, began before both markets implemented decimal pricing, 

indicating that causes other than decimal pricing were also affecting 

institutional investors’ trading during this period. An official from a trade 

analytics firm told us that the spike in costs that preceded the 

decimalization of NASDAQ stocks correlated to the pricing bubble that 

technology sector stocks experienced in the late 1990s and early 2000s. An 

official from another trade analytics firm explained that trading costs 

increased during this time because when some stocks’ prices would begin 

to rise, other investors—called momentum investors—would also begin 

making purchases and drive prices for these stocks up even faster. As a 

result, other investors faced greater than usual market impact costs when 

also trading these stocks. In general, trading during periods when stock 

prices are either rapidly rising or falling can make trading very costly.

19To measure market impact costs, the Plexus Group compares a proprietary benchmark 

stock price to the average price an investor receives. The Plexus Group benchmark 

attempts to show the price at which the order for a particular stock should be executed. The 

firm calculates this expected price using trade data of its clients for the two quarters 

preceding the date of the trade under study and takes into account variables such as trade 

size, liquidity, and the direction of stock price movement.
Page 17 GAO-05-535 Securities Markets

Figure 2: Total Trading Costs from a Trade Analytics Firm for NYSE and NASDAQ Stocks, 1999–2003 (cents per share)

Note: Data are reported quarterly. After a phase-in period, all NYSE stocks were trading with decimal 

prices by January 29, 2001, and all NASDAQ stocks were converted by April 9, 2001.

10

20

30

40

50

60

70

80

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

Cents per share

1999 2000 2001 2002 2003

Year and quarter

Decimalized

Source: GAO analysis of Plexus Group data.

Nasdaq

NYSE
Page 18 GAO-05-535 Securities Markets

According to our analysis of the Plexus Group data, market impact and 

delays in submitting orders accounted for the majority of the decline in 

trading costs for NYSE stocks and NASDAQ stocks.20 Together, the 

reduction in these two cost components accounted for nearly 17 cents per 

share (or about 96 percent) out of a total decline of about 17.6 cents per 

share on NYSE. Delay costs declined about 11.2 cents per share in the 2 

years following the implementation of decimal pricing and 1-cent ticks on 

NYSE and market impact costs declining by about 5.8 cents (fig. 3). An SEC 

economist noted that declines in delay costs may reflect increased 

efficiency on the part of institutional investors in trading rather than 

changes in the markets themselves. 

20Delay costs are market impact costs that occur between the time institutional investors’ 

portfolio managers direct their traders to buy or sell stock and the moment these orders are 

released to brokers. The amount that the stock’s price changes during this period is the cost 

of delaying the order. An order may be delayed for a number of reasons—for instance, 

because it could affect prices in the market too much. See Plexus Group, The Official 

Icebergs of Transaction Costs, Commentary #54, January 1998.

Cents Per Share Versus Basis Points

Institutional investors’ trading costs are 

commonly measured in two units: cents per 

share and basis points. Cents per share is an 

absolute measure of cost based on executing 

a single share. Basis points—measured in 

hundredths of a percentage point—show the 

absolute costs relative to the stock’s average 

share price. Costs reported in terms of basis 

points can show changes resulting solely 

from changes in the level of stock prices.

In this section, we present our analysis of 

trade anlytics firms’ data on institutional 

investor trading costs in cents per share 

because the period surrounding the U.S. 

markets’ implementation of decimal pricing 

coincided with a large decline in the overall 

prices of stocks. Therefore, we chose to 

present data on trading costs in cents per 

share units as a way to better isolate 

decimalization’s impact. However, we also 

calculated these same costs in basis points 

and present this analysis in appendix III.

Source: GAO.
Page 19 GAO-05-535 Securities Markets

Figure 3: Trading Cost Components from a Trade Analytics Firm for NYSE and 

NASDAQ Stocks, 2001 and 2003 (cents per share)

Note: Data are from first quarter 2001 to second quarter 2003 for NYSE and second quarter 2001 to 

second quarter 2003 for NASDAQ.

0

5

10

15

20

25

30

35

0

5

10

15

20

25

30

35

Delay

Market impact

Commission

NYSE Nasdaq

2001

Year Year

2003 2001 2003

Cents per share Cents per share

Source: GAO analysis of Plexus Group data.

17.9

10.5

4.7

6.3

5.1

4.1

10.1

14.6

5.7

4.9

3.8

0.9
Page 20 GAO-05-535 Securities Markets

Figure 3 also shows that market impact and delay costs accounted for all 

declines to total NASDAQ trading costs. For example, market impact and 

delay costs declined about 14.1 cents per share between the second quarter 

of 2001 and the second quarter of 2003. However, at the same time that 

these cost components were improving, commission charges for NASDAQ 

stocks were rising. As shown in figure 3, commissions that market 

intermediaries charged for trading NASDAQ stocks increased about 2.8 

cents per share from second quarter of 2001 to second quarter of 2003. 

Industry representatives told us these increases were the result of the 

broker-dealers that made markets in NASDAQ stocks transitioning from 

trading as a principal, in which a portion of the trade’s final price included 

some compensation for the market maker, to trading as an agent for the 

customer and charging an explicit commission.21

Analysis of data from the other two trade analytics firms from whom we 

obtained data, Elkins/McSherry and Abel/Noser, also indicated that 

institutional investor trading costs declined following the decimalization of 

U.S. stock markets in 2001. Because these two firms’ methodologies do not 

include measures of delay, which the Plexus Group data shows can be 

significant, analysis of data from these two firms results in trading cost 

declines of a lower magnitude than those indicated by the Plexus Group

data analysis. Nevertheless, the data we analyzed from Elkins/McSherry 

showed total costs for NYSE stocks declined about 40 percent between the 

first quarter of 2001 and year-end 2004 from about 11.5 cents per share to 

about 6.9 cents per share. Analysis of Abel/Noser data indicated that total 

trading costs for NYSE stocks declined about 30 percent, from 6.9 cents per 

share to 4.8 cents per share between year-end 2000 and 2004 (fig. 4). 

21As principals, NASDAQ market makers had earned revenue from spreads by buying shares 

at the bid price from investors and selling those same shares to other investors at the higher 

ask price.
Page 21 GAO-05-535 Securities Markets

Figure 4: Total Trading Costs from Two Trade Analytics Firms for NYSE, 1998–2004 (cents per share)

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and the fourth quarter of 2004; 

Abel/Noser data are year-end totals for 1998–2004.

Our analysis of these firms’ data also indicated that total trading costs 

declined for NASDAQ stocks, which appeared to have declined even more 

significantly than they did for NYSE stocks. For example, our analysis of 

the Elkins/McSherry data showed that total trading costs for NASDAQ 

stocks dropped by nearly 50 percent, from about 14.6 cents per share to 

about 7.4 cents per share, between the second quarter of 2001 when that 

market decimalized and the end of 2004. Analysis of the Abel/Noser data 

indicated that total trading costs declined about 46 percent for NASDAQ 

stocks between the end of 2000 and 2004, falling from 8.7 cents per share to 

4.7 cents per share (fig. 5).

0

2

4

6

8

10

12

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

NYSE

decimalized

Cents per share

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Abel/Noser (averaged annual data)

Elkins/McSherry (quarterly data)
Page 22 GAO-05-535 Securities Markets

Figure 5: Total Trading Costs from Two Trade Analytics Firms for NASDAQ Stocks, 1998–2004 (cents per share)

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and the fourth quarter of 2004; 

Abel/Noser data are year-end totals for 1998–2004.

As our analysis of the Plexus Group data showed, the Elkins/McSherry and 

Abel/Noser data also indicated that reductions to market impact costs 

accounted for a vast proportion of overall reductions for NYSE stocks (fig. 

6).22 Analysis of the Elkins/McSherry data indicated that these costs 

declined by 3.7 cents per share, accounting for about 80 percent of the total 

fall in trading costs during this period. The 1.1 cent per share reduction in 

0

5

10

15

20

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

Nasdaq

decimalized

Cents per share

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Abel/Noser (averaged annual data)

Elkins/McSherry (quarterly data)

22These two firms analyze market impact costs by comparing their clients’ trades to the 

volume-weighted average price (VWAP) of the particular stocks traded. The VWAP 

represents the average price at which a particular stock traded on a specific trading day and 

is calculated by adding up the dollars traded for every transaction (price times shares 

traded) and then dividing by the total number of shares traded for the day. The closer an 

investor’s average price is to the VWAP, the lower the calculated market impact costs.
Page 23 GAO-05-535 Securities Markets

market impact costs identified in the Abel/Noser data represented over half 

of the total trading cost reductions of 2.1 cents per share for NYSE stocks.

Figure 6: Trading Cost Components from Two Trade Analytics Firms for NYSE 

Stocks, 2001 and 2004 (cents per share)

Note: Abel/Noser does not account for exchange fees as a component of trading cost. For 

Elkins/McSherry, we obtained first quarter 2001 data and fourth quarter 2004. For Abel/Noser, we 

obtained data from the end of 2000 and 2004.

Reductions to market impact costs explained the entire decline to total 

trading costs captured by the Elkins/McSherry and Abel/Noser data for 

NASDAQ stocks, and the total declines would have been even larger had 

commissions for these stocks not increased after 2001. Market impact 

costs declined about 10.6 cents per share (about 78 percent) according to 

our analysis of the Elkins/McSherry data, and 6.7 cents per share (about 87 

0

2

4

6

8

10

12

0

2

4

6

8

10

12

Elkins/McSherry Abel/Noser

Cents per share Cents per share

Exchange fee

Market impact

Commission

2001

Year Year

2004 2001 2004

0.3

5.9

5.4

2.1

4.7

2.1

1.0

4.1

5.0

0.1

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.
Page 24 GAO-05-535 Securities Markets

percent) according to our analysis of the Abel/Noser data (fig. 7). However, 

during this period, commissions charged on NASDAQ stock trades 

included in these firms’ data increased by more than 3 cents per share, 

representing a more than threefold increase in commissions as measured 

by Elkins/McSherry and a more than sixfold rise according to Abel/Noser.

Figure 7: Trading Cost Components from Two Trade Analytics Firms for NASDAQ 

Stocks, 2001 and 2004 (cents per share)

Note: Abel/Noser does not account for exchange fees as a component of trading cost. For 

Elkins/McSherry, we obtained first quarter 2001 data and fourth quarter 2004. For Abel/Noser, we 

obtained data from the end of 2000 and 2004.

0

3

6

9

12

15

0

3

6

9

12

15

Elkins/McSherry Abel/Noser

Cents per share Cents per share

Exchange fee

Market impact

Commission

2001

Year Year

2004 2001 2004

0.1

13.5

1.0

2.9

4.4

7.7

1.0

3.8

0.5

0.1

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.
Page 25 GAO-05-535 Securities Markets

Data from a fourth firm, ITG, which recently began measuring institutional 

trading costs, also indicates that such costs have declined. This firm began 

collecting data from its institutional clients in January 2003. Like the other 

trade analytics firms, its data is similarly broad based, representing about 

100 large institutional investors and about $2 trillion worth of U.S. stock 

trades. ITG’s measure of institutional investor trading cost is solely 

composed of market impact costs and does not include explicit costs, such 

as commissions and fees, in its calculations. Although changes in ITG’s 

client base for its trade cost analysis service prevented direct period to 

period comparisons, an ITG official told us that its institutional investor 

clients’ trading costs have been trending lower since 2003.23

Academic Studies Generally 

Showed Declining Costs for 

Institutional Investors

In attempting to identify all relevant research relating to the impact of 

decimal pricing on institutional investors, we found 15 academic studies 

that discussed the impact of decimalization but only 3 that specifically 

examined institutional investors’ trading costs. As of May 2005, none of 

these three studies had been published in an academic journal. Two of 

these studies used direct measures of trading costs, and the other used an 

indirect measure.24 Those that relied on more direct measures of these 

costs found that these costs had declined since the implementation of 

decimal pricing and 1-cent ticks. The first of these studies analyzed more 

than 80,000 orders in over 1,600 NYSE-listed stocks that were traded by 32 

institutional investors.25 To measure the change in trading costs after 

decimal pricing was implemented, this study used data from one of the 

leading trade analytics firms and computed trading costs over the period 

from November 28, 2000, to January 26, 2001 (before the change to decimal 

pricing), and the period from January 30 to March 31, 2001 (after decimal 

pricing). The study found that institutional trading costs appeared to have 

23We do not present the specific analysis of ITG’s data because the firm’s client base for its 

trade cost analysis grew significantly after it first began offering this service, including the 

addition of some larger clients with sophisticated trading operations that contributed to the 

overall decline measured by the firm.

24According to an academic recognized as an expert in financial markets’ use of information 

technology, studies based on direct measurements of institutional trading costs, such as 

data compiled by trade analytics firms and exchanges, lead to more reliable calculations of 

trading costs than do studies that rely on indirect determinants.

25Sugato Chakravarty, Venkatesh Panchapagesan, and Robert A. Wood. “Has Decimalization 

Hurt Institutional Investors? An Investigation into Trading Costs and Order Routing 

Practices of Buy-Side Institutions” (Unpublished study: May 28, 2003).
Page 26 GAO-05-535 Securities Markets

declined by about 5 cents per share (or about 11 percent), falling from 44 

cents per share to 39 cents per share after NYSE switched to 1-cent ticks. 

The other study that used direct measures of institutional trading costs 

examined the trading of over 1,400 NASDAQ stocks.26 The author of this 

study obtained data on over 120,000 orders for NASDAQ stocks submitted 

by institutional investors, which allowed her to calculate the costs of 

trading orders of more than 9,999 shares before and after NASDAQ’s 

adoption of 1-cent ticks. Given the potentially large volume of order data, 

the author studied three sample periods, each consisting of 5 trading days: 

February 1 through 8, 2001 (before decimalization), and June 18 through 22 

and November 26 through 30, 2001 (after decimalization). Trading costs in 

this study are measured as the difference between an order’s volumeweighted average execution price and a pre-execution benchmark price, 

the opening midquote (the midpoint between the quoted bid and ask 

prices). Using the opening midquote benchmark, the author found that 

average trading costs for orders of 10,000 shares and above fell about 19 

cents per share (or about 49 percent), from about 39 cents per share to 

about 20 cents per share during the 9 months or so after NASDAQ’s 

adoption of 1-cent ticks.

26Ingrid M. Werner, “Execution Quality for Institutional Orders Routed to NASDAQ Dealers 

Before and After Decimals, Study Prepared for the Fisher College of Business, The Ohio 

State University” (October 20, 2003).
Page 27 GAO-05-535 Securities Markets

Unlike the other two studies we identified, the third study reported that 

costs for institutional investors had increased. However, this study relied 

on an indirect measure of these costs for its analysis.27 To assess the change 

in trading costs, the authors of this study examined a sample of 265 mutual 

funds chosen from a database of mutual funds compiled by Morningstar, an 

independent investment research firm. These firms were selected using 

two criteria—investing predominantly in U.S. stocks and having at least 90 

percent of assets invested in stocks. However, the study did not obtain 

these mutual funds’ actual trading data but instead attempted to identify 

costs by comparing the funds’ daily returns (gain or loss from the prior 

day’s closing price) to the daily returns of a synthetic benchmark for the 

periods before and after decimalization, from April 17 through August 25, 

2000, and from April 16 through August 24, 2001.28 After finding that the 

returns of actively managed mutual funds were generally lower than the 

returns of the benchmark in the period after decimals were introduced, the 

authors attributed the lower returns to increases in the trading costs for 

these funds. 

Although this is a plausible explanation for these funds’ lower returns, 

some of the market participants that we spoke with indicated that other 

factors could also account for the results. For example, officials from a 

large mutual fund company that had reviewed the study told us that the 

lower returns may have resulted from the 3-year decline in stock prices in 

the market. As the value of their assets decline, funds can report higher 

expenses because their fixed operating costs correspondingly represent a 

larger portion of a mutual fund’s total costs, which would reduce reported 

returns. In addition, an academic regarded as an expert in applying 

technology to the financial markets noted that the lower returns could be 

the result of many of the funds in the study’s sample having similar holdings 

that all performed more poorly than those in the benchmark portfolio in the 

months following decimalization.

27Nicolas P.B. Bollen and Jeffrey A. Busse, “Tick Size, Trading Costs, and Mutual Fund 

Performance” (Unpublished study: 2004).

28The authors constructed a synthetic benchmark that mimics the stock holdings and 

expense ratios of the actual mutual funds they studied. Because the benchmark portfolio 

has zero trading costs by construction, the difference between the return on the benchmark 

and the actual funds was the authors’ measure of trading cost. 
Page 28 GAO-05-535 Securities Markets

Institutional Investors Reported 

Reduced or Level Trading Costs 

after Decimalization

In addition to analyzing data from trade analytics firms and academic 

studies, we interviewed 23 institutional investors that represented nearly 

one-third of assets managed by a ranking of the 300 largest money 

managers.29 Representatives for 20 of these firms said that their trading 

costs had fallen or stayed about the same since decimals were 

implemented (table 4).

Table 4: Institutional Investor Positions on Changes to Trading Costs After 

Decimalization

Source: GAO.

Note: Percentages rounded to the nearest full number.

As shown in table 4, fifteen of these firms said that their trading costs had 

declined since decimals were introduced. These firms included large 

mutual fund companies, pension fund administrators, a hedge fund, and 

smaller asset management firms, indicating that cost declines in our 

sample were not limited solely to just larger firms with greater trading 

resources. For example, a representative of a small money management 

firm not ranked as one of the 300 largest noted that trading costs had 

decreased since decimalization. In addition, the president of a hedge fund 

that was ranked in the lower half of the rankings told us that his firm’s 

trading costs had declined significantly since 2001. As shown in the table 

above, 5 of the 23 firms we interviewed said that their costs had remained 

about the same since decimal pricing was implemented. For example, 

representatives of one large mutual fund firm that measures its trading 

costs internally as well as through a trade analytics firm told us that their 

29This ranking was published in Institutional Investor, vol. 38, no. 7 (July 2004). The firms 

we interviewed represented a broad cross section of the institutional investor community, 

including representatives of the four largest money managers in the United States in 2003, 

four large public pension plan administrators, two large hedge funds, and other large, midsize, and small money managers with assets under management ranging from about $2 

billion to $500 billion.

Institutional investors

Trading cost change Number Percent

Declined 15 65%

Increased 3 13

Stayed about the same 5 22

Total 23 100%
Page 29 GAO-05-535 Securities Markets

firm’s transaction costs had not increased since decimal pricing was 

introduced, but had trended down to flat. Three institutional investors 

reported higher trading costs. One of these firms, a large mutual fund 

manager, attributed the increases to heightened levels of volatility 

following the reduction in tick size. For example, in his view, stock prices 

tended to trade in a wider daily range since decimals were implemented 

than they had before. The other two firms included a mutual fund firm and 

a mid-size asset management firm, with officials from the mutual fund 

noting that trading had become more involved and that completing trades 

of similarly sized orders takes longer since the conversion to decimal 

pricing.

In discussing institutional investors’ views on their trading costs since 

decimal pricing began, we found that the precision with which these firms 

measured their trading costs varied. Many firms told us that they used 

outside trade analytics firms, such as Abel/Noser, Elkins/McSherry, ITG, 

and Plexus Group, to measure their transaction costs. Representatives of 

some firms and a state pension plan administrator noted that their firms 

used trade cost analysis tools from more than one trade analytics firm. The 

head of trading for one firm said that his firm had been using a trade 

analytics firm to measure their trading costs for 10 years. Some firms said 

that they had developed in-house capabilities to measure their own 

transaction costs. These systems appeared to vary in their levels of 

sophistication. For example, representatives of a large money management 

firm told us that they had developed a sophisticated cost measurement 

system that shows them what a trade should cost before it is executed. The 

system takes into account factors such as the executing broker and the 

market venue where the trade executes. A managing partner of another 

firm noted that it measures costs of completed trades in-house, including 

the bid-ask spreads and the execution prices, and compares them to the 

volume-weighted average price for trades it executes. Some money 

managers told us that their firms did not measure their costs for trading. 

For example, officials from one firm said that while not formally measuring 

costs on their own, they sometimes were provided with data on the costs of 

their trades from their own clients who use trade analytics firms to 

evaluate the costs of using various money managers. Also, another state 

pension plan administrator told us that while his organization does not 

currently measure its trading costs, it plans to do so within the next 2 years.
Page 30 GAO-05-535 Securities Markets

Volatility Has Also Improved 

since Decimal Pricing Began 

In addition to lower spreads and reduced market impact costs, some 

market participants noted that another measure of market quality—price 

volatility—had also improved since decimal pricing was implemented. 

