1 

Electronic Market Makers, Trader Anonymity and 

Market Fragility 

by 

Vikas Raman, Michel Robe and Pradeep K. Yadav*

Abstract 

We investigate empirically the impact of electronic market-makers on the reliability and the consistency

with which financial markets provide transactional liquidity services. Our empirical analysis is based on 

proprietary intraday data from U.S. futures markets. We document results of considerable regulatory 

importance. We find strong evidence that, in sharp contrast to the erstwhile locals in futures pits, 

electronic market makers reduce their participation and their liquidity provision in periods of significantly 

high and persistent volatility, in periods of significantly high and persistent customer order imbalances, 

and in periods of significantly high and persistent bid ask spreads. Our results are consistent with trader 

anonymity in electronic markets’ not being conducive to facile adjustment of severe information 

asymmetries. We also find that electronic market makers with longer trading horizons are much less 

susceptible to withdrawing from liquidity provision in periods of market stress. Overall, given that 

electronic market-makers represent the irreversible and inevitable progression of technology, our results 

raise the question whether exchanges and regulators should consider affirmative obligations for hitherto 

voluntary market makers. 

Keywords: Voluntary market-making, Electronic trading, HFT

JEL classification: G10, G14, G18 

First version: August 25, 2011 

This version: December 1, 2012 (with updated references) 

Permitted for public circulation by CFTC: May 29. 2014 

 

 

*

 Vikas Raman is at the Warwick Business School, University of Warwick, England. Michel Robe is at the Kogod 

School of Business, American University, Washington DC and at the Commodities and Futures Trading 

Commission (CFTC). Pradeep Yadav is at the Price College of Business, University of Oklahoma. Their email 

addresses are, respectively: vikas.raman@wbs.ac.uk, mrobe@american.edu and pyadav@ou.edu. We thank Andrei 

Kirilenko and Jim Moser for comments on the results of an earlier draft. Vikas Raman thanks the Energy Institute at 

the University of Oklahoma for financial support. The research presented in this paper was co-authored by Michel 

Robe, who performed the work on this paper in his official capacity as a consultant to the CFTC; and Vikas Raman 

and Pradeep Yadav, who performed work as CFTC contractors under CFTC OCE contracts CFCE-10-CO-0237 and 

CFOCE-13-0064. The Office of the Chief Economist and CFTC economists produce original research on a broad 

range of topics relevant to the CFTC’s mandate to regulate commodity future markets, commodity options markets, 

and the expanded mandate to regulate the swaps markets pursuant to the Dodd-Frank Wall Street Reform and 

Consumer Protection Act. These papers are often presented at conferences and many of these papers are later 

published by peer-reviewed and other scholarly outlets. The analyses and conclusions expressed in this paper are 

those of the authors and do not reflect the views of other members of the Office of Chief Economist, other 

Commission staff, or the Commission itself. The authors are responsible for all errors, if any. 

 
2 

Electronic Market Makers, Trader Anonymity and 

Market Fragility 

1. Background and Motivation

The quality of a financial market is determined by its ability to continually and reliably

provide low-cost transaction services for large size trades at an efficient price. In this context, the 

economic agents who enable and supply this liquidity are often generically (and sometimes 

loosely) labeled as “market-makers”. Such market makers perform the critically important role 

of providing ‘predictable immediacy’ in financial markets by standing ready and waiting to trade 

with the incoming buy and sell orders of those who demand immediate execution of their orders 

(Demsetz, 1968). They are willing to bear the cost and the risk of carrying unbalanced inventory 

exposures of the traded asset (Grossman and Miller, 1988), and are economically rewarded with 

the premium for doing so. 

Trading has increasingly moved to electronic platforms over the last two decades. With 

innovations in trading technology, a new market maker category has emerged and increasingly 

dominated liquidity supply in U.S. markets in recent years. This category is that of the electronic 

market makers (hereafter sometimes abbreviated as “EMM”). An electronic market maker can be 

characterized as a professional trader acting in a proprietary capacity, and engaged in trading 

strategies primarily directed at harvesting bid-offer spread revenues by net supply of liquidity, 

often through computer-based electronic trading decisions and automated computer-based trade 

executions. Electronic market maker profits are driven by buying and selling financial securities, 

often without human trade-by-trade interaction or the making of pre-meditated directional bets, 

but instead participating on both sides of the book, turning over inventory as often as is optimal 
3 

and often a large number of times during the day, thereby potentially generating a relatively high 

amount of trading volume with minimal capital investment.1

 Electronic market makers are very 

important since they collectively account for well over half of the trading volume in U.S. 

financial markets. The aim of this paper is to empirically investigate the impact of electronic 

market-makers on the reliability and the consistency with which financial markets provide 

transactional liquidity services. 

In this context, it is important to note that market makers in traditional equity markets, 

like the specialists on the New York Stock Exchange and the competing market makers on 

NASDAQ, have typically been obligated, through affirmative obligations, to always stand ready 

to supply liquidity and to maintain orderly markets. On the other hand, market makers in U.S. 

futures markets, like the “Locals” in futures pits, have always been “voluntary market makers”, 

being essentially traders with exchange membership engaged in liquidity supply activities to 

generate revenues and earn profits as part of their normal trading business, but without any 

formal affirmative obligations to maintain liquid and orderly markets. 

