Securities Markets: Decimal Pricing has Contributed to Lower Trading Costs and a More Challenging Trading Environment
Published: 2005-05-31T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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 broker-dealers 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.
section_title: What GAO Found
paragraphs: 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.

section_title: Letter
paragraphs: 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. 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. 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. 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. 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  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. 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  Washington, D.C., between May 2004 and May 2005 in accordance with  generally accepted government auditing standards.
subsections: section_title: Background
paragraphs: 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. 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.
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 broker- dealers 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.
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. 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 investors’ duty to obtain the best execution for their trades, further  increasing the pressure on these firms to better manage their trading costs.
section_title: Investors’ Trading Costs Have Declined Since Decimalization, but Reduced Market Transparency Has Caused Firms to Adopt New Trading Strategies
paragraphs: 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.
subsections: section_title: Decimal Pricing Reduced Trading Costs for Retail Investors
paragraphs: In ordering U.S. markets to convert to decimal pricing, SEC had several  goals. 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. 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). 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  NASDAQ following the implementation of decimal pricing, falling to levels  well below those that existed before the conversion to decimal pricing.
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  average quoted spread falling 73 percent for NYSE stocks and 68 percent  for NASDAQ stocks.
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. 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  sample stocks and 59 percent for our NASDAQ sample stocks between the  periods after decimal pricing was implemented.
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). 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.
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. 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. 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.
section_title: Institutional Investors’ Trading Costs Have Also Declined Since Decimalization
paragraphs: 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. 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. 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.
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. 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.
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.  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.
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.
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).
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).
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). 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  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.
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  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.
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.
subsections: section_title: Academic Studies Generally Showed Declining Costs for Institutional Investors
paragraphs: 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. 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. 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  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. 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 volume- weighted 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.
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. 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. 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.
section_title: Institutional Investors Reported Reduced or Level Trading Costs after Decimalization
paragraphs: 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. Representatives for 20 of these firms said that their trading  costs had fallen or stayed about the same since decimals were  implemented (table 4).
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  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.
section_title: Volatility Has Also Improved since Decimal Pricing Began
paragraphs: 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. 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.
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.
section_title: Despite Reduced Market Transparency for Large Orders, Institutional Investors Have Been Able to Complete Trades
paragraphs: 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 sample period before the markets converted to decimal pricing and a  period soon after the conversion took place (table 6).
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.
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.  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 and can increase their trading costs. 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). 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.
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. 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.
subsections: section_title: Institutional Investors Have Adjusted Their Trading Methods to Continue Executing Large Orders
paragraphs: 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. 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.
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.
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.
section_title: Institutional Investors Increasingly Use Electronic Trading and Alternative Trading Venues
paragraphs: 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  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. 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.
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. 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).
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.
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.
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  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. 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.
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.
section_title: Market Conditions May Also Have Helped Lower Institutional Investors’ Trading Costs
paragraphs: 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.
section_title: Some Stock Intermediaries Have Experienced Lower Profits since Decimalization, but Other Factors Have Contributed to the Declines
paragraphs: Although overall securities industry profits have returned to levels similar  to those in the past, some market intermediaries, particularly those broker- dealers 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.
subsections: section_title: Conditions in the Overall Securities Industry Appear to Be Improving
paragraphs: 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. 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.  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.  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.
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.
section_title: Decimalization Has Negatively Affected Exchange Specialists
paragraphs: 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.   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).
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).
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 Securities Clearing Corporation, of between $0.0075 to $0.15 per trade.   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.
section_title: Broker-dealers Revenues from NASDAQ Activities Have Also Fallen since Decimal Pricing Began
paragraphs: 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.
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.
section_title: Declining Intermediary Profits Have Accelerated Industry Consolidation
paragraphs: 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.
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  to support other lines of business. For example, an official of a broker- dealer 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.
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.
section_title: Other Factors Have Contributed to Declining Intermediary Revenues and Profits
paragraphs: 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.
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 broker- dealers 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  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.
section_title: Brokerage Firms Have Made Adjustments to Business Activities and Personnel Levels
paragraphs: 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  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.   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.
section_title: Decimal Pricing Did Not Appear to Affect Businesses’ Ability to Raise Capital
paragraphs: 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. 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.
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.
section_title: Proposed Pilot for Higher Minimum Tick Size Receives Mixed Responses
paragraphs: 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.