According to some market participants, the smaller 1-cent ticks generally 

slowed price movement in the markets and narrowed the range of prices at 

which stocks trade over the course of time, such as a day. For example, a 

noted expert on market microstructure told us that price volatility has 

declined since the reduction in tick size because price changes occur in 

smaller increments.30 Our own study of NYSE and NASDAQ stocks using 

TAQ data showed that price volatility has declined since decimal pricing 

was implemented. To assess the change in volatility for the stocks in our 

sample, we calculated the percentage change in price for each one hour 

increment (between 10 a.m. and 4 p.m.) each trading day. We also 

calculated the percentage change in price for each stock that occurred 

between 10 a.m. and 4 p.m. For each stock, we also calculated the standard 

deviation of these percentage changes, which measures how widely the 

individual price changes are dispersed around the average change, and 

reported the median (that is the middle) standard deviation. As shown in 

table 5, the volatility of the price changes in the stocks in our sample 

decreased for both the hourly percentage change between 10 a.m. and 4 

p.m. each trading day and the percentage change from 10 a.m. to 4 p.m. 

each trading day after decimal prices were implemented. These findings 

were in agreement with a recently published academic study.31 

30Market microstructure is the study of the process of how the trading of securities affects 

prices, volumes and trader behavior.

31See Hendrik Bessembinder, “Trade Execution Costs.”
Page 31 GAO-05-535 Securities Markets

Table 5: Price Change Volatility for NYSE and NASDAQ Stocks Before and After 

Decimalization

Source: GAO analysis of TAQ data.

Note: The median standard deviation in this table is based on the continuously compounded 

percentage change in the quote midpoint for each stock.

However, not all participants attributed the reduced price volatility to 

decimal pricing. For example, a representative of a trade analytics firm 

noted that with the Internet boom, investors increased their positions in 

technology-sector stocks in a hurry and when the prices of these stocks 

fell—which was coincident with the change to decimal pricing—investors 

quickly reversed their positions. By selling quickly, these investors incurred 

greater market impact costs. With the subsiding of this type of trading 

activity in ensuing years, markets have become calmer, which has made 

trading less costly.

Despite Reduced Market 

Transparency for Large 

Orders, Institutional 

Investors Have Been Able to 

Complete Trades

Although some major elements of market quality—trading costs and 

volatility—have improved since decimal pricing began, another market 

quality element—transparency—appears to have been negatively affected. 

The transparency of a market can depend on whether large numbers of 

shares are publicly quoted as available to buy or sell. The various sources 

of data we collected and analyzed indicated that after decimal pricing and 

the 1-cent tick were implemented in 2001, the volume of shares shown as 

available for sale—or displayed depth—on U.S. stock markets declined 

significantly. For example, studies required by SEC on the impact of 

decimal pricing on trading, among other things, on U.S. markets showed 

that the average number of shares displayed for trading on NYSE and 

NASDAQ at the best quoted prices declined by about two-thirds between a

Median standard deviation of price changes

NYSE stocks NASDAQ stocks 

Price change 

period

Before

decimals

After

decimals

Before

decimals

After

decimals

Hourly 1.00% 0.79% 0.97% 0.75%

From 10 a.m. to

4 p.m. 2.48 1.99 2.37 1.87
Page 32 GAO-05-535 Securities Markets

sample period before the markets converted to decimal pricing and a 

period soon after the conversion took place (table 6).32

Table 6: Average Number of Shares Displayed at the Best Quoted Prices Reported 

by NYSE and NASDAQ in Studies of Their Markets Before and After Decimalization

Source: GAO analysis of NYSE and NASDAQ data.

a

Averages on NYSE are trade weighted. Averages are for all 2,466 NYSE-listed securities trading in 

both sample periods. NYSE’s presample period is August 1-25, 2000; its postsample period is June 

2001.

b

Averages are for 4,766 NASDAQ-listed securities that converted to decimal pricing on April 9, 2001. 

Another 211 securities converted to decimal pricing earlier. NASDAQ’s pre sample period is the 2 

weeks prior and 2 weeks after the conversion date.

In addition, our own study of 300 matched pairs of NYSE and NASDAQ 

stocks found that the liquidity at the best quoted prices declined 

significantly. According to our analysis, the average number of shares 

displayed at the best quoted prices fell by 60 percent on NYSE and 34 

percent on NASDAQ over the nearly 5-year period between February 2000 

and November 2004 (fig. 8). The greatest declines occurred around the time 

that the markets converted to decimal pricing and 1-cent ticks. In its impact 

study, NASDAQ attributed declines in the volume of shares displayed at the 

best prices to the conversion to decimal pricing.

32New York Stock Exchange, Inc., Decimalization of Trading on the New York Stock 

Exchange, A Report to the Securities and Exchange Commission, 9. Also see NASDAQ 

Stock Market, Inc., The Impact of Decimalization on The NASDAQ Stock Market: Final 

Report to the SEC, 33. 

Market

Average shares

displayed before

Average shares

displayed after Percent change

NYSEa 7,930 2,657 -67%

NASDAQb 13,974 4,539 -68
Page 33 GAO-05-535 Securities Markets

Figure 8: Volume-weighted Average Number of Shares Displayed at the Best Quoted Prices on the NYSE and NASDAQ Before 

and After Decimalization, Sample Weeks from February 2000–November 2004

0

1

2

3

4

5

6

7

Nov. (1-5)

 July (19-23)

 Mar. (8-12)

 Oct. (20-24)

 June (2-6)

 Feb. (24-28)

 Sept. (23-27)

 May (6-10)

 Jan. (7-11)

 Dec. (10-14)

 Aug. (20-24)

 Apr. (23-27)

 Jan. (22-26)

 Dec. (18-22)

 Nov. (6-10)

 Oct. (23-27)

 Sept. (18-22)

 Aug. (21-25)

 July (10-14)

 June (19-23)

 May (8-12)

 Apr. (10-14)

 Mar. (20-24)

 Feb. (7-11)

Number of shares (in thousands)

2000 2001 2002 2003 2004

Sample week (year, month, and days)

Markets

decimalized

Source: GAO.

Nasdaq

NYSE
Page 34 GAO-05-535 Securities Markets

The amount of shares displayed as available for trading also declined at 

prices away from the best quoted prices. For example, the SEC-mandated 

NYSE impact study shows that the amount of shares displayed for trading 

within about a dollar of the midpoint between the best quoted prices 

generally declined to well under half of what it was when the tick size was 

1/16 of a dollar. NASDAQ’s own impact study reported that the cumulative 

amount of shares displayed for trading declined by about 37 percent within 

a fixed distance equal to twice the size of the average quoted spread from 

the midpoint between the best quoted prices.33 This decline in the volume 

of shares displayed across all prices—called market depth—is particularly 

significant for institutional investors because they are often executing large 

orders over multiple price points that are sometimes inferior to the best 

quoted prices. 

Various reasons can explain the reduced number of shares displayed at the 

best prices. First, the amount of shares displayed for trading at the best 

price likely declined because the decrease in the minimum tick size created 

more prices at which orders could be displayed. The reduction in tick size 

increased the number of price points per dollar at which shares could be 

quoted from 16, under the previous minimum tick size of 1/16 of a dollar, to 

100. With more price points available to enter orders, some traders that 

may have previously priced their orders in multiples of 1/16 to match the 

best quoted price may now instead be sending orders priced 1, 2, or 3 cents 

away from the best price, depending on their own trading strategy. As a 

result, the volume of shares displayed as available at the best price is lower 

as more shares are now distributed over nearby prices.

In addition to fewer shares displayed at the best price, displayed market 

depth may also have declined because the reduction in tick size reduced 

incentives to large-order investors to display their trading interest. Since 

the implementation of penny ticks, market participants said that displaying 

large orders is less advantageous than before because other traders could 

now submit orders priced one penny better and execute these orders ahead 

of the larger orders. This trading strategy, called “penny jumping” or 

“stepping ahead,” harms institutional investors that display large orders

33NASDAQ used the average quoted spread from January 2001, before the market converted 

to decimal pricing, to study the cumulative number of shares that were displayed before and 

after decimalization.
Page 35 GAO-05-535 Securities Markets

and can increase their trading costs.34 For example, an investor wants to 

purchase a large quantity of shares of a stock (e.g., 15,000 shares) and 

submits an order to buy at a price of $10.00 (a limit order).35 Another trader, 

seeing this large trading interest, submits a smaller limit order (e.g., 100 

shares) to buy the same stock at $10.01. This smaller order will be executed 

against the first market order (which are orders executed at the best price 

currently prevailing at the time they are presented for execution) that 

arrives. As a result, the investor’s larger order will go unexecuted until that 

investor cancels its existing order at $10.00 and resubmits it at a higher 

price. In this case, the investor’s trading costs increase due to price 

movements that occur in the process of completing a large order (i.e., 

market impact). 

The potential for stepping ahead has increased because in a 1-cent tick 

environment the financial risk to traders stepping ahead of larger displayed 

orders has been greatly reduced. For example, assume a trader who steps 

ahead of a larger order offering to buy shares at $10.00 by entering a limit 

order to buy 100 shares at a price of $10.01 is executed against an incoming 

market order. However, if the price of the stock appears to be ready to 

decline, such as when additional orders to sell are entered with prices 

lower than $10.00, the trader who previously stepped ahead can quickly 

enter an order to sell the 100 shares back to the large investor whose order 

is displayed at $10.00. In such situations, the trader’s loss is only one penny 

per share, whereas in the past, traders stepping ahead would have risked at 

least 1/16 of a dollar per share. Many market participants we spoke to 

acknowledged that institutional investors are reluctant to display large 

orders in the markets following the switch to 1-cent ticks for fear that 

competing traders would improve the best quoted prices by one penny and 

drive up prices to execute large orders.

34In a related matter, on April 6, 2005, the SEC Commission adopted Regulation NMS 

(National Market System), which included a ban on quotations in increments of less than 

one penny (known as subpenny pricing) for stocks priced $1 and above. In prior GAO work, 

we found that quoting in subpenny increments resulted in more instances of traders 

“stepping ahead” of large limit orders. For additional information on subpenny pricing, see 

GAO’s testimony Securities Markets: Preliminary Observations on the Use of Subpenny 

Pricing, GAO-04-968T (Washington, D.C.: July 22, 2004).

35An order that specifies a particular price at which it can be executed is called a limit order. 

Limit orders are required to be executed at the specified price or better. Limit orders 

provide liquidity to markets.
Page 36 GAO-05-535 Securities Markets

The potential that the reduced tick size would increase the prevalence of 

stepping ahead was acknowledged prior to decimal pricing’s 

implementation. For example, in 1997 a prominent academic researcher 

predicted that problems with stepping ahead would increase following 

decimalization because smaller price increments would make it easier (i.e., 

cheaper) for professional traders to step in front of displayed orders and 

that this would result in fewer shares being quoted and less transparency in 

the markets.36 However, some market participants we interviewed 

acknowledged that stepping ahead had been a problem before decimal 

pricing was implemented. For example, representatives of a hedge fund 

told us they were worried about getting stepped ahead of if they revealed 

their interest to trade large amounts of a stock by entering limit orders with 

large numbers of shares even when ticks were 1/8 and 1/16. An SEC staff 

person told us that instances of orders being stepped ahead of has 

increased since the penny tick was implemented, but he did not think that 

it negated the benefits of decimal pricing overall. 

Institutional Investors Have 

Adjusted Their Trading Methods 

to Continue Executing Large 

Orders 

Although markets became less transparent following decimalization, 

institutional investors and traders appear to be able to execute large orders 

at a lower cost by adapting their trading strategies and technologies. For 

example, the academic study that studied around 120,000 large orders 

submitted for NASDAQ stocks found that the average proportion of total 

order size that was executed (filled) increased slightly from 78 percent 

before the change to decimal pricing to about 81 percent about 6 months 

following the change. Similarly, the study found the length of time required 

to fill orders—measured from the time the order arrived at a NASDAQ 

dealer to the time of the last completed trade—decreased from about 81 

minutes before decimal pricing to about 78 minutes 6 months after.37 Eight 

of the institutional investment firms we contacted for this report also 

provided information about their experiences in completing trades. Of 

these, officials from seven of the eight told us that their fill rates had either 

stayed about the same or had increased. An official at one firm noted that 

the proportion of orders that were completely executed had risen by as 

much as 10 percent in the period following decimal pricing’s introduction. 

36Lawrence Harris, Decimalization: A Review of the Arguments and Evidence, USC 

Working Paper (Los Angeles, Calif.: Apr. 3, 1997), i.

37Ingrid M. Werner, 17 and 26.
Page 37 GAO-05-535 Securities Markets

One of the ways that institutional investors have adapted their trading 

strategies to continue trading large orders is to break up these orders into a 

number of smaller lots. These smaller orders can more easily be executed 

against the smaller number of shares displayed at the best prices. In 

addition, not displaying their larger orders all at once prevents other 

traders from stepping ahead. Evidence of this change in investors’ trading 

strategy is illustrated by the decline in the average executed trade size on 

NYSE and NASDAQ. As table 7 shows, the average size of trades executed 

on these markets has declined about 67 percent since 1999 on NYSE and by 

about 41 percent on NASDAQ.

Table 7: Average Trade Size for NYSE and NASDAQ, 1999–2004 (in shares)

Source: GAO analysis of NYSE and NASDAQ data.

Market 1999 2000 2001 2002 2003 2004

Percent

change

1999–

2004

NYSE 1,205 1,187 907 666 488 393 -67%

NASDAQ 808 693 782 735 580 477 -41
Page 38 GAO-05-535 Securities Markets

With average trade size down, some market participants noted that at least 

4 to 5 times as many trades are required to fill some large orders since 

decimalization. For example, a representative of a large mutual fund 

company said that his traders have always broken their funds’ large orders 

up into smaller lots so that they could trade without revealing their activity 

to others in the marketplace. Before decimalization, completing an order 

may have required 10 trades, but following the change to decimal pricing a 

similar order might require as many as 200 smaller trades. Referring to the 

increased difficulty of locating large blocks of shares available for trading, 

one representative of a money management firm stated that 

“decimalization changed the trading game from hunting elephants to 

catching mice.” In fact, the number of trades that NYSE reported being 

executed on its market increased more than fourfold between 1999 and 

2004, rising from about 169 million trades to about 933 million trades.38

Institutional Investors 

Increasingly Use Electronic 

Trading and Alternative Trading 

Venues 

To facilitate the trading of large orders while minimizing market impact 

costs, many market participants said that they had increased their use of 

electronic trading techniques. Many of these techniques involve 

algorithmic execution strategies, which are computer-driven models that 

segment larger orders into smaller ones and transmit these over specified 

periods of time and trading venues. The simplest algorithms may just break 

a large order into smaller pieces and route these to whichever exchange or 

alternative trading system offers the best price. Institutional investors often 

obtain these algorithms as part of systems offered by broker-dealers and 

third-party vendors. They may also develop them using their own staff and 

integrate them into the desktop order management systems they use to 

help conduct their trading. 

One of the primary purposes of using these algorithmic trading systems is 

to conduct trading in a way that prevents other traders from learning that a 

large buyer or seller is active in the market. Institutional investors want 

tools that allow them to trade more anonymously to reduce the extent to 

38Data on the volume of trades executed on NASDAQ for this period was not comparable to 

that from NYSE because trades in NASDAQ stocks were increasingly being executed 

outside this market. The declining trading volumes being reported by NASDAQ were the 

result of alternative trading venues, such as ECNs, executing increasing portions of volume 

in NASDAQ shares but reporting these trades outside the NASDAQ trade reporting system. 

For example, trades executed by the Island ECN were previously reported to NASDAQ and 

were included in NASDAQ’s total trading volume statistics. However, in 2002 Island began 

reporting its trades instead through the Cincinnati Stock Exchange (now called the National 

Stock Exchange), which caused a reduction of over 20 percent in trades that NASDAQ 

reported as being executed within its market.
Page 39 GAO-05-535 Securities Markets

which others can profit at their expense, such as when other traders, 

realizing that a large buyer is active, also buy shares, which quickly causes 

prices to rise, in hopes of selling these now more expensive shares to this 

large buyer. Several market participants told us that the anonymity that 

algorithms provide reduces the potential for other traders to learn that a 

large buyer or seller is active in the market (known as information 

leakage), thus reducing the likely market impact of executing the entire 

order. 

The use of these tools is growing. A 2004 survey conducted by The Tabb 

Group, a financial markets’ consulting firm, of more than 50 head and 

senior traders at institutional investor firms reported that over 60 percent 

of these firms were using algorithmic trading vehicles.39 The report noted 

that this widespread adoption rate was higher than anticipated. Many of the 

market participants we contacted also told us they were actively using 

algorithms in their trading activities and those that were not currently using 

algorithms generally indicated that they planned to begin using them in 

their trading strategies in the near future. In its report, The Tabb Group 

predicted that algorithmic trading will grow by almost 150 percent over the 

next 2 years.

To locate the additional shares available for trading that are otherwise not 

displayed, institutional investors are also increasingly using alternative 

trading venues outside the primary markets, such as NYSE and NASDAQ, 

to execute their large orders at lower cost. For example, institutional 

investors are conducting increasing portions of their trading on ECNs. 

Originally, ECNs were broker-dealers that operated as real-time electronic 

trading markets by allowing their customers to enter orders for stocks and 

obtain executions automatically when the prices of the orders entered 

matched those of orders entered by other customers. Recently, ECNs have 

entered into formal associations with existing stock exchanges.40 

39See The Tabb Group, Institutional Equity Trading in America: A Buy-Side Perspective. 

(Westborough, Mass.: April 2004), 32.

40For example, in 2001 SEC approved the establishment of the Archipelago Exchange as the 

stock trading facility of the Pacific Exchange. See SEC, PCX Rulemaking: Order Approving 

Proposed Rule Change by the Pacific Exchange, Inc., as Amended, and Notice of Filing and 

Order Granting Accelerated Approval to Amendment Nos. 4 and 5 Concerning the 

Establishment of the Archipelago Exchange as the Equities Trading Facility of PCX Equities, 

Inc., Exchange Act Release No. 44983 (October 25, 2001), 66 Fed. Reg. 55225 (2001).
Page 40 GAO-05-535 Securities Markets

Use of ECNS has been a growing trend. According to The Tabb Group, 88 

percent of the institutional investor firms it surveyed responded that they 

traded using ECNs. Furthermore, a 2004 survey by Institutional Investor

magazine asked the trading staff of institutional investor firms to identify 

their preferred venues for executing stock trades. The survey reported that 

three of the top five trading venues for institutional stock trade execution 

were ECNs.41 According to data we obtained from a financial markets 

consulting firm, the share of ECN trading in NASDAQ and NYSE stocks has 

increased between 1996 and 2003. For example, ECN trading volume 

increased from about 9 percent of all NASDAQ trading in 1996 to about 40 

percent of total NASDAQ trading volume in 2003 (fig. 9).

Figure 9: Proportion of Total Share Trading Volume NASDAQ and NYSE Stocks by 

ECNs, 1996–2003

41Justin Shack, “The Orders of Battle,” Institutional Investor, vol. 38, no. 11, November 

2004, 82.

0

5

10

15

20

25

30

35

40

1996 1997 1998 1999 2000 2001 2002 2003

Percentage

Source: GAO analysis of Celent Communications data.

Nasdaq

NYSE

Year
Page 41 GAO-05-535 Securities Markets

The percent of trading volume for NYSE stocks conducted through ECNs 

has also increased, though to a much lesser degree than has these 

organizations’ trading in NASDAQ stocks. According to some market 

participants, ECNs have been less successful in gaining greater market 

share in NYSE stocks because of rules that result in most orders being sent 

to that exchange. For example, one regulation—the trade through rule—

requires that broker-dealers send orders to the venue offering the best 

price, and in most cases NYSE has the best quoted price for its listed 

stocks. However, in a report issued by a financial market consulting firm, 

ECN officials called the trade through rule anticompetitive because the rule 

fails to acknowledge that some investors value the certainty and speed of 

execution more than they do price. They noted that under current rules, the 

NYSE specialists have as long as 30 seconds to decide whether to execute 

an order sent to them or take other actions. During this time, market 

participants told us that the price of the stock can change and their order 

may not be executed or will be executed at an undesirable price. On April 

6, 2005, SEC approved Regulation NMS (National Market System) which, 

among other things, limits the applicability of trade through requirements 

to quotes that are immediately accessible.42 

42Regulation NMS was originally proposed for public comment in February 2004. Exchange 

Act Release No. 49325 (Feb. 26, 2004), 69 Fed. Reg. 11126 (2004). The SEC extended the 

period for comment and issued a supplemental release regarding Regulation NMS in May 

2004. Exchange Act Release No. 49749 (May 20, 2004), 69 Fed. Reg. 30142 (2004). SEC 

reproposed a revised Regulation NMS in December 2004. Exchange Act Release No. 50870 

(Dec. 16, 2004), 69 Fed. Reg. 77424 (2004). Changes to the trade through rule (now known as 

the Order Protection Rule) are to be implemented for a limited number of stocks beginning 

April 10, 2006, and for all National Market System stocks by June 12, 2006. 
Page 42 GAO-05-535 Securities Markets

Institutional investors we spoke with highlighted anonymity, speed, and the 

quality of the prices they receive as reasons for their increased use of 

ECNs. The respondents to The Tabb Group survey indicated that their 

firms used ECNs to reduce market impact costs and to take advantage of 

lower fee structures. Many market participants we interviewed and studies 

we reviewed also indicated that trading using ECNs lowered institutional 

trading costs. According to market participants we interviewed, 

decimalization accelerated technology innovation, which they believe has 

been significant in reducing trading costs primarily by providing a means 

for investors to directly access the markets and reducing the need for 

intermediation. However, many acknowledged that increasing use of ECNs 

has been a growing trend since 1997, when SEC implemented rule changes 

that allowed ECNs to better compete against NASDAQ market makers.43 

Other alternative trading venues that institutional investors are 

increasingly using to execute their large orders are block trading platforms 

operated by broker-dealers called crossing networks. These networks are 

operated by brokers such as ITG, Liquidnet, and Pipeline Trading Systems. 