In spite of the move to electronic markets, of the increasing ability of public traders to 

contribute to liquidity supply, and of the deregulation that has taken place over the past two 

decades, market maker affirmative obligations in equity markets still exist through designated 

market-makers – though the ambit and effective impact of these affirmative obligations have 

significantly declined. In equity markets, we had very effective affirmative obligations before, 

and the market microstructure environment has changed to reduce but not eliminate the need or 

the effect of affirmative obligations. Hence, it is difficult to isolate the effect of electronic 

market-makers through an empirical analysis of equity markets. Also, because of the continued 

 

1

 EMM trading strategies can potentially generate a large number of trades over a very short period. However, the 

focus of this paper is on EMMs, not on high frequency traders per se. 
4 

availability of designated market-makers with affirmative obligations, the absence of continual 

and reliable liquidity services from electronic market-makers is arguably likely to have less of an 

adverse impact on the functioning of the market. On the other hand, the U.S. futures markets 

have always functioned through voluntary market makers without affirmative obligations, and 

they still function through voluntary market makers without affirmative obligations – albeit with 

one important change: the new electronic trading platform, and the presence of electronic 

market-makers rather than scalping locals in the trading pits. In the absence of any liquidity 

suppliers with affirmative obligations, the reliability of the liquidity supply – and hence the 

fragility of these markets – is critically dependent on the reliability with which liquidity is 

provided by voluntary electronic market-makers. The U.S. futures markets therefore provide the 

ideal laboratory to empirically investigate the impact of electronic market-making on market 

fragility. This is what this paper sets out to do. 

 Our motivation for investigating the impact of electronic market-making on market 

fragility arises from two perspectives. First, from an academic perspective, on the basis of extant 

theoretical models in finance, we argue that, even in a trading environment in which all marketmaking is voluntary (like it is in the U.S. futures markets), voluntary electronic market makers, 

often trading through computerized decision-making and automated trade execution, would be 

more likely than other voluntary market makers (like “locals” in futures markets) to exit the 

market and reduce their contribution to overall liquidity during periods of market stress. This is 

because the new electronic market-makers operate in an anonymous trading environment with 

greater sensitivity to perceived information asymmetry than in the traditional floor/pit trading 

environment where reputational considerations can potentially be relevant; and also because the 

electronic platform enables them to reduce their holding period of the asset. We comprehensively 
5 

formulate several questions and test several hypotheses in this context. 

Second, from the perspective of market participants, there have been extensive concerns 

articulated by regulators and policymakers that, while electronic market-making improves 

overall liquidity, it also generates greater potential for periodic episodic illiquidity. The issue 

came to the forefront with the Flash Crash of May 6, 2010 in one of the most liquid markets in 

world. In July 2011, a report by the International Organization of Securities Commissions 

(IOSCO), an international body of securities regulators, concluded that while "algorithms.... 

have been used by market participants to manage their trading and risk, their usage was also 

clearly a contributing factor in the flash crash event of May 6, 2010.” 2

 Regulators have 

specifically questioned the stability of the liquidity provided by electronic market-makers in 

modern electronic markets, though their major concerns have been specifically in relation to high 

frequency trading rather than electronic market makers more generally.3,4 Consequently, 

regulatory proposals endeavor to impose affirmative obligations to mandate market makers to 

‘make’ markets even during periods of stress. For example, the European Commission’s final 

 

2

 Earlier, a joint SEC/CFTC official report had concluded on September 30, 2010 that “HFTs initially provided 

liquidity to the large sell order that was identified as the cause of the crash. But after fundamental buyers withdrew 

from the market, HFTs, and all liquidity providers, also stopped trading and providing competitive quotes.”

Although Kirilenko, Kyle, Samadi and Tuzun (2014) find no evidence to link the flash crash to HFTs, they 

document that HFTs exacerbated volatility during the ‘Flash Crash’. 

3

 For example, speaking in the specific context of high frequency trading (“HFT”) rather than the broader context of 

electronic market-making, Andrew Haldane, Executive Director for Financial Stability at the Bank of England, and 

Member of the Interim Financial Policy Committee in his speech ‘Race to Zero’ (July, 2011), said: “Far from 

solving the liquidity problem in situations of stress, high-frequency trading firms appear to have added to it. And far 

from mitigating market stress, high-frequency trading appears to have amplified it. High-frequency trader liquidity, 

evident in sharply lower peacetime bid-ask spreads, may be illusory. In wartime, it disappears. This disappearing 

act, and the resulting liquidity void, is widely believed to have amplified the price discontinuities evident during the 

Flash Crash. High-frequency trader liquidity proved fickle under stress, as flood turned to drought”.

4

 In the same vein, Senator Charles Schumer, in a September 2010 letter to the SEC writes: “I have come to believe 

that high frequency traders provide less of the benefits to our markets than its adherents claim, and does so at a 

greater cost to long term investors.... The SEC should identify market participants who frequently engage in these 

practices, and require exchanges and other trading venues to slow down those market participants [in times of 

stress].... The Commission should consider imposing a minimum quote duration, so that orders could not be sent 

and cancelled within a fraction of a second”.
6 

draft proposals for the revised Markets in Financial Instruments Directive (MiFID II) along with 

a related regulation (MiFIR) were published on October 20, 2011; and included affirmative 

obligations requiring Electronic market makers to “be in continuous operation during the 

trading hours of the trading venue to which it sends orders or through the systems of which it 

executes transactions’ and also required that “the trading parameters or limits of an electronic 

trading strategy shall ensure that the strategy posts firm quotes at competitive prices with the 

result of providing liquidity on a regular and ongoing basis to these trading venues at all times, 

regardless of prevailing market conditions.” Regulatory proposals have also aimed at 

constraining electronic market-makers through fees and rules.5

In the context of the extensive regulatory concerns summarized above, and the associated 

proposals for affirmative obligations and fees, it is important to empirically test, in as clinically 

controlled testing environment as possible, whether electronic market makers are just the “fair 

weather friends” they have been conjectured to be, or if their contribution to liquidity supply is 

as reliable and stable as that of other voluntary market makers even at times of market stress. 