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.
section_title: Decimal Pricing Has Had a Limited Impact on the Options Markets, but Other Factors Have Helped Improve Market Quality
paragraphs: 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  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 electronic- based 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.
subsections: section_title: The Shift to Decimal Pricing Did Not Reduce Tick Sizes for Options as Much as for Stocks
paragraphs: 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.
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. 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.”
section_title: Although Decimal Pricing Not Significant, Key Measures of Options Markets’ Quality Have Improved
paragraphs: 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.
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. 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.
section_title: Factors Other Than Decimal Pricing Have Been Credited with Improving the Quality of Options Markets
paragraphs: 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. 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. 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.
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. 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.
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  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. 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.
section_title: The Impact on Options Market Intermediaries Varied since Decimal Pricing Began
paragraphs: 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 broker- dealers 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.
section_title: Options Market Participants Oppose Lower Minimum Ticks for the Options Industry
paragraphs: 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.   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. 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  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.
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.
section_title: Observations
paragraphs: 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 bid- ask 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  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.
section_title: Agency Comments
paragraphs: 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.
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.
section_title: Scope and Methodology
paragraphs: 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.
subsections: section_title: Methodology for Assessing Impact on Institutional Investors
paragraphs: 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.
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 broker- dealers, securities regulators, academics, and alternative trading venues,  stock exchanges, trade analytics firms, a financial market consulting and  research firm, and industry trade groups.
section_title: Methodology for Assessing Impact on Market Intermediaries
paragraphs: 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.
section_title: Methodology for Assessing Impact on Options Markets
paragraphs: 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.
section_title: Methodology for GAO Analysis of Trade and Quotes Data
paragraphs: 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. 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. 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  volume and reported our results so that any differences across stock  characteristics could be observed.
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.
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 2004. 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.
subsections: section_title: Determining the Sample Period for Our Analysis
paragraphs: 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. 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)  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. 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.
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. 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.
section_title: Generating the Matched Pairs
paragraphs: 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. 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. 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. in which the superscripts  respectively, and  Yfor each—in which  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  normalized relative to the overall NYSE attributes.   refer to NYSE and NASDAQ,   and   denotes the NYSE stock and   represent one of the four stock attributes  —in order to create unit less measures that were  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 NASDAQ stocks that provided the best matches. We chose the 300 best  CMS matched pairs, which consisted of 300 NYSE and 186 unique NASDAQ  stocks. 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.
section_title: Characteristics of Our Sample Stocks
paragraphs: 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.
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).
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  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 bid- ask 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. 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  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.
section_title: General Analysis Techniques
paragraphs: 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  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 cross- sectional variation in the stock-specific means. With the exception of  volatility measures, statistical significance was assessed using a standard t- test 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.
section_title: Measuring Trade Execution Costs and Other Market Quality Components with TAQ Data
paragraphs: 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 pre-  and 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.
section_title: Calculating Quoted Bid-Ask Spreads as a Simple Measure of Trading Costs
paragraphs: 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  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.
section_title: Calculating Effective Bid-Ask Spreads as a Better Measure of Trading Costs
paragraphs: 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.
section_title: Measuring Quotation Sizes
paragraphs: 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 volume- weighted average over each sample week as well as across all sample  weeks.
section_title: Measuring Intraday Return Volatility
paragraphs: 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.
section_title: Measuring Daily Price Range
paragraphs: 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 equal- weighted 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.
section_title: Measuring Trade and Quote Clustering
paragraphs: 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. 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.   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.
section_title: Efforts to Assess Reliability of TAQ Data
paragraphs: 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  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.
section_title: Measurement of Institutional Investors’ Trading Costs in Basis Points Shows Decline since Decimal Pricing Implemented
paragraphs: 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. 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.  Total trading costs declined by about 32  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.
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.   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  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 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  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.
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).
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.
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). 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  identified in the Abel/Noser data represented the entire total trading cost  reductions for NYSE stocks.
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  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.
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.
section_title: Additional Analysis Using Trade and Quotes Data
paragraphs: 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).
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  sample weeks from April 2001 through November 2004. Prices are  observed generally clustering at nickel increments.
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.
section_title: GAO Contacts and Staff Acknowledgments
subsections: section_title: GAO Contacts
section_title: Staff Acknowledgments
paragraphs: 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.
section_title: Glossary of Terms
subsections: section_title: Ask price (offer/sell price)
paragraphs: The lowest price at which someone is willing to sell a security at a given  time.
section_title: Basis point
paragraphs: A basis point is equal to 1/100 of 1 percent.
section_title: Bear market
paragraphs: A market in which stock prices decline over a sustained period of time.
section_title: Best execution requirement
paragraphs: The obligation of broker-dealers to seek to obtain the best terms  reasonably available under the circumstances for customer orders.