Crossing networks generally provide an anonymous venue for institutional 

investors to trade large blocks of stock (including orders involving tens or 

hundreds of thousands of shares) directly with other institutional 

investors. For example, one crossing network integrates its software with 

the investor’s desktop order management system so that all of the 

investor’s orders are automatically submitted to this crossing network in an 

effort to identify a match with another institutional investor. Once a match 

is identified, the potential buyer and seller are notified, at which time they 

negotiate the number of shares and price at which a trade would occur. The 

heads of stock trading for two large money management firms told us an 

advantage of using crossing networks is that they minimize market impact 

costs by allowing investors to trade in large blocks without disclosing their 

trading interests to others in the markets. Also, the chief executive officer 

of a crossing network noted that the absence of market intermediaries in 

the negotiation of trades on crossing networks provides the customers’ 

traders with the ability to control the price and quantity of their executions. 

However, we were told that crossing networks may not be the preferred 

43These rules include the Limit Order Display Rule (SEC Rule 11Ac1-4) and the Quote Rule 

(SEC Rule 11Ac1-1). Rule 11Ac1-4 mandated that public limit orders for all NASDAQ 

securities should be reflected in the best bid and offer disseminated by that market. Rule 

11Ac1-1, states that market makers may not post one quote on NASDAQ and a different 

quote on an alternative quote dissemination system (i.e., ECN). These rules are known as 

the Order Handling Rules.
Page 43 GAO-05-535 Securities Markets

strategy for all kinds of institutional orders because orders remain 

unexecuted if a natural match cannot be found. 

Crossing networks are gaining in prominence among institutional investors 

as a destination of choice for trading large quantities of stock. According to 

The Tabb Group’s survey of head and senior traders, 70 percent of all firms 

reported using crossing networks.44 In Institutional Investor’s 2004 survey, 

Liquidnet, a crossing network established in 2002, ranked second on the list 

of institutional investors’ favorite venues for trade executions.45 

Despite advances in electronic trading technologies that give institutional 

investors increased access to markets, some institutional investors 

continue to use full-service brokers to locate natural sources of liquidity as 

they did before decimal pricing began. According to institutional investor 

officials we interviewed, with fewer shares displayed as available for 

trading and reductions in average trade size, they are more patient about 

the time required to completely execute (fill) large orders using brokers in 

this way. In addition, some noted they increasingly use NYSE floor brokers 

to facilitate the trading of large orders in less-liquid stocks, explaining that 

floor brokers have information advantages in the current market structure 

that help to minimize adverse price changes.

Market Conditions May Also 

Have Helped Lower Institutional 

Investors’ Trading Costs

In addition to increased use of electronic trading, overall market conditions 

also likely helped lower trading costs for institutional investors. For 

example, prices on U.S. stock markets began a multiyear downturn around 

2000. As stock prices declined, asset managers faced increased pressure to 

manage costs and boost investment returns. Representatives of all four 

leading firms we interviewed that analyze institutional investors’ trading 

activity noted that the declining market that persisted after the 

implementation of decimal pricing also had led to reduced costs. 

Representatives of two of these trade analytics firms noted specifically that 

institutional buyers and sellers appeared more cost sensitive as a result of 

the 3-year declining stock market, which caused investment returns to 

decline substantially. This increased the incentive for institutional investors 

to take actions to lower their trading costs as a way to offset some of the 

reduced market returns.

44The Tabb Group, Institutional Equity Trading, 29.

45Shack, “Orders of Battle,” 82.
Page 44 GAO-05-535 Securities Markets

Some Stock 

Intermediaries Have 

Experienced Lower 

Profits since 

Decimalization, but 

Other Factors Have 

Contributed to the 

Declines

Although overall securities industry profits have returned to levels similar 

to those in the past, some market intermediaries, particularly those brokerdealers acting as exchange specialists and NASDAQ market makers, have 

been significantly affected by the implementation of decimal pricing. 

Between 2000 and 2004, exchange specialists and NASDAQ market makers 

generally saw their revenues and profits from stock trading fall, forcing 

some smaller market intermediaries out of the market. Decimal pricing was 

not the only force behind these declines, however. Sharp declines in the 

overall level of prices in the stock market, the growing use of trading 

strategies that bypass active intermediary involvement, and heightened 

competition from ECNs and other electronic trading venues have affected 

revenues and profits. We found that intermediaries were adapting to the 

new conditions by changing their business practices—for example, by 

investing in electronic trading devices and data management systems, 

reducing the size of their trading staffs, or changing how they priced their 

services. In response to the negative conditions that some believe exist in 

U.S. stock markets, a proposal has been made to conduct a pilot test of the 

use of a higher minimum tick for trading. Many of the market 

intermediaries but fewer than half of the institutional investors we 

contacted favored this move.
Page 45 GAO-05-535 Securities Markets

Conditions in the Overall 

Securities Industry Appear 

to Be Improving

The business environment for the securities industry as a whole, which saw 

reduced revenues after 2000, appears to be improving. The Securities 

Industry Association (SIA), which represents the broker-dealers holding 

the majority of assets in the securities industry, has compiled data on all of 

its member broker-dealers that have conducted business with public 

customers in the United States over the last 25 years.46 As shown in figure 

10, the data SIA compiles are derived from filings broker-dealers are 

required to make with the SEC and detail, among other things, revenues 

and expenses for market activities such as trading in stocks, debt 

securities, and options and managing assets.47 SIA’s 2004 data show that 

industry revenues of $237 billion, while down from the height of the bull 

market in 2000, are now similar to revenues earned before the 

unprecedented gains of 2000.48 In addition, the industry’s total pretax net 

income of $24.0 billion in 2003 and $20.7 billion in 2004 represent some of 

the highest levels of pretax industry profits of the past 25 years. 

46SIA has approximately 600 members. SIA members include most of the largest U.S. brokerdealers.

47These filings are the Financial and Operational Combined Uniform Single (FOCUS) 

reports.

48A bull market is a market in which stock prices rise over a sustained period of time.
Page 46 GAO-05-535 Securities Markets

Figure 10: Securities Industry Total Revenues and Net Income, 1994–2004

Further, our review indicated these improved industry conditions are not 

only the result of improved performance among the largest firms. By 

examining the trend in this data after excluding the results for the 25 

largest broker-dealers, the revenue and net-income trend for the remaining 

firms revealed the same pattern of improvement.

0

50

100

150

200

250

300

350

'80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04

0

5

10

15

20

25

30

35

'80 '82 '84 '86 '88 '90 '92 '94 '96 '98 '00 '02 '04

Dollars in billions

Dollars in billions

Source: Securities Industry Association.

Year

Net income

Total revenue
Page 47 GAO-05-535 Securities Markets

Decimalization Has 

Negatively Affected 

Exchange Specialists

Despite these improvements, some market intermediaries, such as stock 

exchange specialists, have been negatively affected by the shift to decimal 

pricing. Stock exchange specialists buy or sell shares from their own 

accounts when insufficient demand exists to match orders from public 

customers directly. The lower spreads that have prevailed since decimal 

pricing have reduced the income that exchange specialists can earn from 

this activity. In addition, the number of shares displayed as being available 

for purchase or sale has declined, leaving specialist firms with less 

information about market trends and thus less ability to trade profitably.49 

According to NYSE data, between 2000 and 2004 aggregate NYSE specialist 

revenues declined by more than 50 percent, falling from $2.1 billion to $902 

million (table 8). 

Table 8: NYSE Specialist Firm Revenues and Profits, 1999–2004 (in millions of 

dollars)

Source: GAO analysis of NYSE data.

a

Result reflects the booking of approximately $147 million in fines that NYSE specialist firms paid to 

settle charges with SEC and NYSE for trading violations.

b

Result reflects the booking of approximately $109 million in fines that NYSE specialist firms paid to 

settle charges with SEC and NYSE for trading violations.

Further, since decimal pricing began, the extent to which specialist firms 

participate in trades on their own exchanges has been low, falling below 

predecimalization levels. The participation rate shows the percentage of 

the total shares traded represented by trades conducted by specialists as 

part of their obligation to purchase shares when insufficient demand exists 

or sell shares when insufficient numbers of shares are being offered. After 

climbing during the first year decimal pricing was implemented, the 

percentage of trades on NYSE in which NYSE specialists participated 

declined from 15.1 percent in 2001 to 10.2 percent in 2004 (fig. 11). 

49Lawrence Harris and Venkatesh Panchapagesan, “The Information Content of the Limit 

Order Book: Evidence from NYSE Specialist Trading Decisions,” Journal of Financial 

Markets, Vol. 8 (2005).

Category 1999 2000 2001 2002 2003 2004

Revenues $1,566 $2,136 $1,776 $1,645 $987 $902

After-tax profits $476 $708 $414 $397 $3a $(38)b
Page 48 GAO-05-535 Securities Markets

Figure 11: NYSE Specialist Participation Rates, 1999-2004, in Percent of Trades

Note: These percentages are calculated by dividing the total number of shares that specialists trade by 

twice the total volume of the trading on the exchange to reflect that specialists are usually only either 

buying shares or selling shares as part of a trade with other customers. 

The trend toward smaller order sizes and more trade executions that have 

accelerated since the introduction of decimal pricing (as discussed earlier 

in this report) has also impacted the operating expenses of exchange 

specialists. The average trade execution size on the NYSE dropped from 

1,205 shares per execution in 1999 to 393 shares per execution in 2004, so 

that specialists now generally process more trades to execute orders than 

they did before decimal pricing began. This trend toward greater numbers 

of executions, which many market participants indicated was exacerbated 

by decimal pricing, has required exchange specialists to absorb additional 

processing costs and make related investments in more robust data 

management and financial reporting tools. For example, each trade that is 

submitted for clearance and settlement carries a fee, paid to the National

0

10

20

30

40

50

60

70

80

90

100

1999 2000 2001 2002 2003 2004

Participation rate (percentage)

Year

12.5 15.1 14.9 13.1 13.8

10.2

Source: NYSE Fact Book.

NYSE's specialist

participation rate 

Sum of shares

purchased and sold

2 x total volume =
Page 49 GAO-05-535 Securities Markets

Securities Clearing Corporation, of between $0.0075 to $0.15 per trade.50 

Several smaller regional exchange specialist firms we spoke with 

highlighted these kinds of increased operating costs as significant to their 

ability to continue profitable operations. Additionally, a floor brokerage 

firm we spoke with said that other charges had contributed to its declining 

operating performance. These charges included those from clearing firms, 

which typically charge in the range of $0.20 cents per 100 shares to process 

trades, and execution fees from exchange specialists related to the 

processing of more trades and typically paid by floor brokers. 

As shown in table 9 below, average trade size has declined over the past 6 

years as the number of executions on NYSE has risen. As the table shows, 

volumes have remained relatively consistent since 2002, even though 

exchange specialists and floor brokers have seen their revenue and profits 

decline during this period. 

Table 9: NYSE Reported Trades, Average Daily Volume, and Average Trade Size, 

1999–2004 

Source: NYSE Fact Book.

50This fee is one charged for trade recording and is assessed at $0.0025 per share in the trade 

with a minimum charge of $0.0075 and the maximum of $0.15. For example, a trade 

executed for 10,000 shares would be charged the maximum of $0.15. However, if this trade is 

broken into two executions of 5,000 shares each, each trade would be charged $0.125, or a 

total of $0.25, illustrating how more trades could lead to higher clearing costs.

Category 1999 2000 2001 2002 2003 2004

Total reported 

trades (in millions 

of shares) 169 221 339 546 723 933

Average daily 

share volume 

(in millions of 

shares) 809 1,042 1,240 1,441 1,398 1,457

Average trade 

size

 (number of 

shares) 1,205 1,187 907 666 488 393
Page 50 GAO-05-535 Securities Markets

Broker-dealers Revenues 

from NASDAQ Activities 

Have Also Fallen since 

Decimal Pricing Began

Decimal pricing has also generally negatively affected the profitability of 

firms that make markets in NASDAQ stocks. Traditionally, these firms 

earned revenue by profitably managing their inventories of shares and 

earning the spread between the prices at which they bought and sold 

shares. With the reduced bid-ask spreads and declines in displayed liquidity 

that have accompanied decimal pricing, the ability of broker-dealers to 

profitably make markets in NASDAQ stocks has been significantly 

adversely affected. For example, an official from one firm said that penny 

spreads had severely curtailed the amount of revenues that market makers 

could earn from their traditional principal trading. Table 10 presents SIA 

data on all NYSE members, which SIA indicates is often used as a proxy for 

the entire industry. As the table shows, these firms’ revenues from 

NASDAQ market making activities, after rising between 1999 and 2000, 

declined about 73 percent between 2000 and 2004, falling from nearly $9 

billion to about $2.5 billion. 

Table 10: NYSE Member Broker-Dealer Revenues from NASDAQ Market Making 

Activities, 1999–2004 (in millions of dollars)

Source: SIA Databank.

Firms acting as NASDAQ market makers have also seen their operating 

expenses rise since decimal pricing began. Officials at one broker-dealer 

said that because the average trade size is smaller, market makers now 

generally process more trades to execute the same volume. This increase in 

the number of executions has required NASDAQ market makers to absorb 

additional processing and clearing costs. Additionally, the increased 

number of executions associated with decimal pricing has required some 

NASDAQ market makers to increase their investments in information 

technology systems. Table 11 shows the reduced average order size on the 

NASDAQ market over the past 6 years. 

Category 1999 2000 2001 2002 2003 2004

Revenues $6,786 $8,994 $4,648 $2,742 $2,385 $2,462
Page 51 GAO-05-535 Securities Markets

Table 11: NASDAQ Average Trade Size, and Average Daily Volume, 1999–2004

Source: NASDAQ.

Declining Intermediary 

Profits Have Accelerated 

Industry Consolidation

Declining revenues and increased operating expenses since the 

implementation of decimal pricing have encouraged some firms to merge 

with other entities and forced other smaller market intermediaries out of 

the market, accelerating a trend toward consolidation among stock 

exchange specialists and NASDAQ market makers. Generally, to date, two 

developments have contributed to the decline in the number of specialists: 

acquisitions of smaller firms by larger entities and, on the regional 

exchanges, smaller specialist firms and proprietorships leaving the 

business. As shown in table 12, the number of specialist firms operating on 

various floor-based stock exchanges has declined significantly in recent 

years. 

Table 12: Number of Specialist Firms Operating on Selected Stock Markets, 1999–

2004

Sources: Boston Stock Exchange, NYSE, and Philadelphia Stock Exchange.

The number of firms that make markets on NASDAQ has similarly 

declined. Between 2000, when 491 firms were acting as NASDAQ market 

makers, and 2004, the number of firms making markets in NASDAQ stocks 

declined to 258—a drop of more than 47 percent. According to an industry 

association official, NASDAQ market-making activity is increasingly not a 

stand-alone profitable business activity with firms but instead is conducted 

Category 1999 2000 2001 2002 2003 2004

Average trade size 

(number of shares) 808 693 782 735 580 477

Average daily volume

(in millions of shares) 1,071 1,752 1,923 1,754 1,702 1,808

Number of specialist firms

Market 1999 2004

Boston Stock Exchange 16 7

NYSE 25 7

Philadelphia Stock 

Exchange 20 3
Page 52 GAO-05-535 Securities Markets

to support other lines of business. For example, an official of a brokerdealer that makes markets in NASDAQ stocks told us that his firm has 

made no profits on its market-making operations in the last 3 years but 

continues the activity in order to present itself as a full-service firm to 

customers. 

Although fewer firms are now acting as market makers, the overall 

NASDAQ market has not necessarily been affected. Since 2000, the number 

of stocks traded on NASDAQ has declined from 4,831 to 3,295, potentially 

reducing the need for market makers. In addition, some firms that continue 

to make markets have expanded the number of stocks in which they are 

active. For example, one large broker-dealer expanded its market-making 

activities from 500 stocks to more than 1,500. A NASDAQ official told us 

that with reduced numbers of stocks being traded, the average number of 

market makers per stock has increased since decimal pricing began. As 

shown in table 13, our analysis of data from NASDAQ indicated that 

although the number of NASDAQ market makers has declined, the number 

of firms making markets in the top 100 most active NASDAQ stocks 

actually grew between 1999 and 2004. 

Table 13: Consolidation among NASDAQ Market Makers, 1999–2004

Source: NASDAQ.

Improved technology has likely helped market makers increase their ability 

to make markets in more stocks. An official at one market maker we spoke 

with explained that his firm had invested in systems that automatically 

update the firm’s price quotes across multiple stocks when overall market 

prices change, allowing the firm to manage the trading of more stocks with 

the same or fewer staff. The use of such technology helps explain why the 

number of market makers per stock has not fallen as the overall number of 

market-making firms has declined. 

1999 2000 2001 2002 2003 2004

Number of NASDAQ market 

makers 528 491 459 384 316 258

Market makers per top 100 

NASDAQ stocks 23 25 28 32 29 63
Page 53 GAO-05-535 Securities Markets

Other Factors Have 

Contributed to Declining 

Intermediary Revenues and 

Profits

Although decimal pricing affected market intermediaries’ operations, the 

changes in these firms’ revenues, profits, and viability are not exclusively 

related to the reduction in the minimum tick size. One major impact on 

firms’ revenues since 2000 has been the sharp multiyear decline in overall 

stock market prices. Securities industry revenues have historically been 

correlated with the performance of U.S. stock markets (fig. 12). After 5 

consecutive years of returns exceeding 10 percent, prices on U.S. stock 

markets began declining in March 2000, and these losses continued until 

January 2003. The performance record for U.S. stocks during this period 

represents some of the poorest investment returns for U.S. stocks over the 

last 75 years. Because intermediary revenues tend to be correlated with 

broader stock market returns, as measured by the Standard & Poor’s 500 

(S&P 500) Stock Index, many market observers we spoke with told us that 

the 3-year down market, which coincided with the transition to decimal 

pricing, contributed to reduced intermediary revenues and profits. 
Page 54 GAO-05-535 Securities Markets

Figure 12: Securities Industry Revenues and Net Income as Compared to the Performance of the S&P 500 Stock Index, 1994–

2004 

The widespread emergence of technology-driven trading techniques, such 

as algorithmic trading models, has also reportedly affected market 

intermediaries negatively. These new techniques allow institutional 

investors, which account for the bulk of stock trading volume, to execute 

trades with less active intermediary involvement. Although only brokerdealers can legally submit trades for execution on U.S. stock markets, 

broker-dealers are reportedly only charging around 1 cent per share to 

transmit orders sent electronically as part of algorithmic trading models, an 

amount that represents much less revenue than the standard commission 

of around 5 cents per share for orders broker-dealers execute using their 

own trading systems and staff. Market intermediaries’ revenues are also 

reduced by institutional investors increasing use of alternative execution 

venues such as crossing networks to execute trades. The commissions 

these venues charge are less than those of traditional broker-dealers, 

specialists, and market makers. Several market observers said that because 

Dollars in billions

0

50

100

150

200

250

300

350

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

S&P 500

Total revenue

Net income

S&P 500

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

1,300

1,400

1,500

Source: Securities Industry Association.

Year
Page 55 GAO-05-535 Securities Markets

crossing networks and algorithmic trading solutions divert order flow from 

and create price competition for traditional broker-dealers, their increased 

use is a probable factor in the reduced profitability of exchange specialists, 

floor brokers, and NASDAQ market makers. 