Extant empirical research has been confined to high-frequency trading rather than been on 

electronic market-making per se; and, even for high frequency trading, existing research has 

focused mostly on “normal” market conditions.6,7

 

5

 There have been proposals (e.g., House Resolution 1068) to impose a per-trade tax of .25%. Other suggestions 

include implementing fees when the number of canceled orders by a market participant exceeds a certain level, 

limiting the number of canceled orders, or requiring quotes to have a minimum life before they can be canceled or 

revised. The European Commission has also proposed a financial trading tax of 0.1% on trading of shares and bonds 

and 0.01% on trading of derivative contracts within the 27 member states of the European Union by 2014. 

6

 A recent exception is Brogaard, Hendershott and Riordan (2014) who conclude, based on 2008-2009 data on trades 

executed against liquidity on the NASDAQ exchange (excluding trades executed on other stock markets), that HFTs 

do not reduce their liquidity supply on high volatility days. Their data, unlike ours, do not identify each HFT 

individually, so their results rely on an artificial “aggregate” HFT – see also Carrion (2013) and Chordia (2013). A 

further advantage of our dataset is that it allows us to compare liquidity provision at the trader level in electronic 

(2008, 2011) vs. non-electronic (2006) market environments. 7

 Hendershott and Riordan (2013) find that high frequency traders play a positive role in price efficiency through 

their marketable orders. Hasbrouk and Saar (2013) find that low-latency activity improves traditional market quality 
7 

Despite extensive regulatory concerns, almost all extant studies examine the impact on 

market quality only under normal or average market conditions, rather than on any periods of 

stress; and extant studies do not tell us how market-makers in electronic markets differ in their 

trading behavior in this context from market-makers in the erstwhile face-to-face futures pits. 

They do not examine if and how the trading of electronic market makers exacerbates liquidity 

problems in situations of market stress. Do they continue to intermediate trades during periods of 

market stress? Do they ‘lean against the wind’? Are electronic market makers, who intermediate 

more than half the trading volume, averse to taking positions when volatility and/or informed 

trading is high? Are they more sensitive to market conditions than other voluntary liquidity 

providers? How did they trade during the 2008 financial crisis? How reliable or valuable is the 

liquidity electronic market makers supply? Are they only fair-weather friends who run for the 

exits when their liquidity provision is most needed? These important questions are the focus of 

our study. 

 We investigate crude oil futures markets. Our results are based on comparing two 

periods: a three-month period in 2006 in which trading was entirely in futures pits and market 

making was done by locals, and a three-month period in 2011 in which trading was largely on 

the electronic platform and intermediated largely by electronic market makers. Our focus is on 

“stressful” periods where stress is measured by high and persistent volatility, and/or high and 

persistent customer order imbalances, and/or high and persistent bid-ask spreads. We also 

consider the trading behavior of electronic market makers over a three-month period at the time 

of the 2008 financial crisis as being representative of a major “stressful” period. In a later 

 

measures such as short-term volatility, spreads, and displayed depth in the limit order book. Brogaard (2010) also 

finds that high-frequency traders provide liquidity and correct mispricing of securities. Hendershott, Jones, and 

Menkveld (2011) find that the introduction of auto-quote on the NYSE improves liquidity and enhances the 

informativeness of quotes. Raman, Robe and Yadav (2012) document that the introduction of electronic trading by 

the New York Mercantile Exchange (NYMEX) in 2006 leads to a big increase in trading by financial institutions 

which improves several measures of market quality. 
8 

version, we also propose to analyze from this perspective two essentially equivalent assets, i.e., 

two derivative contracts – a futures contract and an options contract – on the same asset (i.e., 

crude oil) traded at the same time, but in different systems, one an anonymous electronic system 

and the other a traditional face-to-face pit/floor trading environment where reputational 

considerations are potentially relevant. 

We document results of considerable academic and regulatory importance. We find 

strong empirical evidence that, in sharp contrast to the erstwhile locals in futures pits, electronic 

market makers reduce their participation and their liquidity provision in periods or significantly 

high and persistent volatility, in periods of significantly high and persistent customer order 

imbalances, and in periods of significantly high and persistent bid ask spreads. Our results are 

consistent with trader anonymity in electronic markets’ not being conducive to facile adjustment 

of severe information asymmetries. We also find that electronic market makers with longer 

trading horizons are much less susceptible to withdrawing from liquidity provision in periods of 

market stress. Finally, our results are also fully consistent with the changes we observe in 

liquidity provision around the 2008 financial crisis. Overall, insofar as electronic market-makers 

represent the irreversible and inevitable progression of technology, our results raise the question 

whether exchanges and regulators might usefully consider affirmative obligations for hitherto 

voluntary market makers. 

The remainder of this paper is organized as follows. Section 2 explains the motivation 

and delineates the questions that are addressed in this paper. Section 3 describes the data. Section 

4 documents the empirical results. Section 5 summarizes our findings and offers concluding 

remarks. 
9 

2. Questions Addressed and Testable Hypotheses

 The trading of electronic market makers is expected to be different from other marketmakers (voluntary or obligatory) during periods of stress mostly due to at least two reasons: first, 

they trade in electronic markets where traders are anonymous rather than in markets in which 

trader reputations can influence trading; and second, they trade with very short horizons. The 

questions we address in this paper largely flow from these two reasons. 