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.
section_title: Bid price (buy price)
paragraphs: The highest price at which someone is willing to buy a security at a given  time.
section_title: Block trade
paragraphs: 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.
section_title: Broker
paragraphs: An individual or firm who acts as an intermediary (agent) between a buyer  and seller and who usually charges a commission.
section_title: Bull market
paragraphs: A market in which stock prices rise over a sustained period of time.
section_title: Call option
paragraphs: A contract granting the right to buy a fixed amount of a given security at a  specified price within a limited period of time.
section_title: Commission
paragraphs: A fee paid to a broker for executing a trade based on the number of shares  traded or the dollar amount of the trade.
section_title: Dealer
paragraphs: An individual or firm in the business of buying and selling securities for his  or her own account (principal) through a broker or otherwise.
The quoting and trading of securities in dollars and cents ($2.25) instead of  fractions ($8 1/8).
section_title: Delay cost
paragraphs: 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.
section_title: Effective spread
paragraphs: 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.
section_title: Electronic Communication Network (ECN)
paragraphs: 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.
section_title: Exchange
paragraphs: 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.
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.
section_title: Floor broker
paragraphs: 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.
section_title: Inside spread (inside quote)
paragraphs: The highest bid and lowest offer being quoted among all the market makers  competing in a security.
section_title: Intermarket linkage system
paragraphs: 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.
section_title: Institutional investor
paragraphs: 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.
section_title: Limit order
paragraphs: 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.
section_title: Liquidity
paragraphs: The ease with which the market can accommodate large volumes of  securities trading without significant price changes.
section_title: Listed stock
paragraphs: The stock of a company that is listed on a securities exchange.
section_title: Market depth
paragraphs: The numbers of shares available for trading around the best bid and ask  prices.
section_title: Market impact
paragraphs: The degree to which an order affects the price of a security.
section_title: Market maker
paragraphs: A dealer that maintains a market in a given security by buying or selling  securities at quoted prices.
section_title: Market order
paragraphs: An order to buy or sell a stated amount of a security at the best price  available when the order reaches the marketplace.
section_title: NASDAQ Stock Market (NASDAQ)
paragraphs: 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.
section_title: National best bid and offer (NBBO)
paragraphs: Defined as the highest bid and lowest ask across all U.S. markets providing  quotes for an individual stock.
section_title: Order Handling Rules
paragraphs: 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));  (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).
section_title: Opportunity cost
paragraphs: 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.
section_title: Price improvement
paragraphs: Occurs when an order is executed at better than the quoted price.
section_title: Put option
paragraphs: A contract granting the right to sell a fixed amount of a given stock at a  specified price within a limited period of time.
section_title: Quote
paragraphs: The highest bid to buy and the lowest offer to sell any stock at a given time.
section_title: Quote flickering
paragraphs: Where a given price quote is only visible for a brief moment on the display  screen.
section_title: Quoted spread
paragraphs: 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).
section_title: Retail investor
paragraphs: One who trades securities for himself/herself or who gives money to any  institution, such as a mutual fund, to invest for himself/herself.
section_title: Securities and Exchange Commission
paragraphs: 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.
section_title: Specialists
paragraphs: 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.
section_title: Stepping ahead/penny jumping
paragraphs: The practice of improving the best price by a penny or less in an attempt to  gain execution priority.
section_title: Stock
paragraphs: A financial instrument that signifies an ownership position in a company.
section_title: Tick size (or minimum price increment)
paragraphs: The smallest price difference by which a stock price can change (up or  down).
The execution of a customer order in a market at a price that is inferior to a  price displayed (or available) in another market.
section_title: Trading cost
paragraphs: The cost for executing the trade (brokerage commission, fees, market  impact).
section_title: Transparency
paragraphs: The degree to which trade and quotation information (price and volume) is  available to the public on a current basis.
section_title: Volatility
paragraphs: A measure of the fluctuation in the market price of a security.
section_title: Volume
paragraphs: 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.
section_title: Volume weighted average price (VWAP)
paragraphs: 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.
section_title: Related GAO Products
paragraphs: 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/T- GGD-98-121. Washington, D.C.: May 8, 1998.