The increasing use of ECNs also has also likely reduced the revenues 

earned by market intermediaries. Several market participants we spoke 

with told us that the increased number of executions on ECNs, such as 

Bloomberg Tradebook, Brut, and INET, has reduced the profits of exchange 

specialists, floor brokers, and NASDAQ market makers. ECN executions 

are done on an agency/commission basis, typically in the range of 1 to 3 

cents per share, compared with traditional broker-dealer execution fees of 

approximately 5 cents per share. As a result, the activities that lower 

investors trading costs can result in lower revenues for market 

intermediaries. However, market participants noted that institutional 

investors’ use of electronic trading technologies and ECNs had been 

increasing even before decimal pricing was implemented. 

Brokerage Firms Have Made 

Adjustments to Business 

Activities and Personnel 

Levels

We found that in response to the changes brought about by decimal pricing 

and particularly to changes in institutional investors’ trading behavior, 

many stock market intermediaries had adapted their business operations 

by making investments in technology to improve trading tools and data 

management systems, reducing the size of their trading staffs, and changing 

the pricing and mix of services they offer. Most exchange specialists, floor 

brokers, NASDAQ market makers, and the broker-dealer staff that trade 

stocks listed on the exchanges we spoke with had made investments in 

new technology since the implementation of decimal pricing. For example, 

some NASDAQ market makers and listed traders were increasingly using 

aggregation software to locate pools of liquidity instead of relying on 

telephone contacts with other broker-dealers as they had in the past. 

Several intermediaries were also using algorithmic trading solutions more 

frequently to execute routine customer orders, allowing more time for their 

staff to work on more complex transactions or the trading of less liquid 

stocks. 

Other intermediary firms have responded to the more challenging business 

environment since 2000 by reducing the size of their trading staffs. Most 

stock broker-dealer firms we spoke with employed fewer human traders in 

2004 than they had before 2001. Senior traders at the firms we spoke with 

cited reduced profits and the increased number of electronic and 

automated executions as the primary reasons for the reductions in the 
Page 56 GAO-05-535 Securities Markets

number of traders they employed. Consequently, although trades executed 

by broker-dealers using computer-generated algorithms typically generated 

lower revenues from commissions than traditional executions, the reduced 

salary and overhead costs associated with employing fewer traders, we 

were told, had made it easier for some broker-dealers to maintain viable 

stock trading operations. 

We also found that market intermediaries were adapting to the new 

business environment by modifying the pricing and mix of the services they 

offered. For example, instead of trading as principals, using their own 

capital to purchase or sell shares for customers, many NASDAQ market 

makers have begun acting as agents that match such orders to other orders 

in the market. Like ECNs, these market makers charge commissions to 

match buy and sell orders. The agency/commission model provides the 

benefit of reduced risk for NASDAQ market makers because they were 

using less of their own capital to conduct trading activity. However, market 

participants told us that this activity may not generally be as profitable for 

market makers as traditional principal/dealer trading operations. Other 

firms had attempted to diversify or broaden their service offerings. For 

example, a NYSE floor brokerage firm we spoke with was attempting to 

make up for lost revenues by developing a NASDAQ market-making 

function. 

Some firms were also expanding into other product lines. For example, one 

large NASDAQ market maker we spoke with was attempting to make up for 

declining stock trading revenue by becoming a more active market maker 

in other over-the-counter stocks outside those traded on NASDAQ’s 

National Market System, including those sold on the Over-the-Counter 

Bulletin Board (OTCBB) market, which trades stocks of companies whose 

market valuations, earnings, or revenues are not large enough to qualify 

them for listing on a national securities market like NYSE or NASDAQ.51 

These stocks often trade with higher spreads on a percentage basis than do 

the stocks listed on the national exchanges. Finally, other firms had moved 

staff and other resources formerly used to trade stocks to support the 

trading of other instruments, such as corporate bonds, credit derivatives, 

or energy futures. 

51Over-the-counter stocks are those not listed on exchanges. NASDAQ’s National Market 

System includes the largest, most actively traded stocks. 
Page 57 GAO-05-535 Securities Markets

Decimal Pricing Did Not 

Appear to Affect 

Businesses’ Ability to Raise 

Capital 

The willingness and ability of broker-dealers to assist companies with 

raising capital in U.S. markets also does not appear to have diminished as a 

result of decimal pricing. Broker-dealers, acting as investment banks, help 

American businesses raise funds for operations through sales of stock and 

bonds and other securities to investors. After the initial public offering 

(IPO), such securities can be traded among investors in the secondary 

markets on the stock exchanges and other trading venues.52 Several market 

observers had voiced concerns that the reduced displayed liquidity and 

declining ability of market makers to profit from trading could reduce the 

liquidity for newly issued and less active stocks. In turn, this loss of 

liquidity could make it more difficult for firms to raise capital. We found 

that in 2002 and 2003, U.S. stock underwriting activity was down 

significantly from recent years (fig. 13). However, as figure 13 shows, 

although stock IPOs are down from record levels of the bull market of the 

late 1990s, 247 companies offered stock to the public for the first time in 

2004—up from the 2002 and 2003 levels of 86 and 85 companies, 

respectively. Additionally, stock underwriting activity measured in dollars 

rose to $47.9 billion in 2004, a level consistent with activity in the late 

1990s.

52An IPO is the first sale of stock by a private company to the general public. This process is 

often called “going public” and represents the primary market. The secondary market for 

stocks is the market where securities are traded after they are initially offered in the primary 

market.
Page 58 GAO-05-535 Securities Markets

Number of issues

0

200

400

600

800

1,000

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Dollars in billions

Number of companies issuing new stock for the first time (initial public offerings)

Dollar amounts raised

Source: Securities Industry Association.

Year

0

10

20

30

40

50

60

70

80

Figure 13: Number of IPOs and Dollars Raised, 1994–2004

Of the market participants that we spoke with, most did not believe that 

decimal pricing had affected companies’ ability to raise capital in U.S 

markets, noting that underwriting activity is primarily related to investors’ 

overall demand for stocks. More IPOs generally occur during periods with 

strong economic growth and good stock market performance. Institutional 

investors we spoke with noted that the poor growth of the U.S. economy 

after 2000 and the associated uncertainty about future business conditions 

had contributed more than decimal pricing to the reduced level of new 

stock issues in 2002 and 2003. Others cited the new Sarbanes-Oxley Act 

corporate governance and disclosure requirements, which can increase the 

costs of being a public company, as a factor that may be discouraging some 

firms that otherwise would have to sought to raise capital from filing an 

IPO. However, one broker-dealer official said that his firm was less willing 

to help small companies raise capital because of its reduced ability since 

decimal pricing began to profitably make a market in the new firm’s stock 

after its IPO.
Page 59 GAO-05-535 Securities Markets

Proposed Pilot for Higher 

Minimum Tick Size Receives 

Mixed Responses

In response to the drop in displayed liquidity and other negative conditions 

that some believe to exist in the U.S. stock markets, a proposal has been 

made to conduct a pilot that would test the use of a higher minimum tick 

for trading, but opinions among the various market participants we spoke 

with were mixed. The proposal, which was put forth by a senior official at 

one NYSE specialist firm, calls on SEC to oversee a pilot program that 

would test a 5-cent tick on 200 to 300 NYSE stocks across all markets. The 

purpose of the pilot program would be to provide SEC with information it 

could use to decide whether larger-sized ticks improve market quality in 

U.S. stock markets. 

Proponents believe that larger ticks would address some of the perceived 

negative conditions such as the reduction in displayed liquidity brought 

about with the change to penny ticks. For example, some proponents 

anticipate that investors would be more willing to display large orders 

because larger tick sizes would increase the financial risk of stepping 

ahead for other traders. Some also expected that market intermediaries 

would be more willing to trade in less liquid stocks because of the 

increased potential to profit from larger spreads. Some proponents of a 

pilot program believed 5-cent ticks would also increase the cost efficiency, 

speed, and simplicity of execution for large-order investors, especially in 

less liquid stocks. Most of the market intermediaries we spoke with 

supported the proposed 5-cent pilot for stocks. Opinions from the 

representatives of the markets we spoke with were more mixed, with 

officials from floor-based exchanges supporting the pilot, while officials 

from two of the electronic markets we spoke with did not support a change 

and officials from two others supporting the pilot under the belief that 

larger ticks would benefit less liquid stocks. 

Of the 23 institutional investors we talked with, 10 indicated support for a 

proposed 5-cent pilot, 9 did not see a need for such a pilot, and 4 were 

indifferent or had no opinion. Of those institutional investors who did not 

see the need to conduct a pilot, most indicated that 5-cent ticks would not 

increase liquidity in the markets because the negative conditions that are 

attributed to decimal pricing are more the result of the inefficiencies they 

believed existed in markets that rely on executing trades manually rather 

than using technology to execute them automatically. In addition, officials 

at several firms noted that such a pilot is unnecessary because institutional 

investors have already adjusted to penny ticks. For example, an official of a 

very large institutional investment firm noted that the challenges of 

locating sufficient numbers of shares for trading large orders had already 

been solved with advances in electronic trading and crossing networks. 
Page 60 GAO-05-535 Securities Markets

Some of these investors were also concerned that conducting such a pilot 

could have negative consequences. For example, one firm noted that 

having different ticks for different stocks could potentially confuse 

investors. Also, a trade association official noted that mandating that some 

stocks trade only in 5-cent ticks could be viewed as a form of price fixing, 

particularly for highly liquid stocks that were already trading efficiently 

using a 1-cent tick. An official from a financial markets consulting and 

research firm noted that if a pilot program were to occur, NASDAQ stocks 

should be included; this would better isolate the effects of a larger tick size 

on market quality factors since NYSE appears to be undergoing changes 

towards a more electronic marketplace, potentially making it more difficult 

to interpret the study’s results.

In addition, some of the 10 institutional investors that supported a pilot of 

nickel-sized ticks indicated that they saw such ticks as being useful 

primarily for less-liquid stocks that generally have fewer shares displayed 

for trading, including smaller capitalized stocks. These proponents told us 

that 5-cent ticks might increase displayed liquidity for such stocks. In 

addition, they stated that 5-cent ticks could provide financial incentive for 

intermediaries to increase their participation in the trading of such stocks, 

including providing greater compensation for market makers and 

specialists to commit more capital to facilitate large-order trades. Many 

also anticipated a reduction in stepping ahead since it would become more 

costly to do so. SEC staff that we asked about the pilot told us that 

conducting such a test did not appear to be warranted because, to date, the 

benefits of penny pricing—most notably the reduction in trading costs 

through narrower spreads—seem clearly to justify the costs. They also 

noted that penny pricing does not, and is not designed to, establish the 

optimal spread in a particular security, which will be driven by market 

forces.

Decimal Pricing Has 

Had a Limited Impact 

on the Options 

Markets, but Other 

Factors Have Helped 

Improve Market 

Quality

Decimal pricing in U.S. options markets has generally had a more limited 

impact on the options market than it has on the stock market. Although 

various measures of market quality, including trading costs and liquidity, 

have improved in U.S. options markets, factors other than decimal pricing 

are believed to be the primary contributors. First, the tick size reductions 

adopted for options trading were less dramatic than those adopted in the 

stock markets. Second, other factors, including increased competition 

among exchanges to list the same options, the growing use of electronic 

trading, and a new system that electronically links the various markets, 

were seen as being more responsible for improvement in U.S. options 
Page 61 GAO-05-535 Securities Markets

markets. Options market intermediaries such as market makers and 

specialists have had mixed experiences since decimal pricing began, with 

floor-based firms facing declining revenues and profitability and electronicbased firms seeing increased trading revenues and profitability. As part of a 

concept release on a range of issues pertaining to the options markets, SEC 

has sought views on reducing tick sizes further in the options markets by 

lowering them from the current 5 and 10 cents to one penny. Options 

market participants were generally strongly opposed to such a move for a 

variety of reasons, including the possibility that the number of quotes could 

increase dramatically, overwhelming information systems, and the 

potential for reduced displayed liquidity. 

The Shift to Decimal Pricing 

Did Not Reduce Tick Sizes 

for Options as Much as for 

Stocks 

One reason that decimal pricing’s impact on options markets was not seen 

as significant was that the tick size reductions for options market were not 

as large as those adopted for the stock markets. Options markets had 

previously used a minimum tick size of 1/8 of a dollar (12.5 cents) for 

options contracts priced at $3 and more and a tick size of 1/16 of a dollar 

(6.25 cents) for options priced at less than $3. After decimal pricing came 

into effect, these tick sizes fell to 10 cents and 5 cents, respectively—a 

decrease of 20 percent. This decline was far less than the 84 percent 

reduction in tick size in the stock market, where the bid-ask spread 

dropped from 1/16 of a dollar to 1 cent. 
Page 62 GAO-05-535 Securities Markets

Studies done by four options exchanges in 2001 to assess the impact of 

decimal prices on, among other factors, options contract bid-ask spreads 

did not find that decimal pricing had any significant effect on the spreads 

for options.53 Most market participants shared this view. For example, an 

official of a large market-making firm stated that decimalization in the 

options market was “a small ripple in a huge pond.” 

Although Decimal Pricing 

Not Significant, Key 

Measures of Options 

Markets’ Quality Have 

Improved

Although decimal pricing’s impact was not seen as significant, various 

measures used to assess market quality have shown improvements in U.S. 

options markets in recent years. Unlike for stocks, data on trading costs in 

options markets was not generally available. For example, we could not 

identify any trade analytics or other firms that collected and analyzed data 

for options trading. However, some market participants we interviewed 

indicated that bid-ask spreads, which represent a measure of cost of 

trading in options markets, have narrowed since the 1990s. In addition, the 

studies done by SEC and others also indicated that spreads have declined 

for options markets. 

In addition to lower trading costs, liquidity, which is another measure that 

could be used to assess the quality of the options market, has improved 

since decimal pricing was implemented. According to industry participants 

we interviewed, liquidity in the options market has increased since 2001. 

They noted that trading volumes (which can be an indicator of liquidity) 

had reached historic levels and that many new liquidity providers, such as 

hedge funds and major securities firms, had entered the market. As shown 

in figure 14, options trading volumes have grown significantly (61 percent) 

since 2000, rising from about 673 million contracts to an all-time high of 

1.08 billion contracts in 2004. 

53The four option exchanges whose studies we obtained include the American Stock 

Exchange, Chicago Board Options Exchange, Pacific Exchange, and Philadelphia Stock 

Exchange.
Page 63 GAO-05-535 Securities Markets

Figure 14: Total Contract Trading Volumes for Stock Options, 2000–2004 (Volume in 

millions)

However, some market participants noted that the implementation of 

decimal pricing in the stock markets had negatively affected options 

traders. According to these participants, the reduced number of shares 

displayed in the underlying stock markets and quote flickering in stock 

prices had made buying and selling shares in the stock markets and 

determining an accurate price for the underlying stocks more difficult.54 As 

a result, options traders’ and market makers’ attempts to hedge the risks of 

their options positions by trading in the stock markets had become more 

challenging and costly. 

54Quotes “flicker” on trading information screens when the prices of underlying stocks are 

changing too rapidly.

0

200

400

600

800

1,000

1,200

2000 2001 2002 2003 2004

Contract trading volume (in millions)

Year

Source: GAO analysis of Options Clearing Corporation data.

672.9

722.7 709.8

830.3

1,083.6
Page 64 GAO-05-535 Securities Markets

Factors Other Than Decimal 

Pricing Have Been Credited 

with Improving the Quality 

of Options Markets 

Market participants attributed the improvements in market quality for U.S. 

options markets not to decimal pricing but to other developments, 

including the practice of listing options contracts on more than one 

exchange (multilisting), the growing use of electronic exchanges, and the 

development of electronic linkages among markets. These developments 

have increased competition in these markets. Multilisting, one of the most 

significant changes, created intense competition among U.S. options 

markets.55 Although SEC had permitted multilistings since the early 1990s, 

the options exchanges had generally tended not to list options already 

being actively traded on another exchange, but began doing so more 

frequently in August 1999.56 According to an SEC study, in August 1999, 32 

percent of stock options were traded on more than one exchange, and that 

percentage rose steadily to 45 percent in September 2000. The study also 

showed that the percentage of total options volume traded on only one 

exchange fell from 61 percent to 15 percent during the same period. Almost 

all actively traded stock options are now listed on more than one U.S. 

options exchange. 

55The first multiple listing of an options contract occurred in February 1976 when the 

Chicago Board Options Exchange multilisted options on the stock of the Boise Cascade 

Corporation, which had previously been listed only by the Philadelphia Stock Exchange. 

56In September 2000, both the Department of Justice and SEC reached a settlement with the 

American Stock Exchange, Chicago Board Options Exchange, Pacific Exchange, and 

Philadelphia Stock Exchange with respect to alleged anticompetitive activities and the 

failure to adequately enforce compliance with their own rules.
Page 65 GAO-05-535 Securities Markets

Multilisting has been credited with increasing price competition among 

exchanges and market participants. The SEC study examined, among other 

things, how multiple listings impacted pricing and spreads in the options 

market and found that the heightened competition had produced 

significant economic benefits to investors in the form of lower quoted and 

effective spreads.57 The study looked at 1-week periods, beginning with 

August 9 through 13, 1999 (a benchmark period prior to widespread 

multilisting of actively traded options), and ending with October 23 through 

27, 2000 (a benchmark period during which the actively traded options in 

the study were listed on more than one exchange). During this period, the 

average quoted spreads for the most actively traded stock options declined 

8 percent. Quoted spreads across all options exchanges over this same 

period showed a much more dramatic change, declining approximately 38 

percent. The actual transaction costs that investors paid for their options 

executions, as measured by effective spreads, also declined, falling 19 

percent for options priced below $20 and 35 percent for retail orders of 50 

contracts or less. Several academic studies also showed results consistent 

with SEC’s findings that bid-ask spreads had declined since the widespread 

multiple listing of the most active options.58 

The introduction of the first all-electronic options exchange in 2000 also 

increased competition in the options markets. Traditionally, trading on U.S. 

options markets had occurred on the floors of the various exchanges. On 

the new International Securities Exchange (ISE), which began operations 

in May 2000, multiple (i.e., competing) market makers and specialists can 

submit separate quotes on a single options contract electronically. The 

quotes are then displayed on the screens of other market makers and at the 

facilities of broker-dealers with customers interested in trading options, 

enhancing competition for customer orders. ISE also introduced the 

practice of including with its quotes the number of contracts available at 

57SEC, Office of Compliance Inspections and Examinations and Office of Economic 

Analysis, Special Study: Payment for Order Flow and Internalization in the Options 

Markets, December 2000. The quoted spread is the difference between the displayed bid and 

ask prices and generally measures retail trading costs, since retail investors typically 

conduct transactions at these prices. The effective spread measures the trading cost relative 

to the midpoint of the quoted spread at the time the trade occurred. The lower the effective 

spread, the lower the cost to investors.

58See Patrick De Fontnouvelle, Raymond P.H. Fishe, and Jeffrey H. Harris, “The Behavior of 

Bid-Ask Spreads and Volume in Options Markets During the Competition for Listings in 

1999,” The Journal of Finance, vol. 58, no. 6, (December 2003); Battalio, Robert, Brian 

Hatch and Robert Jennings, “Toward a National Market System for U.S. Exchange-Listed 

Stock Options,” The Journal of Finance, vol. 59, no. 2, (April 2004).
Page 66 GAO-05-535 Securities Markets

the quoted price. According to market participants, the additional 

information benefited retail and institutional investors by providing them 

with better information on the depth of the market and the price at which 

an order was likely to be executed. Finally, ISE allowed customers to 

execute trades in complete anonymity and attracted additional sources of 

liquidity by allowing market makers to access its market remotely. 

In response, the four floor-based options exchanges—the American Stock 

Exchange, Chicago Board Options Exchange (CBOE), the Pacific 

Exchange (PCX), and the Philadelphia Stock Exchange—also began 

including the number of available contracts with their quotations and 

offering electronic trading systems in addition to their existing floor-based 

trading model.59 Another new entrant, the Boston Options Exchange (BOX) 

(an affiliate of the Boston Stock Exchange) also began all-electronic 

operations in 2004. The result has been increased quote competition among 

markets and their participants that has helped to further narrow spreads 

and has opened markets to a wide range of new liquidity providers, 

including broker-dealers, institutional firms, and hedge funds. 

Electronic linkages were first introduced to U.S. options markets in 2003, 

offering the previously unavailable opportunity to route orders among all 

the registered options exchanges. In January 2003, SEC announced that the 

options markets had implemented the intermarket linkage plan, so that 

U.S. options exchanges could electronically route orders and messages to 

one another. The new linkages further increased competition in the options 

industry and made the markets more efficient, largely by giving brokers, 

dealers, and investors’ better access to displayed market information. 