2.1 Trader anonymity and informed trading 

Benveniste, Marcus and Wilhelm (1992) show, on the basis of their theoretical modeling 

framework, that, in an exchange where traders are not anonymous, longstanding relationships 

between market participants can mitigate the effects of asymmetric information. A floor broker 

with a reputation of being informed, invariably pays higher spreads than others irrespective of 

the ‘informedness’ of the current trade. Due to lack of anonymity, reputation of a floor broker or 

market-maker plays an important role. These authors argue that, if a broker/trader is identified as 

having traded on private information, then the broker/trader will face long-term ‘sanctions’ 

whose costs will outweigh the benefits of concealing the private information. Consequently, 

floor traders/market-makers can separate informed and uninformed traders more efficiently than 

their counterparts in an electronic exchange, and the resulting separating equilibrium dominates 

the pooling equilibrium obtained in anonymous electronic exchanges. Since electronic market 

makers trade in markets where traders are anonymous, their trading strategies are significantly 

more sensitive to informed trading then the strategies often voluntary market makers in markets 

where market participants are not anonymous, for example, the locals in futures pits. 

Supporting the above contention, Franke and Hess (2000), using data from DTB and 

LIFFE, show that in periods of low information intensity, the insight into the order book of the 
10 

electronic trading system provides more valuable information than floor trading, but in periods of 

high information intensity, this is not the case. Similarly, Easley, Prado and O’Hara (2011) show 

that order flow ‘toxicity’ peaked around the flash crash. And importantly, Zigrand, Cliff and 

Hendershott (2011) argue that high-frequency traders rely on automated risk management 

algorithms to mitigate the disadvantage arising from the fact that they have no way of knowing 

their counterparties information level : these algorithms tend to limit high-frequency trader 

participation and liquidity provision at the first hint of a spike in informed trading. 

Consequently, electronic market maker strategies should arguably be very sensitive to the 

probability of informed trading. Electronic market makers should be more averse to taking 

positions and to providing liquidity during periods of market stress than other voluntary marketmakers operating in a Floor Trading type system in which traders are not anonymous. In this 

context, we first compare the trading activity of locals and electronic market makers during 

periods of market stress using two different sample periods with similar characteristics. Like 

electronic market makers, locals are voluntary market-makers, have inventories with similar 

half-lives, and also tend not to carry overnight inventories (Manaster and Mann, 1996). Second, 

we note that, according to Benveniste, Marcus and Wilhelm (1992), “the benefits of a floor 

exchange mechanism will be greatest when the potential for privately informed trading is 

greatest and when liquidity traders are most sensitive to transaction costs”. Hence, the greater the 

persistence of extreme market conditions, the greater the differences we would expect to find 

between the trading of locals and electronic market makers. 

2.2 Short-horizon trading

Electronic market makers are the prototypical ‘short-horizon’ traders in De Long, 

Shleifer, Summers and Waldmann (1990) who bear position risks only when they expect to 
11 

profitably offload their positions within their trading horizon. The trading advantage of 

Electronic Market Makers stems from their ability to trade in and out of positions faster than 

others (Javanovic and Menkveld, 2010). Such agility is hindered when capital is locked-up in a 

single position. Therefore, the lower the chances of profitable inventory rebalancing in a short 

period of time, the greater the reluctance to take a position and, conditional on participation, the 

smaller the position undertaken. 

These observations yield several testable implications. First, the factors that lower the 

chances of a profitable rebalancing of inventory in a short period of time are the underlying 

volatility (informational and liquidity related), the informed (‘Toxic’) order-flow, and the preexisting inventory position. As a corollary, electronic market makers are more likely to offload 

their inventory when the aforementioned variables increase; and consequently, more likely to 

demand liquidity than provide it when volatility, informed trading or pre-existing inventory 

positions increase. Second, electronic market makers with shorter trading horizons (proxied by 

rate of inventory mean-reversion) should arguably be more sensitive to the aforementioned 

variables than those with longer trading horizons. Third, by extension, electronic market makers 

should be more severely averse to trading and providing liquidity when both market conditions 

are severe and capital costs are high (which reduce trading horizons). Finally, trading and 

liquidity provision should be significantly lower during the 2008 financial crisis. 

3. Data and Variables Analyzed 

The data employed in this study consist of intraday transaction records of all WTI Crude Oil 

futures on the New York Mercantile Exchange (NYMEX) from three different time periods: 

January and March, 2006; April and December, 2008; and January and March, 2011. These data 

are obtained from the Commodity Futures Trading Commission (CFTC). In the electronic 
12 

trading period, post September, 2008, the crude oil futures market trades around the clock 

(except between 5:15 pm and 6 pm, EST). Prior to September, 2008, during the pit trading 

period, the markets trade only between 9am and 2.30pm. In order to ensure a “fair” comparison 

between the pre-electronic and post-electronic periods, we only study trading between 9am and 

2.30pm.8

 This dataset provides details such as the commodity and delivery month, the quantity, 

the price, and the date and time of the transaction. Moreover, buyer and seller identity codes are 

also provided.9

 Further, traders are classified into one of four customer types via a Customer 

Type Indicator (CTI), which ranges from 1 to 4 as follows: 

 CTI 1 traders are the individual members of the exchange, also known as ‘Locals’. 

 CTI 2 traders are the institutional members of the exchange. 