According to SEC and others, as a result of this development investors can 

now receive the best available prices across all options exchanges, 

regardless of the exchange to which an order was initially sent. Intermarket 

linkages are as essential to the effective functioning of the options markets 

as they are to the functioning of the stock markets and will further assist in 

establishing a national options market system.

59These systems include the American Stock Exchange’s ANTE, the CBOE’s Hybrid Trading 

System, the Pacific Exchange’s PCX Plus, and the Philadelphia Stock Exchange’s XL. The 

two electronic-based option exchanges are BOX and ISE.
Page 67 GAO-05-535 Securities Markets

The Impact on Options 

Market Intermediaries 

Varied since Decimal 

Pricing Began

Decimal pricing and other changes in options markets appear to have 

affected the various types of market intermediaries differently. 

Representatives of firms that trade primarily on floor-based exchanges told 

us that their revenues and profits from market making had fallen while 

their expenses had increased. For example, one options specialist said that 

his firm’s profitability had declined on a per-option basis and was now back 

to pre-1995 levels. However, he noted that the cost of technology to operate 

in today’s market had increased substantially and that adverse market 

conditions and increased competition were more responsible for his firm’s 

financial conditions than were decimal prices. 

The increasingly competitive and challenging environment has also led to 

continued consolidation among firms that trade on the various options 

exchange floors. According to data from one floor-based options exchange, 

the number of market intermediaries active on its market declined 

approximately 22 percent between 2000 and 2004. Market intermediaries 

and exchange officials we spoke with noted in particular that the smaller 

broker-dealer firms that trade options and sometimes have just one or two 

employees had been the most affected, with many either merging with 

other firms or going out of business because of their inability to compete in 

the new trading environment. 

In contrast, the introduction of electronic exchanges and expanded 

opportunities for electronic trading at other exchanges has been beneficial 

for some market intermediaries. Officials of some broker-dealers that trade 

options electronically told us that their firms’ operations had benefited 

from the increased trading volume and the efficiency of electronic trading. 

The officials added that other firms, such as large financial institutions, had 

increased their participation in the options marketplace. They also noted 

that the availability of electronic trading systems and the inherent 

economies of scale associated with operating such systems had attracted 

new marketplace entrants, including some hedge funds and major 

securities firms. For example, representatives of ISE and several brokerdealers told us that the ability to trade electronically had encouraged 

several large broker-dealers that were not previously active in options 

markets to begin acting as market makers on that exchange. These firms, 

they explained, were able to enter into the options markets because 

making markets electronically is less expensive than investing in the 

infrastructure and staff needed to support such operations on a trading 

floor. According to market participants we spoke with, these new entrants 

appeared to have provided increased competition and positively affected 

spreads, product innovation, and liquidity in the options industry. 
Page 68 GAO-05-535 Securities Markets

Options Market Participants 

Oppose Lower Minimum 

Ticks for the Options 

Industry

In 2004, SEC issued a concept release that sought public comments on 

options-related issues that have emerged since the multiple listing of 

options began in 1999, including whether the markets should reduce the 

minimum tick sizes for options from 5 and 10 cents to 1-cent increments.60 

According to the release, SEC staff believed that penny pricing in the 

options market would improve the efficiency and competitiveness of 

options trading, as it has in the markets for stocks, primarily by tightening 

spreads. If lower ticks did lead to narrower spreads for options prices, 

investors trading costs would likely similarly decline. As of May 2004, SEC 

has received and reviewed comments on the concept release but has taken 

no further action.

All of the options exchanges and virtually all of the options firms we spoke 

with, as well as 15 of the 16 organizations and individuals that submitted 

public comments on SEC’s 1-cent tick size proposal, were opposed to 

quoting options prices in increments lower than those currently in use (10 

and 5 cents, depending on the price of an options contract). One of the 

primary reasons for this opposition was that trading options contracts in 1-

cent increments would significantly increase quotation message traffic, 

potentially overwhelming the capacity of the existing systems that process 

options quotes and disrupting the dissemination of market data. For any 

given stock, hundreds of different individual options contracts can be 

simultaneously trading, with each having a different strike price (the 

specified price at which the holder can buy or sell underlying stock) and 

different expiration date.61 Because options are contracts that provide their 

holders with the right to either buy or sell a particular stock at the specified 

strike price, an option’s value and therefore its price also changes as the 

60Competitive Developments in the Options Markets, Exchange Act Release No. 49175 (Feb. 

3, 2004), 69 Fed. Reg. 6124 (2004). In this release, SEC also sought comments on a variety of 

issues, including payment for order flow, internalization, and specialist participation 

guarantees. Payment for order flow is an arrangement under which a broker is paid to route 

its customer orders to a particular market for execution. Internalization occurs when a 

brokerage firm fills a customer’s order from the broker’s own inventory of securities without 

exposing the order to the market. Specialist participation guarantees offer these 

intermediaries a percentage of the order flow from a particular options exchange for 

providing liquidity, depth, and continuity in that market.

61An actively-traded stock like International Business Machines (IBM) may have thousands 

of options available for trading. For example, if IBM’s stock price is around $100, options 

granting the right to buy or sell the stock are likely trading with strike prices of $90, $95, 

$100, $105, $110, etc., and each of these prices will have separate options expiration dates 

(months). Simultaneously, trading will also be occurring in both call options and put options 

using the same strike prices and expiration months. 
Page 69 GAO-05-535 Securities Markets

underlying stock’s price changes. If options were priced in pennies, market 

participants said that thousands of new option price quotes could be 

generated because prices would need to adjust more rapidly to remain 

accurate than they do using nickel or dime increments. 

Markets and market participants also expressed concerns that penny 

pricing would exacerbate an already existing problem for the industry—

ensuring that the information systems used to process and transmit price 

quotations to market participants have adequate capacity. The quotes 

generated by market makers on the various markets are transmitted by the 

systems overseen by the Options Price Reporting Authority (OPRA). The 

OPRA system has been experiencing message capacity issues for several 

years. In terms of the number of messages per second (mps) that can be 

processed, the OPRA system had a maximum mps of 3,000 in January 2000. 

Since then, the processing and transmission capacity of the system has had 

to be expanded significantly to accommodate the growth in options’ 

quoting volumes, and as of April 2005, the OPRA system was capable of 

processing approximately 160,000 mps. Prior to the implementation of 

decimal pricing in 2001, similar concerns about the impact on message 

traffic volumes were also raised for stocks, but the magnitude of the 

anticipated increases were much larger for options. 

To address the capacity constraints in the options market systems thus far, 

the administrators of the OPRA system have tried to reduce quotation 

traffic by having the options exchanges engage in quote mitigation. Quote 

mitigation requires the exchanges to agree to prioritize their own quotes 

and trade report message volumes so that the amount of traffic submitted 

does not exceed a specified percentage of the system’s total capacity. As of 

April 2005, the OPRA administrators were limiting the volume of messages 

that exchanges were able to transmit to just 88,000 mps based on requests 

from the six options exchanges. 

Two market participants that commented on SEC’s proposal noted that 

with options market data continuing to grow at a phenomenal rate each 

year, OPRA would have to continue increasing its current message capacity 

to meet ongoing demand. If penny quoting were to create even faster 

growth in the total number of price quotes generated, market participants 

indicated that options exchanges, market data vendors, and broker-dealers 

would need to spend substantial sums of money on operational and 

technological improvements to their capacity and communication systems 

in order to handle the increased amounts of market data. These costs, they 

said, would likely be passed on to investors. 
Page 70 GAO-05-535 Securities Markets

Another reason that market participants objected to lowering tick sizes for 

options trading was that doing so would likely reduce market 

intermediaries’ participation in the markets. Because these intermediaries 

make their money from the spreads between the bid and offer prices, 

narrower spreads that would likely accompany penny ticks would also 

reduce these intermediaries’ revenues and profits. This, in turn, would 

reduce these firms’ ability and willingness to provide liquidity, especially 

for options that are traded less frequently. According to the commenters on 

the proposal and the participants we contacted, intermediaries would 

likely become reluctant to provide continuous two-sided markets (e.g., 

offering both to buy and sell options simultaneously) to facilitate trading, 

since profit potential would be limited by the 80 percent or more reduction 

in tick size. And because the 1-cent tick could increase the chance of other 

traders stepping ahead of an order, such intermediaries could become 

reluctant to display large orders. With the options markets having hundreds 

of options for one underlying stock, market intermediaries would likely 

quote fewer numbers of contracts, which would further reduce displayed 

liquidity, and market transparency. 

Market participants also raised other concerns about trading in penny ticks 

for options. For example, they worried that option prices quoted in 1-cent 

increments would change in price too rapidly, resulting in more quote 

“flickering.” They also noted that the options market could experience 

some of the other negative effects that have occurred in the stock markets, 

including increasing instances of stepping ahead by other traders. 

SEC staff responsible for options markets oversight told us that they would 

like to see tick sizes reduced in the options markets as a means of lowering 

costs to investors. They acknowledged that the benefits of such tick size 

reductions would have to be balanced with the likely accompanying 

negative impacts. SEC staff responsible for options markets oversight told 

us that they would like to see tick sizes reduced in the options markets as a 

means of lowering costs to investors. They acknowledged that the benefits 

of such tick size reductions would have to be balanced with the likely 

accompanying negative impacts. They noted that recent innovations permit 

a small amount of trading in pennies and that continued innovation and 

technological advances may lead to approaches more favorable to 

investors without substantial negative effects.
Page 71 GAO-05-535 Securities Markets

Observations In advocating decimal pricing, Congress and SEC expected to make stock 

and options pricing easier for the average investor to understand and 

reduce trading costs, particularly for retail investors, from narrower bidask spreads. These goals appear to have been met. Securities priced in 

dollars and cents are clearly more understandable, and the narrower 

spreads that have accompanied this change have made trading less costly 

for retail investors. Although the resulting trading environment has become 

more challenging for institutional investors, they too appear to have 

benefited from generally lower trading costs since decimal pricing was 

implemented. In response to the reduced displayed market depth, 

institutional investors are splitting larger orders into smaller lots to reduce 

the market impact of their trading and accelerating their adoption of 

electronic trading technologies and alternative trading venues. As a result 

of these adaptations, institutional investors have been able to continue to 

trade large numbers of shares and at even less total cost than before. 

However, since decimal pricing was introduced, the activities performed by 

some market intermediaries have become less profitable. Decimal prices 

have adversely affected broker-dealers’ ability to earn revenues and profits 

from their stock trading activities. But one of the goals of decimal pricing 

was to lower the artificially established tick size, and thus the loss of 

revenue for market intermediaries that had benefited from this price 

constraint was a natural outcome. Various other factors, including 

institutional investors’ adoption of electronic technologies that reduce the 

need for direct intermediation, can also explain some of market 

intermediaries’ reduced revenues. Nevertheless, the depressed financial 

condition of some intermediaries would be of more concern if conditions 

were also similarly negative for investors, which we found was not the 

case. 

In response to the changes since decimal pricing began, a proposal has 

been made to conduct a pilot program to test higher tick sizes. This 

program would provide regulators with data on the impacts, both positive 

and negative, of such trading. However, given that many investors and 

market intermediaries have made considerable efforts to adapt their 

trading strategies and invest in technologies that allow them to be 

successful in the penny tick trading environment, the need for increased 

tick sizes appears questionable. 

Although decimal pricing has been a less significant development in U.S. 

options markets, other factors, such as new entrants and the increased use 
Page 72 GAO-05-535 Securities Markets

of electronic trading and linkages, have served to improve the quality of 

these markets. SEC’s proposal to further reduce tick sizes in the options 

markets has been met with widespread opposition from industry 

participants, and many of the concerns market participants raised, 

including the potential for significant increases in quote traffic and less 

displayed liquidity, appear to have merit. The magnitude of these potential 

impacts appears larger than those that accompanied the implementation of 

penny ticks for stocks. As a result, it is not clear that additional benefits of 

the narrower spreads that could accompany mandated tick size reductions 

would be greater than the potentially negative impacts and increased costs 

arising from greatly increased quote processing traffic. 

Agency Comments We provided a draft of this report to SEC for comments and we received 

oral comments from staff in SEC’s Division of Market Regulation and Office 

of Economic Analysis. Overall, these staff said that our report accurately 

depicted conditions in the markets after the implementation of decimal 

pricing. They also provided various technical comments that we 

incorporated where appropriate. 

As agreed with your offices, unless you publicly announce its contents 

earlier, we plan no further distribution of this report until 30 days after the 

date of this report. At that time, we will send copies of this report to the 

Chairman and Ranking Minority Member, Subcommittee on Securities and 

Investments, Senate Committee on Banking, Housing, and Urban Affairs. 

We will also send copies of this report to the Chairman, SEC. We will make 

copies available to others upon request. This report will also be available at 

no charge on GAO’s Web site at http://www.gao.gov.
Page 73 GAO-05-535 Securities Markets

Please contact me at (202) 512-8678 if you or your staff have any questions 

concerning this report. Contact points for our Offices of Congressional 

Relations and Public Affairs may be found on the last page of this report. 

Key contributors to this report are listed in appendix V.

Richard J. Hillman

Director, Financial Markets and

Community Investment
Page 74 GAO-05-535 Securities Markets

Appendix I

Scope and Methodology ApApenednxidexiIs

To determine the impact of decimal pricing on retail investors, we analyzed 

data from a database of trades and quotes from U.S. stock markets between 

February 2000 and November 2004. Appendix II contains a detailed 

methodology of this analysis. Using this data, we selected a sample of 

stocks traded on the New York Stock Exchange (NYSE) and the NASDAQ 

Stock Market (NASDAQ) and calculated how the trading in these stocks 

had changed between a 1-year period before and an almost 4-year period 

after decimal pricing began. As part of this analysis, we examined the 

changes in spreads on these stocks (the relevant measure of trading costs 

for retail investors). We also undertook steps to assess the reliability of the 

data in the TAQ database by performing a variety of error checks on the 

data and using widely accepted methods for removing potential errors from 

data to ensure its reliability. Based on these discussions, we determined 

that these data were sufficiently reliable for our purposes. We also 

reviewed market and academic studies of decimal pricing’s impact on 

spreads. In addition, we interviewed officials from over 30 broker-dealers, 

the Securities and Exchange Commission (SEC), NASD, two academics, 

and five alternative trading venues, eight stock markets, four trade 

analytics firms, a financial markets consulting and research firm, and four 

industry trade groups. 

Methodology for 

Assessing Impact on 

Institutional Investors

To analyze the impact of decimal pricing on institutional investors, we 

obtained and analyzed institutional trading cost data from three leading 

trade analytics firms—Plexus Group, Elkins/McSherry, and Abel/Noser—

spanning from the first quarter of 1999 through second quarter of 2003 from 

the Plexus Group and from the fourth quarter of 1998 to the end of 2004 

from Elkins/McSherry and Abel/Noser—to determine how trading costs for 

institutional investors responded to decimalization. These firms’ data do 

not include costs for trades that do not fully execute. To address this issue, 

we interviewed institutional investors on their experiences in filling large 

orders. We also undertook steps to assess the reliability of the trade 

analytics firms’ data by interviewing their staffs about the steps the firms 

follow to ensure the accuracy of their data. Based on these discussions, we 

determined that these data were sufficiently reliable for our purposes. 

To identify all relevant research that had been conducted on the impact of 

decimal pricing on institutional investors’ trading costs, we searched public 

and private academic and general Internet databases and spoke with 

academics, regulators, and market participants. We identified 15 academic 

studies that met our criteria for scope and methodological considerations. 
Appendix I

Scope and Methodology

Page 75 GAO-05-535 Securities Markets

Of these, 3 addressed trading costs for institutional investors and 12 

addressed trading costs for retail investors. 

To determine the impact of pricing on investors’ ability to trade, we 

interviewed roughly 70 judgmentally selected agencies and firms, including 

representatives of 23 institutional investors with assets under management 

ranging from $2 billion to more than $1 trillion. The assets being managed 

by these 23 firms represented 31 percent of the assets under management 

by the largest 300 money managers in 2003. In addition, we also discussed 

the impact on intuitional investors during our interviews with brokerdealers, securities regulators, academics, and alternative trading venues, 

stock exchanges, trade analytics firms, a financial market consulting and 

research firm, and industry trade groups. 

Methodology for 

Assessing Impact on 

Market Intermediaries

To assess the impact of decimal pricing on stock market intermediaries, we 

obtained data on the revenues of the overall securities industry from the 

Securities Industry Association (SIA). SIA’s revenue data come from the 

reports that each broker-dealer conducting business with public customers 

is required to file with SEC—the Financial and Operational Combined 

Uniform Single (FOCUS) reports. We used these data to analyze the trend 

in revenues for the industry as a whole as well as to identify the revenues 

associated with making markets in NASDAQ stocks. In addition, we 

obtained data on the specialist broker-dealer revenues and participation 

rates and on executed trade sizes from NYSE. For the number of specialist 

firms participating on U.S. markets, we sought data from NYSE and the 

other exchanges, including the American Stock Exchange (Amex), the 

Boston Stock Exchange, the Chicago Stock Exchange, the Pacific 

Exchange (PCX), and the Philadelphia Stock Exchange (Phlx). We 

obtained data on the number of market makers and the trend in executed 

trade size from NASDAQ. We discussed how these organizations ensure the 

reliability of their data with officials from the organizations where relevant 

and determined that their data were sufficiently reliable for our purposes. 

We also discussed the impact of decimals on market intermediaries during 

our interviews with officials from broker-dealers, securities regulators, 

alternative trading venues, stock exchanges, trade analytics firms, a 

financial market consulting and research firm, and industry trade groups, 

as well as experts from academia.
Appendix I

Scope and Methodology

Page 76 GAO-05-535 Securities Markets

Methodology for 

Assessing Impact on 

Options Markets 

To determine the impact of decimal pricing on the options markets, both 

investors and intermediaries, we reviewed studies that four U.S. options 

exchanges, including Amex, Chicago Board Options Exchange (CBOE), 

PCX, and Phlx, submitted to SEC in 2001 on the impact of decimalization 

on their markets. We also performed literature searches on the Internet for 

academic and other studies that examined the impact of decimal pricing on 

options markets. In addition, we also attempted to identify any sources or 

organizations that collected and analyzed options trading costs. 

To determine the impact on intermediaries, we interviewed officials of all 

six U.S. options exchanges, including Amex, Boston Options Exchange, 

CBOE, International Securities Exchange, PCX, and Phlx, and various 

market participants (an independent market maker, designated primary 

market makers, specialists, a floor broker, hedge funds and a retail investor 

firm) to ascertain their perspectives on the impact of the conversion to 

decimalization on them, investors, and the markets. 

To determine the potential impact of reducing the minimum price tick in 

the options markets to a penny, we interviewed officials from the option 

exchanges and market participants. We also reviewed all comment letters 

that SEC had received on its concept release discussing potential changes 

in options market regulation, including lowering the minimum tick size in 

the options markets to a penny. We reviewed those letters posted on SEC’s 

Web site as of May 4, 2005. Sixteen of these letters specifically commented 

on the penny-pricing proposal. 
Page 77 GAO-05-535 Securities Markets

Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data ApendxiI

To assess the impact of decimal pricing, one of the activities we performed 

was to analyze data from the New York Stock Exchange (NYSE) Trade and 

Quote (TAQ) database spanning the 5-year period between February 2000 

(before the conversion to decimal pricing) and November 2004 (after the 

adoption of decimal pricing) to determine how trading costs for retail 

investors changed and how various market statistics changed, such as the 

average number of shares displayed at the best prices before and after 

decimalization. Although maintained by NYSE, this database includes all 

trades and quotes that occurred on the various exchanges and the NASDAQ 

Stock Market (NASDAQ). Using this database, we performed an event-type 

study analyzing the behavior of trading cost and market quality variables 

for NYSE and NASDAQ stocks in pre- and postdecimalization 

environments.1

 For each of our sample stocks, we used information on 

each recorded trade and quote (that is, intraday trade and quote data) for 

each trading day in our sample period. We generally followed the methods 

found in two recently published academic studies that examined the 

impact of decimalization on market quality and trade execution costs.2

 In 

particular, we analyzed the pre- and postdecimalization behavior of several 

trading cost and market quality variables, including various bid-ask spread 

measures and price volatility, and we also analyzed quote and trade 

execution price clustering across NYSE and NASDAQ environments. We 

generally presented our results on an average basis for sample stocks in a 

given market in the pre- and postdecimalization periods; in some cases we 

separated sample stocks into groups based on their average daily trading 

1

Throughout, “decimalization” reflects the transition from fractional pricing (that is, pricing 

generally in sixteenths of a dollar) to decimal pricing (that is, pricing in round cents) and, 

more significantly, the 84 percent reduction in the minimum price increment, or tick, from 

one-sixteenth of a dollar to 1 cent. Decimalization was fully implemented on the NYSE on 

January 29, 2001, but not until April 9, 2001, on NASDAQ. An event study is the analytical 

framework used to measure the economic effect of an event, such as the transition from 

fractional to decimal pricing.

2

Hendrik Bessimbinder, 2003, “Trade Execution Costs and Market Quality after 

Decimalization,” Journal of Financial and Quantitative Analysis 38(4), 747-777; and K. 

Chung, B. Van Ness, and R. Van Ness, 2004, “Trading Costs and Quote Clustering on the 

NYSE and NASDAQ after Decimalization,” Journal of Financial Research 27(3), 309-328. 

Bessimbinder (2003) performed an event study analysis of the impact of decimalization on 

several trade execution cost and market quality measures using a sample of NYSE-listed and 

NASDAQ stocks in a predecimalization period and a postdecimalization period. While not 

an event study, Chung et al. (2004) focused on the differences in several trade execution 

cost and market quality measures between a sample of NYSE-listed and NASDAQ stocks in 

the month after decimalization; they also analyzed quote clustering, which is the tendency 

for quotes to “cluster” at certain price points, such as nickels and dimes.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 78 GAO-05-535 Securities Markets

volume and reported our results so that any differences across stock 

characteristics could be observed.3

Our analysis was based on intraday trade and quote data from the TAQ 

database, which includes all trade and quote data (but not order 

information) for all NYSE-listed and NASDAQ stocks, among others. TAQ 

data allowed us to study variables that are based on trades and quotes but 

did not allow us to study any specific effects on or make any inferences 

regarding orders or institutional trading costs.4

 

Our data consisted of trade and quote activity for all stocks listed on NYSE, 

NASDAQ, and the American Stock Exchange (Amex) from February 1, 

2000, through November 30, 2004, excluding the month of September 2001. 

We focused on NYSE-listed and NASDAQ issues, as is typical in the 

literature, since the potential sample size from eligible Amex stocks tends 

to be much smaller. Our analysis compared 300 matched NYSE and 

NASDAQ stock pairs over the 12 months prior to decimalization and 12 

months selected from the period spanning April 2001 through November

3

Stocks were grouped by volume according to the following categories:

• High volume stocks were those in our sample of stocks with average daily trading 

volumes exceeding 500,000 shares (the maximum was less than 1.6 million shares).

• Medium volume stocks were those in our sample of stocks with average daily trading 

volumes between 100,000 and 499,999 shares.

• Low volume stocks were those in our sample of stocks with average daily trading 

volumes of less than 100,000 shares.

4

For example, since an order can often be filled through a number of trade executions, and 

use of TAQ data implicitly assumes that each trade record reflects a unique order that is 

filled, our analysis failed to address any impact of a change in how orders are filled and the 

costs associated with this.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 79 GAO-05-535 Securities Markets

2004.5 In constructing our sample period, we omitted the months of 

February and March 2001 from consideration, because not all stocks were 

trading using decimal prices during the transition period. 

Because there were a host of concurrent factors impacting the equities 

markets around the time of and since the transition to decimal pricing, it is 

unlikely that any of our results can be attributed solely to decimalization. 

Any determination of statistically significant differences in pre- and 

postdecimalization trading cost and market quality variables was likely due 

to the confluence of decimalization and these other factors.

Determining the Sample Period 

for Our Analysis

Determining the best sample period presented a challenge because 

decimalization was implemented at different times on NYSE and NASDAQ. 

The transition to decimal pricing was completed on NYSE on January 29, 

2001, while on NASDAQ it was completed on April 9, 2001. In addition, 

there were selected decimalization pilots on NYSE and NASDAQ prior to 

full decimalization on each. Researchers who have analyzed the transition 

to decimal pricing have generally divided up the pre- and 

postdecimalization sample periods differently depending on the particular 

focus of their research.6

 Relatively short sample periods too close to the 

transition might suffer from unnatural transitory effects related to the 

learning process in a new trading environment, while sample periods 

farther from the implementation date or longer in scope might suffer from 

the influence of confounding factors. Analyses comparing different months 

before and after decimalization (e.g., December 2000 versus May 2001) 

5

Since there are important structural differences between the NYSE and NASDAQ markets 

and the stocks listed on each, a general analysis of the effect of an event on both markets 

could yield biased results if the stock samples are not chosen carefully. For this reason, 

researchers analyzing the impact of decimalization on the NYSE and NASDAQ usually 

employ a matched-pairs analysis. For our analysis, a matched pair consisted of one NYSElisted stock and one NASDAQ stock (among all NASDAQ stocks) that provided the closest 

match to it in terms of characteristics related to trading activity, such as share price and 

average daily trading volume, which are generally thought to explain variation in bid-ask 

spreads, among other things. By matching the stocks on these characteristics, a matchedpairs analysis attempts to isolate the effect of an event on the different markets by 

considering how it affects groups of analogous stocks.

6

Bessimbinder (2003) separated the pre and postdecimalization periods as the 3 weeks 

before January 29, 2001, and from April 9, 2001 through August 31, 2001. Chung et al. (2004) 

considered only May 2001, as their focus was not on a pre- versus postdecimalization 

comparison. Other studies, both from researchers and exchanges, examining decimalization 

often selected a 1-month or shorter period sometime shortly before decimalization and an 

analogous period sometime after decimalization for comparison.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 80 GAO-05-535 Securities Markets

might suffer from seasonal influences. We extended the current body of 

research, which includes studies by academic and industry researchers, 

exchanges and markets, and regulators, by including more recent time 

periods in our analysis, providing an expanded view of the trend in trade 

execution cost and market quality variables since 2000. However, to the 

extent that the influence of other factors introduced by expanding the 

sample window outweighed any influence of decimalization on trade cost 

and market quality measures, our results should be interpreted with 

caution. 

Our sample period spanned February 2000 through November 2004 (table 

14). The predecimalization period included February 1, 2000, through 

January 19, 2001, and the postdecimalization period included April 23, 2001, 

through November 5, 2004, excluding September 2001 (due to the effects of 

the September 11 terrorist attacks). We selected one week from each 

month, allowing for monthly five-trading day comparisons that avoided 

holidays and options expiration days as well controlling for seasonality 

issues.7

 Our predecimalization period consisted of a 1-week sample from 

each of the 12 months and our postdecimalization period consisted of 

twelve 1-week sample periods excerpted from April 2001 through 

November 2004, excluding the month of September 2001.

7

Despite the two “event dates” for the NYSE and NASDAQ, our analysis incorporated 

calendar-period comparisons rather than event-time comparisons (for example, 1 month 

following decimalization on the NYSE compared with 1 month following decimalization on 

NASDAQ). We believed that it was reasonable to assume that the lag time between full 

decimalization on the NYSE and NASDAQ would not lead to any sizeable learning 

discrepancies between the markets since NASDAQ market participants were able to 

observe NYSE activity over this period.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 81 GAO-05-535 Securities Markets

Table 14: Pre- and Postdecimalization Sample Weeks

Source: GAO.

Note: The sample weeks selected avoided holidays and partial trading days either before or after 

holidays, as well as other noted trading stoppages, options expiration days (the third Friday of each 

month), and end of quarter days, all of which may lead to unusual trading activity.

Day of the week

Period Year Month Monday Tuesday Wednesday Thursday Friday

Predecimals 2000 February 7 8 9 10 11

2000 March 20 21 22 23 24

2000 April 10 11 12 13 14

2000 May 8 9 10 11 12

2000 June 19 20 21 22 23

2000 July 10 11 12 13 14

2000 August 21 22 23 24 25

2000 September 18 19 20 21 22

2000 October 23 24 25 26 27

2000 November 6 7 8 9 10

2000 December 18 19 20 21 22

2001 January 22 23 24 25 26

Postdecimals 2001 April 23 24 25 26 27

2001 August 20 21 22 23 24

2001 December 10 11 12 13 14

2002 January 7 8 9 10 11

2002 May 6 7 8 9 10

2002 September 23 24 25 26 27

2003 February 24 25 26 27 28

2003 June 2 3 4 5 6

2003 October 20 21 22 23 24

2004 March 8 9 10 11 12

2004 July 19 20 21 22 23

2004 November 1 2 3 4 5
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 82 GAO-05-535 Securities Markets

Generating the Sample of Stocks 

for Our Analysis

Generally following the methods used by other researchers, we generated 

our list by including only common shares of domestic companies that were 

active over our period of interest and that were not part of decimalization 

pilot programs in effect before January 29, 2001. Specifically, we excluded 

preferred stocks, warrants, lower class common shares (for example, Class 

B and Class C shares), as well as NASDAQ stocks with five-letter symbols 

not representing Class A shares.8

 We then eliminated from consideration 

stocks with average share prices that were below $5 or above $150 over the 

February 2000 through December 2000 period. We also eliminated stocks 

for which there were no recorded trades on 10 percent or more of the 

trading days, to ensure sufficient data, leaving us with 981 NYSE-listed and 

1,361 NASDAQ stocks in the potential sample universe. Our stock samples 

for the analysis ultimately consisted of 300 matched pairs of NYSE-listed 

and NASDAQ stocks.

8

NASDAQ stock symbols are four to five letters in length. A fifth letter in a NASDAQ stock 

symbol indicates, among other things, share class or unusual circumstances such as 

bankruptcy or delayed SEC filing.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 83 GAO-05-535 Securities Markets

Generating the Matched Pairs The NYSE-listed and NASDAQ stocks were matched on variables that are 

generally thought to help explain interstock differences in spreads. To the 

extent that our matching samples of NYSE-listed and NASDAQ stocks had 

similar attributes, any differences in spreads between the groups should 

have been due to reasons other than these attributes. The attributes we 

considered were (1) share price, (2) share price volatility, (3) number of 

trades, and (4) trade size.9

 For the matching procedure, daily data from 

February 2000 through December 2000 were used and averages were taken 

over this sample period. Share price was measured by the mean value of 

the daily closing price and volatility by the average of the logarithm of the 

high-low intraday price range. The number of trades was measured by the 

average daily number of trades, and average trade size was measured as the 

average daily trading volume.10 These factors have different measurement 

units, implying that they could not be directly converted into a single 

measure of similarity. To develop a combined measure of similarity we first 

had to standardize the measures of all factors so that their average values 

and differences in their averages were measured on comparable scales. 

Once standardized measures of averages and differences were developed, 

we were able to sum the four measurements into a total measure of 

similarity and identify matched pairs of stocks. Comparability was assured 

because all averages and differences were divided by the standard 

deviation of the measure of each factor on the NYSE. 

Our matching algorithm was similar to those described in Chung et al. 

(2004) and Van Ness et al. (2001). To obtain a matching sample of NYSE and 

NASDAQ stocks, we first calculated the following combined measure of 

similarity—the composite match score (CMS)—for each NYSE stock using 

our entire sample of NASDAQ stocks. The CMS is defined as

9

While Bessimbinder (2003) used only market capitalization as the matching criterion, 

Chung et al. (2004) used five stock attributes—share price, number of trades, trade size, 

return volatility, and market capitalization. In the absence of market capitalization data, we 

followed Van Ness et al. (2001) and used four variables. Researchers have generally found 

that overall results are similar regardless of the matching variables used.

10The reported number of trades on NYSE is not directly comparable to that reported on 

NASDAQ due to interdealer trading on NASDAQ. NASDAQ volume has been estimated to be 

exaggerated by 30 percent to 50 percent relative to NYSE volume. As with Chung et al. 

(2004), we counterbalance the discrepancy by including trades in NYSE-listed stocks that 

occur outside of the NYSE, reflecting activity at regional exchanges and elsewhere, rather 

that incorporating an “inflation factor.”
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 84 GAO-05-535 Securities Markets

,

in which the superscripts and refer to NYSE and NASDAQ, 

respectively, and and represent one of the four stock attributes 

for each—in which denotes the NYSE stock and denotes the NASDAQ 

stock being matched. In the matching algorithm, each of the attributes 

was weighted equally. Unlike the matching algorithms in the two 

aforementioned papers, we divided each stock attribute difference by the 

sample standard deviation of that attribute for the entire NYSE sample—

denoted as —in order to create unit less measures that were 

normalized relative to the overall NYSE attributes. 

Ultimately, for each NYSE stock we selected the NASDAQ stock with the 

smallest CMS. Chung et al. (2004) used a sequential matching algorithm as 

is common in the literature. To start, they considered an NYSE stock and 

computed its CMS with all NASDAQ stocks; they matched that NYSE stock 

to the NASDAQ stock with the lowest CMS. Then they considered the next 

NYSE stock, but the NASDAQ stock that matched the prior NYSE stock 

was no longer considered among the possible universe of matches for this 

or any subsequent NYSE stock. The outcome of this type of algorithm is 

path dependent—the order in which the NYSE stocks are taken influences 

the ultimate list of unique matches. We employed another method that 

avoided this path dependence—ensuring an optimal match for each 

stock—but also allowed for the possibility of duplicate, nonunique 

NASDAQ matches. For the 981 NYSE-listed stocks, there were 293

 

2

4

1 N N 2

NT NT

ij

Y Y

YY YY

CMS = σ σ

       − + =      ⋅       

∑ ij ij

v v

vv vv

v

N T

Yv

N YvT

i j

v

σYv

N
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 85 GAO-05-535 Securities Markets

NASDAQ stocks that provided the best matches.11 We chose the 300 best 

CMS matched pairs, which consisted of 300 NYSE and 186 unique NASDAQ 

stocks.12 Of these 186 NASDAQ stocks, 114 were best matches for one 

NYSE-listed stock, 45 were best matches for two NYSE-listed stocks, 19 

were best matches for three NYSE-listed stocks, 5 were best matches for 

four NYSE-listed stocks, 1 was a best match for five NYSE-listed stocks, 

and 2 were best matches for seven NYSE-listed stocks. In the subsequent 

analysis, each NASDAQ stock was weighted according to the number of 

best matches it yielded. For example, if a NASDAQ stock provided the best 

match for two NYSE-listed stocks, it was counted twice in the overall 

averages for NASDAQ. 

Characteristics of Our Sample 

Stocks

The pairings resulting from the CMS minimization algorithm were well 

matched. The average share price for the 300 NYSE-listed (NASDAQ 

matching) stocks was $19.66 ($19.56), the average daily volume was 

132,404 (127,107), the average number of trades per day was 121 (125), and 

the measure of daily volatility was 0.018 (0.018). In terms of average share 

price, the 300 matching-pair stocks were fairly representative of the full 

sample of matching stocks, as well as of the potential sample universe of 

stocks, as illustrated in table 15 and figure 15. However, the resulting 

matched-pairs sample tended to have more lower-priced stocks.

11Of the 293 NASDAQ matches, 127 were best matches for one NYSE-listed stock, 64 were 

best matches for two NYSE-listed stocks, 28 were best matches for three, 13 were best 

matches for four, 16 were best matches for five, 7 were best matches for six, 11 were best 

matches for seven, 1 was a best match for eight, 6 were best matches for nine, 3 were best 

matches for ten, and 17 were best matches for 11 to 26 NYSE stocks. 

12Relative to the marginal cost in terms of computing resources and analysis time, the 

marginal benefit of increasing the number of matched pairs was limited, as the top 400 (500) 

matched pairs consisted of 400 (500) NYSE stocks and 215 (235) NASDAQ matching stocks.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 86 GAO-05-535 Securities Markets

Table 15: Price Characteristics of NYSE-Listed and NASDAQ Stocks

Source: GAO analysis of TAQ data.

Note: Share price was measured as the average daily closing price from February 2000 through 

December 2000. Of the 186 NASDAQ stocks, 114 were best matches for one NYSE-listed stock and 

the remainder were best matches for multiple NYSE-listed stocks. In the analysis, each NASDAQ stock 

was weighted according to the number of best matches it yielded. 

NYSE NASDAQ

Potential universe of stocks

 Number

 Average (median) share price

 Percent priced below $25

 Percent priced below $50

981

$29.20 (24.07)

52%

87%

1,361

$25.27 (17.54)

65%

89%

Full sample of matching stocks

 Number

 Average (median) share price

 Percent priced below $25

 Percent priced below $50

981

$29.20 (24.07)

52%

87%

263

$26.60 (22.27)

52%

91%

300 matched-pair sample of stocks

 Number

 Average (median) share price

 Percent priced below $25

 Percent priced below $50

300

$19.66 (16.55)

75%

96%

186 unique

$19.56 (15.94)

74%

98%
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 87 GAO-05-535 Securities Markets

Figure 15: Distribution of Average Daily Closing Prices for Full Sample of Matching Stocks and 300 Matched-Pairs Sample

Note: Share price was measured as the average daily closing price from February 2000 through 

December 2000. There were 981 NYSE-listed stocks and 293 matching NASDAQ stocks in the all 

matching stocks sample. There were 300 NYSE-listed and 300 (186 unique) matching NASDAQ 

stocks in the matched pairs sample. Each NASDAQ stock was weighted according to the number of 

best matches it yielded.

In terms of average daily trading volume, the matched-pairs sample 

underrepresented higher-volume stocks, which likely biased our results 

toward reporting larger spreads (see table 16 and fig. 16).

0

50

100

150

200

Nasdaq -- 300 Matched Pairs (186 Unique)

NYSE -- 300 Matched Pairs

Nasdaq -- All Sample Matches (293 Unique)

NYSE -- All Sample Stocks (981)

>90 >95

to

95

>85

to

90

>80

to

85

>75

to

80

>70

to

75

>65

to

70

>60

to

65

>55

to

60

>50

to

55

>45

to

50

>40

to

45

>35

to

40

>30

to

35

>25

to

30

>20

to

25

>15

to

20

>10

to

15

>5

to

10

>0

to

5

Frequency

Price range

Source: GAO analysis of TAQ data.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 88 GAO-05-535 Securities Markets

Table 16: Volume Characteristics of NYSE-Listed and NASDAQ Stocks

Source: GAO analysis of TAQ data.

Note: Volume was measured as the average daily trading volume from February 2000 through 

December 2000. Of the 186 NASDAQ stocks, 114 were best matches for one NYSE-listed stock and 

the remainder were best matches for multiple NYSE-listed stocks. In the analysis, each NASDAQ stock 

was weighted according to the number of best matches it yielded. 

NYSE NASDAQ

Potential universe of stocks

 Number

 Average (median) daily volume

 Percent below 150,000 shares

 Percent below 500,000 shares

981

689,811 (190,070)

45%

69%

1,361

607,687 (125,627)

54%

82%

Full sample of matching stocks

 Number

 Average (median) daily volume

 Percent below 150,000 shares

 Percent below 500,000 shares

981

689,811 (190,070)

45%

69%

263

511,980 (185,787)

48%

75%

300 matched-pair sample of stocks

 Number

 Average (median) daily volume

 Percent below 150,000 shares

 Percent below 500,000 shares

300

132,404 (74,188)

73%

97%

186

127,107 (73,204)

75%

97%
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 89 GAO-05-535 Securities Markets

Figure 16: Distribution of Average Daily Trading Volume for Full Sample of Matching Stocks and 300 Matched-Pairs Sample

Note: Volume was measured as the average daily trading volume from February 2000 through 

December 2000. There were 981 NYSE-listed stocks and 293 matching NASDAQ stocks in the all 

matching stocks sample. There were 300 NYSE-listed and 300 (186 unique) matching NASDAQ 

stocks in the matched pairs sample. Each NASDAQ stock was weighted according to the number of 

best matches it yielded.

Filtering and Manipulation of 

Trade and Quote Data

Once we had defined our stock sample, to undertake the subsequent 

analysis we first had to filter the trades and quotes data for each sample 

stock, which involved discarding records with TAQ-labeled errors (such as 

canceled trade records and quote records identified with trading halts), 

identifying and removing other potentially erroneous quotation and trade 

records (such as stale quotes or trade or quote prices that appeared 

aberrant), as well as simply confining the data to records between 9:30 a.m. 

and 4 p.m. We also had to determine the national best bid and offer quotes 

in effect at any given moment from all quoting market venues—the NBBO 

quotation. In general, for a given stock the best bid (offer) represents the 

>1,000

1,000

100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

10

20

40

60

80

0

50

100

150

200

Nasdaq -- 300 Matched Pairs (186 Unique)

NYSE -- 300 Matched Pairs

Nasdaq -- All Sample Matches (293 Unique)

NYSE -- All Sample Stocks (981)

Frequency

Volume range (in thousands)

Source: GAO analysis of TAQ data.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 90 GAO-05-535 Securities Markets

highest (lowest) price available from all market venues providing quotes to 

sellers (buyers) of the stock. 