 CTI 3 traders are member traders trading on behalf of other member traders. 

 CTI 4 are the customers of the exchange or external traders 

Identification of Electronic Market-Makers 

Electronic market-makers (EMMs) are identified in the 2011 and 2008 samples. We 

identify EMMs based on two criteria. One, they are relatively active (greater than 2,000 trades a 

day). Two, their end-of-day positions are tiny compared to their daily trading volume (less than 

5%). Based on these criteria, we identify 52 traders as the de facto EMMs. Descriptive statistics 

relating to their trading are provided in Table 1, Panel A. Various important points are made in 

the panel. First, those 52 traders (0.35% of all trading accounts) account for about 50% of the 

overall trading volume in the world’s most heavily traded commodity - median Trades per day is 

 

8

 Even in the electronic trading period, most of the trading (~90%) occurs between 9am and 2.30pm (EST). 

Account-level data are aggregated across several dozen or hundreds of accounts in order to protect the 

confidentiality of individual traders’ underlying position(s) and trade secrets or strategies. 9

 These identities are coded by the CFTC so as to conceal the actual identities of the market participants. 
13 

4,372.73. Second, as expected, electronic market-makers’ trade size is less than the average 

market trade size. Third, consistent with our selection criteria, EMMs carry very little of their 

daily trading overnight - Mean Closing Ratio (End-of-Day Inventory/Total Trading) is ~ 0.00%. 

Identification of Locals 

 Locals are identified in the pre-electronic trading, 2006 sample. It should be noted that all 

the trading in this period happens in the Pits – there are no EMMs. As in Manaster and Mann 

(1996), CTI 1 traders are identified as locals. There are 941 CTI 1 traders, so that considering all 

these traders would be computationally costly. For this reason, we focus on those that trade 

more than 25 times a day - 941 CTI 1 traders account for 50.39% of the total trading volume and 

the selected 200 Locals account for 41.39% of trading volume. Descriptive statistics relating to 

their trading are provided in Table 1, Panel B. Similar to EMMs, Locals’ trade size is less than 

the average market trade size and they a tend to go-home ‘flat’ (Mean Closing Ratio is ~ 0.00%). 

Variable Definitions 

All market variables are calculated as 60 minute moving averages of 1 minute 

estimations. For example, Volatility of returns is calculated every minute and a moving average 

of the last 60 observations is reported. Also, all market variables are volume weighted averages 

across different maturities. Returns, Volatility of Returns, Volume and Bid-Ask spreads are 

calculated as done in the prior literature. We also calculate Customer Demand Imbalance (CD 

Imbalance, i.e., CTI 4 (Customers) Buy minus CTI 4 (Customers) Sell Volume) to indicate the 

direction and magnitude of the liquidity demanded by the customers of the exchange. 
14 

Table 1, Panels A and B provide descriptive statistics of the different market variables in 

the two time periods. This comparison yields the following observations. One, Volatility (of 

Returns) is not significantly different between the two periods. If anything, the 2006 sample 

exhibits higher Kurtosis. Two, Bid-Ask Spreads are higher in 2006 – clearly Pit trading was 

more profitable for liquidity providers than electronic trading is for EMMs. Three, CD 

Imbalance is more volatile in 2006. Finally, as expected, Volume is much higher in the 2011 

sample. In sum, the market variables are either similar across the time-periods, or the market 

conditions and deviations in market conditions are worse in the 2006 sample. 

4. Empirical Results

4.1 Overview of methodology 

Market-makers are clearly expected to be reluctant to trade and provide liquidity during 

market crashes: for example, Floor Traders on the NYSE and Dealers in NASDAQ had both 

closed shop on ‘Black Monday’ October 19, 1987. But, Electronic market makers, because of the 

inherent disadvantage in dealing with information asymmetry arising from electronic trading, 

and because of their objective to maximize their trading with minimal capital investment, could 

be extremely sensitive to even minor deviations from ‘normal’ conditions. It might not take a 

market-wide crash for electronic market makers to withdraw from the market: even small 

perturbations have the potential to instigate a withdrawal. In view of this conjecture, we examine 

the trading and liquidity provision of electronic market makers and locals when market 

conditions deviate from the mean by greater than two standard deviations. 

In all our univariate and multivariate analyses, we examine the trading of locals and of 

electronic market makers via two approaches. Our first approach is from the perspective of how 

intermediaries (both EMMs’ and Locals) trade with respect to the customers of the exchange? 
15 

Demsetz (1968) argues that the true measure of liquidity is the cost of transactions for the 

customers of the exchange, not the intermediaries. Our dataset allows us to ex ante identify 

customers of the exchange (CTI-4 traders, see Manaster and Mann (1996)). So, we test how the 

trading propensity of EMMs and Locals relates to demand imbalance (Buy-Sells) of CTI-4 

traders, which is also a proxy for order flow ‘toxicity’ (See Easley, Prado and O’Hara, 2010). 

And, conditional on trading, how the trading of EMMs and Locals is related to CTI-4 demand 

imbalance. For example, if EMM net trading volume is negatively related to CTI4 demand 

imbalance, then we can infer that EMMs performed the prescribed role of a liquidity supplier. 