The NBBO quotes data for a given stock were used to compute quoted bidask spreads, quote sizes, and share prices, as well as intraday price 

volatility for that stock on a daily basis. They were also used independently 

to document any quote clustering activity in that stock. The trades’ data for 

a particular stock were used to analyze daily price ranges and trade 

execution price clustering. For each stock, the trades and NBBO quotes 

data were used to compute effective bid-ask spreads, which rely on both 

quotes and trades data.

The TAQ Consolidated Quotes (CQ) file covers most activity in major U.S. 

market centers but does not include foreign market centers. A record in the 

CQ file represents a quote update originating in one of the included market 

centers: Amex, the Boston Stock Exchange, the Chicago Stock Exchange, 

electronic communication networks (ECN) and alternative trading systems 

(ATS), NASDAQ, the National Stock Exchange, NYSE, the Pacific Stock 

Exchange, and the Philadelphia Stock Exchange.13 It does not per se 

establish a comprehensive marketwide NBBO quote, however. A quote 

update consists of a bid price and the number of shares for which that price 

is valid and an offer price and the number of shares for which that price is 

valid. In general, a quote update reflects quote additions or cancellations. 

The record generally establishes the best bid and offer prevailing in a given 

market center. Normally, a quote from a market center is regarded as firm 

and valid until it is superseded by a new quote from that center—that is, a 

quote update from a market center supersedes that market center’s 

previous quotes and establishes its latest, binding quotes. 

Specifying the NBBO involved determining the best bid and offer quotes 

available—at a particular instant, the most recent valid bids and offers 

posted by all market centers were compared and the highest bid and the 

13In the TAQ CQ file, NASDAQ dealers and ECNs are collectively classified under “T” as the 

source market for quotations for NYSE-listed issues. The market maker identification 

(MMID) data field provides an additional classification layer among NASDAQ dealers and 

ECNs. For example, “TRIM” denotes “Trimark,” a NASDAQ dealer, while “BRUT” denotes 

the BRUT ECN. “CAES” is the acronym for “Computer Assisted Execution System,” which is 

a NASDAQ system that allows its members to quote NYSE-listed stocks. The National 

Securities Clearing Corporation provides a listing of NASDAQ market makers and their 

MMIDs in the Member Directory at www.nscc.com. The Boston Stock Exchange, the 

Chicago Stock Exchange, the National Stock Exchange, the Pacific Stock Exchange, and the 

Philadelphia Stock Exchange are regional exchanges.
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 91 GAO-05-535 Securities Markets

lowest offer were selected as the NBBO quotes. The national best bid 

(NBB) and national best offer (NBO) are not necessarily from the same 

market center or posted concurrently, and the bid and offer sizes can be 

different. Bessimbinder (2003) outlined a general method for determining 

the NBBO. First, the best bid and offer in effect for NYSE-listed stocks 

among individual NASDAQ dealers (as indicated by the MMID data field) 

was assessed and designated as the NASDAQ bid and offer. Then, the best 

bid and offer in effect across the NYSE, the five regional exchanges, and 

NASDAQ were determined and designated as the NBBO quotations for 

NYSE-listed stocks. For NASDAQ stocks, quote records from NASDAQ 

market makers reflect the best bid and offer across these participants 

(collectively classified as “T” in the TAQ data). Competing quotes are 

issued from other markets (e.g., the Pacific Stock Exchange) as well as 

NASDAQ’s SuperMontage Automated Display Facility, which reflects the 

quotes from most ECNs. We required additional details in constructing the 

NBBO, since quote records from competing market makers and market 

centers can have concurrent time stamps and there can be multiple quotes 

from the same market center recorded with the same time stamp. 

Moreover, identical bid or offer prices can be quoted by multiple market 

makers. To address these complications, we relied on language offered in 

SEC’s Regulation NMS proposal, which defined the NBBO by ranking all 

such identical bids or offers first by size (giving the highest ranking to the 

bid or offer associated with the largest size) and then by time (giving the 

highest ranking to the bid or offer received first in time). In our algorithm, 

the NBB (NBO) is located by comparing the existing bids (offers) from all 

venues. The NBBO is updated with each instance of a change in the NBB or 

NBO.

General Analysis Techniques Each NBBO quotation was weighted by its duration (i.e., the time for which 

it was effective) and used to compute a sample week time-weighted 

average NBBO quotation for the relevant market, which was reported on a 

volume-weighted (relative to total sample market trading volume) basis. 

Ultimately, these averages were compared across markets and across pre 

and postdecimalization periods. The same general techniques were used in 

computing effective spreads, which were determined by comparing trade 

executions with NBBO quotations. For analysis of trades data (e.g., in 

computing price ranges), a simple average over all stocks in a given market 

was computed. In analyzing volatility, intraday returns were measured for 

each stock based on continuously compounded percentage changes in 

quotation midpoints, which were recorded between 10 a.m. and 4 p.m. The 

standard deviation of the intraday returns was then computed for each 

stock, and the cross-sectional median across all stocks was taken. In 
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 92 GAO-05-535 Securities Markets

assessing clustering, the frequencies of trades and quotes at pennies, 

nickels, dimes, and quarters were determined for each market on an 

aggregate basis.

In reporting any differences between the pre- and postdecimalization 

sample periods in the trade execution cost and market quality measures 

that we analyzed, statistical significance was assessed based on crosssectional variation in the stock-specific means. With the exception of 

volatility measures, statistical significance was assessed using a standard ttest for equality of means. Since average volatility measures do not 

conform well to the t-distribution, median volatility was reported for each 

market and the Wilcoxon rank sum test used to assess equality.

Measuring Trade Execution 

Costs and Other Market Quality 

Components with TAQ Data

TAQ data allowed us to study variables that are based on trades and quotes 

but did not allow us to study any specific effects on or make any inferences 

regarding orders or institutional trading costs. This is an important 

limitation because the transition to decimal pricing may have impacted 

retail traders, whose generally smaller orders tend to be executed in a 

single trade, differently than institutional traders. Use of TAQ data 

implicitly assumes that each trade record reflects a unique order that is 

filled, so our analysis failed to address any impact of a change in how 

orders are filled and the costs associated with this. We reported the preand postdecimalization behavior of quoted bid-ask spreads and effective 

spreads. Beyond measures of trade execution cost, market quality is 

multidimensional. Possible adverse effects of decimalization on market 

quality included increased trade execution costs for large traders, 

increased commissions to offset smaller bid-ask spreads, slower order 

handling and trade executions, decreased market depth, and increased 

price volatility. The TAQ data allowed measurement of quotation sizes and 

price volatility, which we reported. We also analyzed quote clustering, 

which reflects any unusual frequency with which prices tend to bunch at 

multiples of nickels, for example. We generally presented our results on an 

average basis for a given market in the pre- and postdecimalization periods; 

we also reported the results for sample stocks grouped by average daily 

trading volume.

Calculating Quoted Bid-Ask 

Spreads as a Simple Measure of 

Trading Costs

Average pre- and postdecimalization bid-ask spreads were calculated in 

cents per share and basis points (that is, the spread in cents relative to the 

NBBO midpoint) using the NBBO quote prices. The average spread was 

obtained in the following way. First, each NBBO quote for a given stock 

was weighted by the elapsed time before it was updated—its duration—on 

a given day of a sample week relative to the total duration of all NBBO 
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 93 GAO-05-535 Securities Markets

quotes for that stock in that sample week. Next, the duration-weighted 

average over the five trading days in that sample period for that stock was 

used to compute the average across all stocks in a given market for that 

week; ultimately, a volume-weighted average was computed. For the 

twelve-sample week period, a volume-weighted average was also 

computed.

Calculating Effective Bid-Ask 

Spreads as a Better Measure of 

Trading Costs

The effective bid-ask spread—how close the execution price of a trade is 

relative to the quote midpoint—is generally considered to be the most 

relevant measure of trade execution cost, as it allows measurement of 

trades that execute at prices not equal to the bid or ask. In keeping with 

standard practice, we measured the effective spread for a trade as twice 

the absolute difference between the price at which a trade was executed 

and the midpoint of the contemporaneous NBBO quote. Suppose for 

example that the NBB is $20.00 and the NBO is $20.10, so that the NBBO 

midpoint is $20.05. If a trade executes at a price of $20.05 then the effective 

spread is zero because the trade executed at the midpoint of the spread—

the buyer of the stock paid $0.05 per share less than the ask price, while the 

seller received $0.05 per share more than the bid price. If a trade executes 

at $20.02 with the same NBBO prices, the effective spread is $0.06—the 

buyer of the stock paid $0.08 per share less than the ask price, while the 

seller received $0.02 per share more than the bid price. Effective spreads 

were computed in cents per share and in basis points.

Measuring Quotation Sizes Smaller quote sizes could reflect a decrease in liquidity supply, which in 

turn could be associated with increased volatility. The size of each NBBO 

quote was weighted by its duration and used to compute a volumeweighted average over each sample week as well as across all sample 

weeks. 

Measuring Intraday Return 

Volatility

A reduction in the tick size could lead to a decline in liquidity supply, which 

in turn could create more volatile prices. Intraday returns were measured 

for each stock based on continuously compounded percentage changes in 

quotation midpoints, which were recorded on an hourly basis between 10 

a.m. and 4 p.m. The continuously compounded return over 6 hours, from 10 

a.m. to 4 p.m., was also computed. The standard deviation (a measure of 

dispersion around the average) of the intraday returns was then computed 

for each stock, and the cross-sectional median (the middle of the 

distribution) was taken over all stocks in a given market. 
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 94 GAO-05-535 Securities Markets

Measuring Daily Price Range As another measure of price volatility, we also considered how a stock’s 

daily price range (i.e., the highest and lowest prices at which trades were 

executed) may have changed following the implementation of decimal 

pricing, as the claim has been made that prices have been moving to a 

greater degree during the day after decimalization. We computed the equalweighted average of each stock’s daily price range and then computed the 

average over all stocks in a given market. To account for potentially varying 

price levels across the pre and postdecimalization sample periods, we 

computed the price range in both cents per share as well as relative to the 

midpoint of the first NBBO quote for each day.

Measuring Trade and Quote 

Clustering

Decimalization provides a natural experiment to test whether market 

participants prefer to trade or quote at certain prices when their choices 

are unconstrained by regulation. Theory suggests that if price discovery is 

uniform, realized trades should not cluster at particular prices. The 

existence of price clustering following decimalization could suggest a 

fundamental psychological bias by investors for round numbers and that 

there may be only minor differences between the transactions prices that 

would prevail under a tick size of 5 cents relative to those observed under 

decimal pricing.14 For quotes, according to competing hypotheses in the 

literature, clustering may be due to dealer collusion, or it may simply be a 

natural phenomenon—as protection against informed traders, as 

compensation for holding inventory, or to minimize negotiation costs.15 

For our analysis, we computed the frequency of trade executions and 

quotes across the range of price points, but we did not attempt to 

determine the causes of any clustering.

Efforts to Assess Reliability of 

TAQ Data 

Consistent with generally accepted government auditing standards, we 

assessed the reliability of computer-processed data that support our 

findings. To assess the reliability of TAQ data, we performed a variety of 

error checks on data from a random sample of stocks and dates. This 

involved comparing aggregated intraday data with summary daily data, 

scanning for outliers and missing data. In addition, since the TAQ database 

is in widespread use by researchers and has been for several years, we 

were able to employ additional methods for discarding potentially 

erroneous data records following widely accepted methods (e.g., we 

14This was explored in a working paper, D. Ikenberry and J. Weston, 2003, “Clustering in U.S. 

Stock Prices after Decimalization.”

15This was explored in Chung et al. (2004).
Appendix II

Methodology for GAO Analysis of Trade and 

Quotes Data 

Page 95 GAO-05-535 Securities Markets

discarded quotation information in which a price or size was reported as 

negative). We assessed the reliability of our analysis of the TAQ data by 

performing several executions of the programs using identical and slight 

modifications of the program coding. Program logs were also generated 

and reviewed for errors.
Page 96 GAO-05-535 Securities Markets

Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented ApendxiI

As discussed in the body of this report, institutional investors’ trading costs 

are commonly measured in cents per share and basis points (bps). Cents 

per share is an absolute measure of cost based on executing a single share. 

Basis points—measured in hundredths of a percentage point—show the 

absolute costs relative to the stock’s average share price. For example, for 

a stock with a share price of $20, a transaction cost of $.05 would be 0.25 

percent or 25 bps. Costs reported in terms of basis points can show 

changes resulting solely from changes in the level of stock prices—if the 

price of the $20 stock falls to $18, the $.05 transaction cost would now be 

almost 0.28 percent or 28 bps. However, many organizations track costs 

using basis points, and in this appendix we present the results of our 

institutional trading cost analysis in basis points.

Analysis of the multiple sources of data that we collected generally 

indicated that institutional investors’ trading costs had declined since 

decimal prices were implemented. Specifically, NYSE converted to decimal 

pricing on January 29, 2001, and NASDAQ completed its conversion on 

April 9, 2001. We obtained data from three leading firms that collect and 

analyze information about institutional investors’ trading costs. These 

trade analytics firms (Abel/Noser, Elkins/McSherry, and Plexus Group) 

obtain trade data directly from institutional investors and brokerage firms 

and calculate trading costs, including market impact costs (the extent to 

which the security changes in price after the investor begins trading), 

typically for the purpose of helping investors and traders limit costs of 

trading.1

 These firms also aggregate client data so as to approximate total 

average trading costs for all institutional investors. Generally, the client 

base represented in aggregate trade cost data is sufficiently broad based 

that the firm’s aggregate cost data can be used to make generalizations 

about the institutional investor industry. 

Although utilizing different methodologies, the data from the firms that 

analyze institutional investor trading costs uniformly showed that costs 

had declined since decimal pricing was implemented. Our analysis of data 

from the Plexus Group showed that costs declined on both NYSE and 

NASDAQ during the 2 year period after these markets converted to decimal 

pricing. Plexus Group uses a methodology that analyzes various 

components of institutional investor trading costs, including the market 

impact of investors’ trading.2

 Total trading costs declined by about 32 

1

ITG, another trade analytics firm, did not begin to measure institutional investors’ trading 

costs until January 2003, after the implementation of decimal pricing and 1-cent ticks.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 97 GAO-05-535 Securities Markets

percent for NYSE stocks, falling from about 82 bps to 56 bps (fig. 17). For 

NASDAQ stocks, the decline was about 25 percent, from about 102 bps to 

about 77 bps. As can be seen in figure 17, the decline in trading costs began 

before both markets implemented decimal pricing, which indicates that 

other causes, such as the 3-year declining stock market, in addition to 

decimal pricing, were also affecting institutional investors’ trading during 

this period. An official from a trade analytics firm told us that the spike in 

costs that preceded the decimalization of NASDAQ stocks correlated to the 

pricing bubble that technology sector stocks experienced in the late 1990s 

and early 2000s. An official from another trade analytics firm explained that 

trading costs increased during this time because when some stocks’ prices 

would begin to rise, other investors—called momentum investors—would 

begin making purchases and cause prices for these stocks to move up even 

faster. As a result, other investors faced greater than usual market impact 

costs when also trading these stocks. In general, trading during periods 

when stock prices are either rapidly rising or falling can make trading very 

costly.

2

To measure market impact costs, the Plexus Group compares a proprietary benchmark 

stock price to the average price an investor receives. The Plexus Group benchmark 

attempts to show the price at which the order for a particular stock should be executed. The 

firm calculates this expected price using trade data of its clients for the two quarters 

preceding the date of the trade under study, and takes into account variables such as trade 

size, liquidity, and the direction of stock price movement.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 98 GAO-05-535 Securities Markets

Figure 17: Total Trading Costs from a Trade Analytics Firm for NYSE and NASDAQ Stocks, 1999–2004 (basis points)

Note: Data are reported quarterly. After a phase-in period, all NYSE stocks were trading with decimal 

prices by January 29, 2001, and all NASDAQ stocks were converted by April 9, 2001.

According to our analysis of the Plexus Group data, all of the decline in 

trading costs for NYSE stocks and NASDAQ stocks were caused by 

decreases in the costs resulting from market impact and delay for orders.3

 

Together, the reduction in these two components accounted for 29.1 bps or 

all of total decline, with delay costs representing 20.6 bps (or about 71 

percent) in the approximately 2 years following the implementation of 

30

60

90

120

150

Basis points

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2

1999 2000 2001 2002 2003

Year and quarter

Decimalized

Source: GAO analysis of Plexus Group data.

Nasdaq

NYSE

3

Delay costs are a type of market impact cost that occur between the time institutional 

investors’ portfolio managers direct their traders to buy or sell stock and the moment these 

orders are released to brokers. The amount that the stock’s price changes during this period 

is the cost of delaying the order. An order may be delayed for a number of reasons—for 

instance, because it could affect prices in the market too much. Plexus Group, The Official 

Icebergs of Transaction Costs, Commentary #54, January 1998.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 99 GAO-05-535 Securities Markets

decimal pricing and 1-cent ticks on the NYSE. However, commissions 

increased 3 bps, which led total trading costs to decline 26.1 bps (fig. 18).

Figure 18: Trading Cost Components from One Trade Analytics Firm for NYSE and 

NASDAQ, 2001–2003 (basis points)

Note: Data are from first quarter 2001 to second quarter 2003 for NYSE and second quarter 2001 to 

second quarter 2003 for NASDAQ.

Figure 18 also shows that market impact and delay costs account for all 

declines to total NASDAQ trading costs. For example, market impact and 

delay costs declined 40.9 bps between the second quarter of 2001 and the 

second quarter of 2003. However, overall trading costs declined by only 

24.4 bps, which is 16.5 bps less than declines in market impact and delay 

costs. According to Plexus Group data, overall costs would have declined 

further if not for increases to commission costs for NASDAQ stocks, the 

0

20

40

60

80

100

120

0

20

40

60

80

100

120

Delay

Market impact

Commission

NYSE Nasdaq

2001

Year Year

2003 2001 2003

Basis points Basis points

Source: GAO analysis of Plexus Group data.

43.6

23.1

11.7

23.0

18.6

14.7

40.0

57.9

30.9

26.1

20.2

3.7
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 100 GAO-05-535 Securities Markets

only cost component that increased after NASDAQ converted to decimal 

pricing and 1-cent ticks. As shown in figure 18, commissions that market 

intermediaries charged for trading NASDAQ stocks increased 16.5 bps 

from the second quarter of 2001 to the second quarter of 2003. Industry 

representatives told us these increases reflect the evolution of the 

NASDAQ brokerage industry from trading as principals, in which the 

compensation earned by market makers was embedded in the final trade 

price, to that of an agency brokerage model, in which broker-dealers charge 

explicit commissions to represent customer orders in the marketplace.4

Analysis of data from the other two trade analytics firms from which we 

obtained data, Elkins/McSherry and Abel/Noser, also indicated that 

institutional investor trading costs varied but declined following the 

decimalization of U.S. stock markets in 2001. Because these two firms’ 

methodologies do not include measures of delay, which the Plexus Group 

data shows can be significant, analysis of data from these two firms results 

in trading cost declines of a lower magnitude than those indicated by the 

Plexus Group data analysis. Nevertheless, the data we analyzed from 

Elkins/McSherry showed total costs for NYSE stocks declined about 20 

percent between the first quarter of 2001 and year-end 2004 from about 29 

bps to about 24 bps. Analysis of Abel/Noser data indicated that total trading 

costs for NYSE stocks declined 25 percent from 20 bps to 15 bps between 

year-end 2000 and 2004 (fig. 19). 

4

For example, NASDAQ market makers previously could earn revenue trading as principals 

by buying shares at the bid price from investors and selling those shares to other investors 

at the higher ask price, thus earning the difference or spread amount as compensation.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 101 GAO-05-535 Securities Markets

Figure 19: Total Trading Costs from Two Trade Analytics Firms for NYSE Stocks, 2001–2004 (basis points)

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and the fourth quarter of 2004; 

Abel/Noser data are year-end totals for 1998–2004.

Our analysis of these firms’ data also indicated that total trading costs 

declined in basis points for NASDAQ stocks or were flat. For example, our 

analysis of the Elkins/McSherry data showed that total trading costs for 

NASDAQ stocks dropped by roughly 13 percent, from about 38 bps to 

about 32 bps between the second quarter of 2001 when that market 

decimalized to year-end 2004. Analysis of the Abel/Noser data indicated 

that total trading costs increased nearly 5 percent for NASDAQ stocks 

during that period, increasing from 21 bps to 22 bps (fig. 20). This increase 

in trading cost can possibly be explained by the approximately 50 percent 

decline in average share price over the period.

0

5

10

15

20

25

30

35

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

NYSE

decimalized

Basis points

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Abel/Noser (averaged annual data)

Elkins/McSherry (quarterly data)
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 102 GAO-05-535 Securities Markets

Figure 20: Total Trading Costs from Two Trade Analytics Firms for NASDAQ Stocks, 2001–2004 (basis points)

Note: Elkins/McSherry data are quarterly from fourth quarter of 1998 and the fourth quarter of 2004; 

Abel/Noser data are year-end totals for 1998–2004.