 In our second approach, we examine how market-maker liquidity provision, both by 

EMMs’ and by Locals, depends on different market conditions. The greater the proportion of 

trading volume for which market makers are passive traders providing liquidity, the better the 

contribution to liquidity provision. In this context, one can proceed in two ways. First, extant 

work uses the textbook perspective on liquidity provision: a trader is deemed to be supplying 

liquidity when s/he is posting a standing limit order and demanding liquidity when s/he is 

“picking” an existing limit order through a market order or a marketable limit order. However, 

this perspective is not the only perspective that should be taken to liquidity provision. Market 

makers supplying liquidity engage in active inventory management, and have to occasionally 

demand liquidity to rebalance their inventory. With electronic market makers, this ratio can be 

much higher - up to 40% - as against 15 to 20% in conventional dealer markets (Sofianos, 1995). 

A second way to measure the extent of liquidity provision by a market maker is to estimate the 

extent to which “customer order flow” finds EMM counterparties to consummate their trades. 

Our data allows us to measure the extent to which EMMs offset customer order-flow, and we use 

this as a second measure of liquidity provision by the market maker. 
16 

4.2 Market maker trading activity in different market conditions: Univariate analysis 

 This subsection provides the results of the univariate analysis of the trading activity in 

different market conditions of both electronic market makers and locals during the relevant 

sample periods, 2011 and 2006 respectively. 

Table 2 provides a univariate picture of the trading activity of electronic market makers 

in normal market conditions and during periods of market stress. Periods of market stress are 

defined in terms of high volatility, high order imbalances, the presence of both high volatility 

and high order imbalances, and high bid ask spreads; where “high” is defined in terms of two 

standard deviations away from the mean. When volatility and/or the order imbalance is greater 

than two standard deviations, it means that the average of the one-minute volatility values or the 

average of the one-minute order imbalances over the past one hour have been abnormally high. 

 Table 2 provides strong and statistically significant conclusions. First, when volatility is 

persistently and significantly high, electronic market makers reduce their participation 

significantly. They also service significantly fewer customer trades, their overall liquidity 

provision in terms of posting of standing limit orders falls significantly, and their liquidity 

provision to customers also falls significantly. Second, when order imbalance is significantly and 

persistently high, the results are very similar to the volatility-related conclusions above. The 

more toxic the order flow, the lower is the extent of participation and liquidity provision by 

electronic market makers, both in general and specifically to customers. Not surprisingly, when 

both volatility and order imbalances are persistently and significantly high, the participation and 

liquidity provision of electronic market makers drops even more dramatically. Finally, when bidask spreads are significantly and persistently high, while the changes in electronic market maker 

participation liquidity provision is of a sign similar to that for volatility and order imbalances, the 
17 

results are not statistically significant. Overall, the univariate analysis clearly indicates that 

electronic market makers tend to withdraw and provide less liquidity during stressful periods. 

 There is, to our knowledge, no extant empirical analysis about the behavior of voluntary 

market makers during periods of market stress. Hence, on the basis of table 2, one may be 

tempted to conclude that voluntary market makers tend to withdraw and cut liquidity provision 

during periods of market stress. However, Table 3 provides a corresponding analysis of the 

behavior of locals, and the results are in complete contrast from the results reported in table 2 for 

each and every stress indicator. First, when volatility is persistently and significantly high, the 

participation of locals increases significantly, they also service significantly greater customer 

trades, their overall liquidity provision in terms of posting of standing limit orders increases

significantly, and their liquidity provision to customers also increases significantly. Second, 

when order imbalance is significantly and persistently high, the results are again very similar to 

the volatility-related conclusions above. The more toxic the order flow, the greater is the extent 

of participation and liquidity provision by locals, both in general and specifically to customers. 

Not surprisingly, when both volatility and order imbalances are persistently and significantly 

high, the participation and liquidity provision of locals increases even more dramatically. 

Finally, when bid ask spreads are significantly and persistently high, the changes in the 

participation and liquidity provision of locals is this time not only of a sign similar to that for 

volatility and order imbalances, the results are also statistically significant. Overall, the 

univariate analysis clearly indicates that locals tend to increase their participation and provide 

more liquidity during stressful periods. 

 The difference in results between locals and electronic market makers is unlikely to arise 

because of a difference in sample characteristics: as shown in the descriptive tables, the two 
18 

periods are quite comparable – if anything, the 2006 sample appears to be more volatile in terms 

of order imbalances. There could be two reasons why the results for different. First, locals could 

be better informed about price and liquidity schedules relative to electronic market makers 

because pits are expected to provide more information due to human interaction than what is 

feasible in anonymously traded electronic markets. Second, locals could be less averse to taking 

positions during stressful periods because they have longer trading horizons. We test these 

hypotheses in the next subsection. 

4.3 Market maker trading activity in different market conditions: Multivariate Analysis

Unlike obligated market-makers, EMMs (being voluntary market-makers/dealers) have 

the option of not participating in trades. Hence, we model EMM trading as a 2-stage process. In 

Stage 1, we model the EMMs’ decision to trade. In Stage 2, we model the direction and 

magnitude of the EMMs’ trading, conditional on trading. To this effect, we employ the Heckman 

two-stage regression methodology. In Stage 1, we use a Probit framework to determine the 

probability of a change in an EMM’s inventory. In Stage 2, we use ordinary least squares 

regressions to determine the direction and magnitude of change in an EMM’s inventory. Our 

results are in table 4. 

First, in regard to inventory, consistent with dealer inventory models, inventory not only 

affects the propensity to trade of the electronic market maker, it also affects the magnitude and 

the direction of new trades. A one-standard deviation increase in absolute inventory raises the 

propensity to trade of the electronic market maker by 81.2%. Also, when electronic market 

makers trade, they rebalance their inventory positions. Second, in regard to volatility, the 

propensity to trade of electronic market makers reduces significantly when volatility increases 

significantly. When volatility is high, they maintain higher spreads that reduce the probability of 
19 

trades. Third, in regard to trading revenues, electronic market makers are more likely to trade 

after losses, but only to reduce their inventories; and they tend to unwind positions after 

significant losses. Fourth, in regard to returns, electronic market makers are, on average, 

contrarian traders. However, importantly, when the magnitude of returns is significantly high, i.e. 

greater or less than two standard deviations away from the mean, these electronic market makers 

trade with the customer order flow: this pattern is completely consistent with the claims made by 

numerous regulators and commentators that electronic market makers demand rather than 

provide liquidity during such periods. Fifth, in regard to the absolute value of customer order 

imbalances, the trading propensity of electronic market makers reduces significantly when the 

absolute value of order imbalances is high; and the incremental effect of high absolute order 

imbalances on liquidity provision is also significantly negative. Clearly, electronic market 

makers are extremely reluctant to take positions when order flow is toxic. Sixth, in regard to 

order imbalances, it is again clear that, in normal conditions, electronic market makers trade 

against customers of the exchange – as they should. However, when order imbalances are 

abnormally and persistently high in magnitude, they trade alongside their customers: this 

behavior is consistent with the claim that, during periods of market stress, EMMs start 

demanding liquidity instead of providing it. Finally, in regard to bid ask spreads, on average, 

Electronic market makers trade more when spreads are high as they should; however, persistent 

and large bid ask spreads significantly reduce their participation. Overall, these results provide 

strong confirmation that electronic market makers significantly reduce their contribution to 

liquidity provision in periods of market stress. 

The corresponding results for locals are in table 5. First, not surprisingly, the results for 

inventory for locals are very similar to those for Electronic market makers; and even the half-
20 

lives of these inventories is very similar. For returns, the results are also similar. However, the 

results for other attributes are, as expected, different. For volatility, the propensity of locals to 

trade also drops significantly amid increases in volatility – but, in contrast to electronic market 

makers, there is measurable nonlinearity in the negative relationship: the participation of locals is 

negatively related to volatility only when the volatility is greater than two standard deviations. 

For trading revenues, locals do not reduce their inventories after significant losses – unlike 

electronic market makers. And the propensity to trade is significantly and positively related to 

absolute order imbalances: the trading of locals increases with demand imbalances, although at a 

lower rate in extreme conditions. Consistent with this finding, customer order imbalances are 

negatively related to inventory changes. Unlike the case of electronic market makers, even when 

customer order imbalances our abnormally and persistently high in magnitude, locals continue to 

trade against customers and continue to provide liquidity to customers. This provides evidence 

supporting the argument that pits may have been better suited to solve the problems associated 

with extreme levels of information asymmetry. Finally, again unlike electronic market makers, 

even persistent and abnormally large bid ask spreads do not reduce the participation of locals. 

Table 6 provides results for the trading behavior of electronic market makers and locals 

in extreme conditions that have not necessarily persisted for a relatively long time. In this table, 

we classify periods as “extreme” when the market variables have been greater than two standard 

deviations for one minute (instead of one hour). 

Our results show that the effect of 60 minutes of extreme conditions on the trading of 

locals is similar to the effect of a single minute of extreme conditions on the trading behavior of 

electronic market makers. Table 6 shows that, as the persistent of toxic order flows goes from 

one minute to 60 minutes, electronic market makers go from providing liquidity to demanding 
21 

liquidity. Their participation is positively related to demand imbalances overall, but the 

relationship in less positive when demand imbalances are greater than two standard deviations. 

They continue to fulfill customer order flow even in these one minute extreme conditions, but 

their behavior in relation to other variables a similar to their behavior in the case of persistent 

extreme conditions. Clearly, the informational disadvantages of electronic market makers with 

respect to locals appear to be greater when periods of market stress are more prolonged. 

Next, Table 7 reports the results of examining the trading behavior of the subset of 

electronic market makers who have a relatively long trading horizon. We measure the trading 

horizon based on the mean reversion of inventories. Those in the lowest quartile of mean 

reversion are classified as “longer-term traders”. Market stress is determined on the basis of a 

one-minute interval, as in Table 6. There are several notable results. First, mean-reversion takes 

place only when the inventory goes beyond two standard deviations, otherwise it drifts along. 

Second, electronic market makers do not liquidate after significant intraday losses. Third, they 

are not sensitive to returns. Fourth, and most importantly, their propensity to participate is 

positively and significantly related to volatility; this relationship holds even when the volatility is 

greater than two standard deviations. Overall, these results show that toxic order flows do not 

hinder participation or liquidity provision of longer-term horizon market makers. 

In Tables 8A and 8B, we present results similar to those in Table 4 and 5 but based on 

measuring liquidity provision according to the conventional approach of whether the market 

maker in question provided liquidity through posting of standing limit orders versus demanded 

liquidity by picking an existing limit order. For electronic market makers, liquidity provision by 

all measures decreases with volatility, and the incremental effect of extreme volatility is 

negative; liquidity provision by all measures also decreases significantly with the absolute value 
22 

off customer order imbalance and the incremental effect of extreme values is always negative 

and significant; but liquidity provision increases when spreads increase. Still, overall the results 

are largely similar to those obtained from the early analysis, and the bottom-line conclusion 

continues to be that electronic market makers provide significantly less liquidity in periods of 

market stress. On the other hand, locals are less sensitive to extreme levels of volatility and the 

starkest difference emerges as before in respect of the impact of customer order imbalances. 