Similar to Plexus Group data analysis, our analysis of the Elkins/McSherry 

and Abel/Noser data also indicated that reductions to market impact costs 

accounted for a vast proportion of overall reductions for NYSE stocks (fig. 

21).5

 Analysis of the Elkins/McSherry data indicated that by declining 7.6 

bps during this period, reduced market impact accounted for 95 percent of 

total cost trading declines. The 3 bps reduction in market impact costs 

0

10

20

30

40

50

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1998 1999 2000 2001 2002 2003 2004

Year and quarter

Nasdaq

decimalized

Basis points

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.

Abel/Noser (averaged annual data)

Elkins/McSherry (quarterly data)

5

These two firms analyze market impact costs by comparing their clients’ trades to the 

volume-weighted average price (VWAP) of the particular stocks traded. The VWAP 

represents the average price at which a particular stock traded on a specific trading day and 

is calculated by weighting each trade’s price according to the proportion of shares of a 

specific stock it represents on a given day. The closer an investor’s average price is to the 

VWAP, the lower the calculated market impact costs.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 103 GAO-05-535 Securities Markets

identified in the Abel/Noser data represented the entire total trading cost 

reductions for NYSE stocks. 

Figure 21: Trading Cost Components from Two Trade Analytics Firms for NYSE 

Stocks, 2001 and 2004 (basis points)

Note: Abel/Noser does not account for exchange fees as a component of trading cost. For 

Elkins/McSherry, we obtained first quarter 2001 data and fourth quarter 2004. For Abel/Noser, we 

obtained data from the end of 2000 and 2004.

Reductions to market impact costs explain virtually the entire decline to 

total trading costs captured by the Elkins/McSherry data for NASDAQ 

stocks and all of the Abel/Noser data for NASDAQ stocks. For 

Elkins/McSherry and Abel/Noser, such costs would have produced even 

larger total declines had commissions for such stocks not increased since 

2001. Market impact costs declined 22.3 bps (about 64 percent) according 

0

5

10

15

20

25

30

0

5

10

15

20

25

30

Elkins/McSherry Abel/Noser

Basis points Basis points

Exchange fee

Market impact

Commission

2001

Year Year

2004 2001 2004

0.6

14.9

13.7

7.3

15.9

6.0

3.0

13.0 13.0

0.2

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 104 GAO-05-535 Securities Markets

to our analysis of the Elkins/McSherry data and 14 bps (about 74 percent) 

according to analysis of the Abel/Noser data (fig. 22). However, during this 

period, commissions charged on NASDAQ stock trades included in these 

firms’ data increased by 16.9 bps, marking approximately a sixfold increase 

in commissions as measured by Elkins/McSherry and by 15 bps or about a 

fifteenfold increase according to Abel/Noser.

Figure 22: Trading Cost Components from Two Trade Analytics Firms for NASDAQ 

Stocks, 2001 and 2004 (basis points)

Note: Abel/Noser does not account for exchange fees as a component of trading cost. For 

Elkins/McSherry, we obtained first quarter 2001 data and fourth quarter 2004. For Abel/Noser, we 

obtained data from the end of 2000 and 2004.

0

5

10

15

20

25

30

35

40

0

5

10

15

20

25

30

35

40

Elkins/McSherry Abel/Noser

Basis points Basis points

Exchange fee

Market impact

Commission

2001

Year Year

2004 2001 2004

0.3

35.1

2.5

12.8

19.4 19.0 5.0

16.0

1.0

0.2

Source: GAO analysis of Elkins/McSherry and Abel/Noser data.
Appendix III

Measurement of Institutional Investors’ 

Trading Costs in Basis Points Shows Decline 

since Decimal Pricing Implemented

Page 105 GAO-05-535 Securities Markets

Data from a fourth firm, ITG, which recently began measuring institutional 

trading costs, also indicates that such costs have declined. This firm began 

collecting data from its institutional clients in January 2003. Like the other 

trade analytics firms, its data is similarly broad based, representing about 

100 large institutional investors and about $2 trillion worth of U.S. stock 

trades. ITG’s measure of institutional investor trading cost is solely 

composed of market impact costs and does not include explicit costs, such 

as commissions and fees, in its calculations. Although changes in ITG’s 

client base for its trade cost analysis service prevented direct period to 

period comparisons, an ITG official told us that its institutional investor 

clients’ trading costs have been trending lower since 2003.6

6

We do not present the specific analysis of ITG’s data because the firm’s client base for its 

trade cost analysis grew significantly after it first began offering this service, including the 

addition of some larger clients with sophisticated trading operations that contributed to the 

overall decline measured by the firm.
Page 106 GAO-05-535 Securities Markets

Appendix IV

Additional Analysis Using Trade and Quotes 

Data ApendxiIV

As part of our analysis of the Trade and Quotes database, we also examined 

how quoted and effective spreads changed as a percentage of stock prices 

and also examined whether the extent to which quotes clustered on 

particular prices changed since decimal pricing began. In addition to 

measuring spreads in cents per share, spreads are also frequently measured 

in basis points, which are 1/100 of a percent. We found that spreads 

generally declined when measured in basis points similar to our analysis 

measured in cents. Reporting spreads in basis points potentially accounts 

for changes in the general price level of our sample stocks, which could 

impact our results reported in cents per share. We found that both quoted 

and effective spreads generally declined when measured relative to quote 

midpoints as they did when measured simply in cents (see tables 17 and 

18). 

Table 17: Average Quoted Spreads Before and After Decimalization, 2000–2004 (basis points)

Source: GAO analysis of TAQ data.

Note: Quoted spreads in the table represent the volume-weighted average quoted spread (i.e., stocks 

and weeks with more total trading volume have greater weight) as a percentage of the midpoint of the 

prevailing quotes over 12 sample weeks during the predecimals period (February 2000–January 2001) 

and 12 sample weeks during the postdecimals period (April 2001–November 2004) for our sample of 

stocks. Stocks were segregated by volume according to the following categories:

• High volume stocks were those in our sample of stocks with average daily trading volumes 

exceeding 500,000 shares.

• Medium volume stocks were those in our sample of stocks with average daily trading volumes 

between 100,000 and 499,999 shares.

• Low volume stocks were those in our sample of stocks with average daily trading volumes of less 

than 100,000 shares.

NYSE quoted spread NASDAQ quoted spread

Stocks by average 

daily volume of shares 

traded 

Average

spread in basis

points before

decimals

Average

spread in basis

points after

decimals Percent change

Average

spread in basis

points before

decimals

Average

spread in basis

points after

decimals Percent change

High 49.3 16.0 -68% 40.9 13.0 -68%

Medium 71.8 19.5 -73 72.4 22.4 -69

Low 125.7 32.6 -74 127.9 36.6 -71

All stocks 78.4 25.1 -68 82.0 27.2 -67
Appendix IV

Additional Analysis Using Trade and Quotes 

Data

Page 107 GAO-05-535 Securities Markets

Table 18: Average Effective Spreads Before and After Decimalization, 2000–2004 (basis points)

Source: GAO analysis of TAQ data.

Note: Effective quoted spreads (the difference between the price at which a trade is executed and the 

midpoint between the prevailing quoted bid and ask prices) in the table represent the volume-weighted 

average effective spread (i.e., stocks and weeks with more total trading volume have greater weight) as 

a percentage of the midpoint of the prevailing quotes over 12 sample weeks during the predecimals 

period (February 2000–January 2001) and 12 sample weeks during the postdecimals period (April 

2001–November 2004) for our sample of stocks. Stocks were segregated by volume according to the 

following categories:

• High volume stocks were those in our sample of stocks with average daily trading volumes 

exceeding 500,000 shares.

• Medium volume stocks were those in our sample of stocks with average daily trading volumes 

between 100,000 and 499,999 shares.

• Low volume stocks were those in our sample of stocks with average daily trading volumes of less 

than 100,000 shares.

We also analyzed the extent to which quote and trade execution prices 

cluster at particular price points, a phenomenon known as clustering. 

Clustering, particularly on multiples of nickels, dimes, and quarters, has 

been well documented by various researchers, and various reasons are 

cited to explain why all possible price points are not used with equal 

frequency. We extended the general body of research to include how 

clustering may have changed after decimalization, but we do not attempt to 

explain its causes. We generally found that prices tend to cluster on certain 

price points—especially on nickel, dime, and quarter multiples—but this 

tendency has been lessening over time. We provide examples of clustering 

in national best bid quote prices recorded for our sample of NYSE-listed 

stocks, but the same general features were found in national best offer 

quote and trade execution prices for both NYSE-listed and Nasdaq stocks. 

Figure 23 illustrates quote price clustering (using national best bid prices) 

over our entire postdecimalization sample period, which included 12 

NYSE effective spreads NASDAQ effective spreads

Stocks by average 

daily volume of shares 

traded 

Average

spread in basis

points before

decimals

Average

spread in basis

points after

decimals Percent change

Average

spread in basis

points before

decimals

Average

spread in basis

points after

decimals Percent change

High 47.8 29.4 -38% 51.1 25.3 -51%

Medium 61.8 26.5 -57 70.8 30.4 -57

Low 99.4 38.3 -61 112.4 39.0 -65

All stocks 65.3 29.4 -55 73.4 32.5 -56
Appendix IV

Additional Analysis Using Trade and Quotes 

Data

Page 108 GAO-05-535 Securities Markets

sample weeks from April 2001 through November 2004. Prices are 

observed generally clustering at nickel increments. 

Figure 23: Quote Clustering After Decimalization, 2001-2004

Notes: Quote clustering in the figure represents the frequency with which each national best bid quote 

price point, from zero cents to 99 cents, was used by all of the NYSE-listed stocks from our matchedpairs sample over the 12 sample weeks during the postdecimals period (April 2001–November 2004). 

While not included in this appendix, similar results were generally obtained for both NYSE-listed and 

Nasdaq stocks using national best offer quote and trade execution prices. 

We also analyzed how clustering may have changed over time. Using the 

same data as above, we separated the data by sample week. Our results, 

displayed in figure 24, depict a general decline in the use of price increment 

multiples of a nickel. This may suggest that traders have been adapting 

their strategies to the penny environment and are becoming increasingly 

comfortable with using various price points, which may be a result of the 

increased use of electronic trading. It may also be the case that traders are 

making use of the finer price grid to gain execution priority.

0

.5

1.0

1.5

2.0

.00 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95 .99

Percentage frequency

Decimal price quote

Source: GAO analysis of TAQ data.
Appendix IV

Additional Analysis Using Trade and Quotes 

Data

Page 109 GAO-05-535 Securities Markets

Figure 24: Quote Clustering After Decimalization, by Sample Week, 2001-2004

Notes: Quote clustering in the figure represents the frequency with which each national best bid quote 

price point, from zero cents to 99 cents, was used by all of the NYSE-listed stocks from our matchedpairs sample over the 12 sample weeks during the postdecimals period (April 2001–November 2004). 

The notation y.x0 indicates any price for which the second decimal place is a zero (e.g., $5.20); 

similarly, the notation y.x9 indicates any price for which the second decimal place is a nine (e.g., 

$5.29). While not included in this appendix, similar results were generally obtained for both NYSElisted and Nasdaq stocks using national best offer quote and trade execution prices. 

0

5

10

15

20

25

November

(1-5)

July

(19-23)

March

(8-12)

October

(20-24)

June

(2-6)

February

(24-28)

September

(23-27)

May

(6-10)

January

(7-11)

December

(10-14)

August

(20-24)

April

(21-27)

2001 2002 2003 2004

Percentage frequency

$.x9

$.x8

$.x7

$.x6

$.x5

$.x4

$.x3

$.x2

$.x1

$.x0

Decimal price with the following ending

Source: GAO analysis of TAQ data.

Sample week (year, month, and days)
Page 110 GAO-05-535 Securities Markets

Appendix V

GAO Contacts and Staff Acknowledgments ApendxiV

GAO Contacts Richard J. Hillman, (202) 512-8678

Staff 

Acknowledgments

In addition to the individuals named above, Cody Goebel, Emily Chalmers, 

Jordan Corey, Joe Hunter, Austin Kelly, Mitchell Rachlis, Carl Ramirez, 

Omyra Ramsingh, Kathryn Supinski, and Richard Vagnoni made key 

contributions to this report.
Page 111 GAO-05-535 Securities Markets

Glossary of Terms

Ask price (offer/sell price) The lowest price at which someone is willing to sell a security at a given 

time.

Basis point A basis point is equal to 1/100 of 1 percent. 

Bear market A market in which stock prices decline over a sustained period of time.

Best execution requirement The obligation of broker-dealers to seek to obtain the best terms 

reasonably available under the circumstances for customer orders.

Bid-ask spread The difference between the price at which a market maker is willing to buy 

a security (bid) and the price at which the firm is willing to sell it (ask). The 

spread narrows or widens according to the supply and demand for the 

security being traded. The spread is what the market maker retains as 

compensation (or income) for his/her effort and risk.

Bid price (buy price) The highest price at which someone is willing to buy a security at a given 

time.

Block trade Represents the purchase or sale of (1) a large quantity of stock, generally 

10,000 shares or more or (2) shares valued at $200,000 or more in total 

market value.

Broker An individual or firm who acts as an intermediary (agent) between a buyer 

and seller and who usually charges a commission.

Bull market A market in which stock prices rise over a sustained period of time.

Call option A contract granting the right to buy a fixed amount of a given security at a 

specified price within a limited period of time.

Commission A fee paid to a broker for executing a trade based on the number of shares 

traded or the dollar amount of the trade.

Dealer An individual or firm in the business of buying and selling securities for his 

or her own account (principal) through a broker or otherwise. 

Decimalization/decimal pricing The quoting and trading of securities in dollars and cents ($2.25) instead of 

fractions ($8 1/8).
Page 112 GAO-05-535 Securities Markets

Delay cost A type of market impact cost that occurs as the result of changes in the 

price of the stock being traded during the time institutional investors’ 

portfolio mangers direct their traders to buy and sell stock and the moment 

these orders are released to brokers.

Effective spread Measures the trading costs relative to the midpoint of the quoted spread at 

the time the trade occurred. It is defined as twice (to reflect the implied 

roundtrip cost) the difference between the trade price and the midpoint of 

the most recent bid and ask quotes. It reflects the price actually paid or 

received by customers. It is considered a better measure of execution costs 

than quoted spreads because orders do not always execute exactly at the 

bid or offer price.

Electronic Communication 

Network (ECN) 

An electronic trading system that automatically matches buy and sell 

orders at specified prices. It is a type of alternative trading system—an 

automated market in which orders are centralized, displayed, matched, and 

otherwise executed.

Exchange An organized marketplace (stock exchange) in which members of the 

exchange, acting both as brokers and dealers, trade securities. Through 

exchanges, brokers and dealers meet to execute orders from individual and 

institutional investors and to buy and sell securities. 

Floor-based (or auction) market Is a stock exchange (like the American Stock Exchange and the New York 

Stock Exchange) where buyers and sellers meet through an intermediary—

called a specialist. A specialist operates in a centralized location or “floor” 

and primarily matches incoming orders to buy and sell each stock. There is 

only one specialist designated for a firm or several firms who is assigned to 

oversee the market for those stocks.

Floor broker A member of an exchange who is an employee of a member firm and 

executes orders, as agent, on the floor of the exchange for their clients.

Inside spread (inside quote) The highest bid and lowest offer being quoted among all the market makers 

competing in a security. 

Intermarket linkage system An electronic trading linkage between the major exchanges (stock and 

option) and other trading centers. The system allows brokers to seek best 

execution in any market within the system.
Page 113 GAO-05-535 Securities Markets

Institutional investor An organization whose primary purpose is to invest its own assets or those 

held in trust by it for others and typically buys and sells large volumes of 

securities. Examples of such organizations include mutual funds, pension 

funds, insurance companies, and charitable organizations.

Limit order An order to buy or sell a specified number of shares of a security at or 

better than a customer-specified price. Limit orders supply additional 

liquidity to the marketplace. A limit order book is a specialist’s record of 

unexecuted limit orders.

Liquidity The ease with which the market can accommodate large volumes of 

securities trading without significant price changes. 

Listed stock The stock of a company that is listed on a securities exchange.

Market depth The numbers of shares available for trading around the best bid and ask 

prices.

Market impact The degree to which an order affects the price of a security.

Market maker A dealer that maintains a market in a given security by buying or selling 

securities at quoted prices. 

Market order An order to buy or sell a stated amount of a security at the best price 

available when the order reaches the marketplace.

NASDAQ Stock Market 

(NASDAQ)

A market for securities traded “over-the-counter” through a network of 

computers and telephones, rather than on a stock exchange floor. NASDAQ 

is an electronic communications system in which certain NASD member 

broker-dealers act as market makers by quoting prices at which they are 

willing to buy or sell securities for their own accounts or for their 

customers. NASDAQ traditionally has been a “dealer” market in which 

prices are set by the interaction of dealer quotes. 

National best bid and offer 

(NBBO)

Defined as the highest bid and lowest ask across all U.S. markets providing 

quotes for an individual stock.

Order Handling Rules SEC rules that require (1) the display of customer limit orders that improve 

certain over-the-counter (OTC) market makers’ and specialists’ quotes or 

add to the size associated with such quotes (Rule 11Ac1-4 (Display Rule)); 
Page 114 GAO-05-535 Securities Markets

(2) OTC market makers and specialists who place priced orders with ECNs 

to reflect those orders in their published quotes (Quote Rule); and (3) OTC 

market makers and specialists that account for more than 1 percent of the 

volume in any listed security to publish their quotations for that security 

(Mandatory Quote Rule).

Opportunity cost The cost from delaying execution to lessen market impact, or not be able to 

make the execution at all, or abandoning part of it because the market has 

turned against the strategy.

Price improvement Occurs when an order is executed at better than the quoted price.

Put option A contract granting the right to sell a fixed amount of a given stock at a 

specified price within a limited period of time.

Quote The highest bid to buy and the lowest offer to sell any stock at a given time.

Quote flickering Where a given price quote is only visible for a brief moment on the display 

screen. 

Quoted spread Measures the cost of executing a simultaneous buy and sell order at the 

quoted prices. It is the simplest measure of trade execution cost (or trading 

cost). 

Retail investor One who trades securities for himself/herself or who gives money to any 

institution, such as a mutual fund, to invest for himself/herself.

Securities and Exchange 

Commission

The federal regulatory agency created by the Securities Exchange Act of 

1934 that is responsible for ensuring investor protection and market 

integrity in the U.S. securities markets.

Specialists Members of an exchange who handle transactions on the trading floor for 

the stocks for which they are registered and who have the responsibility to 

maintain an orderly market in these stocks. They do this by buying or 

selling a stock on their own accounts when there is a temporary disparity 

between supply and demand for the stock.

Stepping ahead/penny jumping The practice of improving the best price by a penny or less in an attempt to 

gain execution priority.
Page 115 GAO-05-535 Securities Markets

Stock A financial instrument that signifies an ownership position in a company.

Tick size (or minimum price 

increment)

The smallest price difference by which a stock price can change (up or 

down). 

Trade-through The execution of a customer order in a market at a price that is inferior to a 

price displayed (or available) in another market.

Trading cost The cost for executing the trade (brokerage commission, fees, market 

impact).

Transparency The degree to which trade and quotation information (price and volume) is 

available to the public on a current basis.

Volatility A measure of the fluctuation in the market price of a security.

Volume The number of shares traded in a security or an entire market during a 

given period—generally on a daily basis. It is a measure of liquidity in a 

market.

Volume weighted average price 

(VWAP)

A trading benchmark used to evaluate the performance of institutional 

traders. It is the average price at which a given day’s trading in a given 

security took place. VWAP is calculated by adding up the dollars traded for 

every transaction (price times shares traded) and then dividing by the total 

shares traded for the day. The theory is that if the price of a buy trade is 

lower than the VWAP, then it is a good trade. The opposite is true if the 

price is higher than the VWAP.
Page 116 GAO-05-535 Securities Markets

Related GAO Products

Securities Markets: Preliminary Observations on the Use of Subpenny 

Pricing. GAO-04-968T. Washington, D.C.: July 22, 2004.

Securities Pricing: Trading Volumes and NASD System Limitations Led 

to Decimal-Trading Delay. GAO/GGD/AIMD-00-319. Washington, D.C.: 

September 20, 2000.

Securities Pricing: Progress and Challenges in Converting to Decimals. 

GAO/T-GGD-00-96. Washington, D.C.: March 1, 2000.

Securities Pricing: Actions Needed for Conversion to Decimals. GAO/TGGD-98-121. Washington, D.C.: May 8, 1998. 

(250195)
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