Overall, our findings support the claim that electronic market makers are significantly more 

sensitive and averse to toxic order flow than locals are. 

4.4 Electronic market makers and the 2008 Financial Crisis 

In this subsection, we investigate our central proposition - the significant reduction in the 

participation of and liquidity provision by electronic market makers in periods of market stress - 

in the specific context of the 2008 financial crisis. To this end, we analyze data from April 2008 

to December 2008. We divide the time period from April, 2008 to December, 2008 as follows. 

The pre-Lehman period is from April to September 14, 2008. Crisis Period 1 is from September 

15 (Lehman Bankruptcy) to October 14, 2008. Crisis Period 2 is from October 15 (when retail 

sales hit a 3-year low and the Chairman of the Fed said that recovery will be slow) to November 

30, 2008. Crisis Period 3 is from December 1 (when the NBER confirmed that the United States 

had entered into a recession and U.S. manufacturing activity hit a 26-year low) to December 31, 

2008. 

 The results of our univariate analysis are in Table 9. Clearly, electronic market maker 

trading and liquidity provision dropped dramatically during the crisis periods, with drops as large 

as 50%. The trading is affected only after October 15. The results strongly confirm our previous 
23 

results about the aversion of electronic market makers to volatility and customer order 

imbalances. 

 The results of our multivariate analysis are presented in Table 10. This table is based on 

the following procedure. We use the three exogenous shocks to the crude oil market to examine 

the relation between the liquidity provision of electronic market makers and market variables of 

interest. In the first stage, we extract the components of market variables that are exogenous to 

the trading of electronic market makers: these are the predicted components from the regression. 

In the second stage, different measures of liquidity provision are regressed on the extracted 

exogenous components of market variables. This methodology is similar to the one employed by 

Hendershot et al. (2011). Our results again show that, during this period of extreme market 

stress, the participation and the liquidity provision of electronic market makers is inversely 

related to volatility, customer order imbalances, and bid ask spreads. 

5. Concluding Remarks

The liquidity and pricing efficiency of financial markets is critically dependent on the 

market makers who provide liquidity in these markets. With the move to electronic trading, and 

changes in trading technology, the nature of the market-makers supplying liquidity has changed 

significantly. Traders in electronic markets trade anonymously and face potentially greater 

information asymmetries than in markets with floor or pit traders. Electronic markets also allow 

market makers to have considerably shorter trading horizons. In these contexts, the aim of this 

paper is to empirically investigate the impact of electronic market-makers on the reliability and 

the consistency with which financial markets now provide transactional liquidity services. 

Our empirical analysis is based on proprietary intraday data from U.S. futures markets. 
24 

Market making in these markets has always been voluntary. Earlier, trading was in futures pits 

and locals were the voluntary market makers. Now, trading is electronic, and the new electronic 

market makers continue to be voluntary. Markets where market-making is voluntary are also 

more susceptible to issues of reliability and stability in liquidity provision. Hence, these markets 

provide an ideal laboratory for our investigation.

We document results of considerable academic and regulatory importance. We find 

strong evidence that, in sharp contrast to the erstwhile locals in futures pits, electronic market 

makers reduce their participation and their liquidity provision in periods or significantly high and 

persistent volatility, in periods of significantly high and persistent customer order imbalances, 

and in periods of significantly high and persistent bid ask spreads. Our results are consistent with 

trader anonymity in electronic markets’ not being conducive to facile adjustment of severe 

information asymmetries. We also find that electronic market makers with longer trading 

horizons are much less susceptible to withdrawing from liquidity provision in periods of market 

stress. Finally, our results are also fully consistent with the changes we observe in liquidity 

provision around the 2008 financial crisis. Overall, given that electronic market-makers represent 

the irreversible and inevitable progression of technology, our results raise the question of 

whether exchanges and regulators should consider affirmative obligations for hitherto voluntary 

market makers. 

 
25 

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27 

Table 1 –Sample Description This table presents characteristics of two data periods. Panel A presents market characteristics for the time-period January to Ma

characteristics of Electronic Market-Makers (EMMs). EMMs are traders who trade more than 2000 trades a day and carry less than 

volume overnight. Panel B presents market characteristics for the time-period January to March, 2006 and the trading characteristics of who trade more than 25 trades a day and are categorized under CTI (Customer Type Indicator) 1 category. All market variables ar

moving averages of 1 minute estimations and are volume weighted averages across different maturities. Returns, Volatility, Volume 

calculated as done in the literature. CD Imbalance Ratio (Customer Demand Imbalance Ratio) is the ratio of the difference between CSell and trading volume.Panel A: January to March, 2011 

Volatility Return Spread CD Imbalance Volume 

Mean 0.35% 0.00% 0.01% 1.83 488.45 

Median 0.24% 0.00% 0.01% -0.07 343.22 

Std 0.35% 0.11% 0.07% 75.49 538.12 

Min 0.00% -1.50% -0.98% -890.25 3.67 

P25 0.11% -0.05% -0.01% -18.81 183.35 P75 0.46% 0.05% 0.02% 18.48 604.02 

Max 3.48% 1.50% 0.88% 2130.06 13388.23 

Number of 

Traders Number of EMMs EMM Proportion (% Total Traders) EMM Trades (% Total Trades) EMM Volume (% Total Volume ) Median EMM Closing Ratio 

14984 52 0.35% 52.55% 47.46% 0.00% 

Panel B: January to March, 2006 

Volatility Return Spread CD Imbalance Volume 

Mean 0.29% 0.00% 0.35% 0.87 256.12 

Median 0.20% 0.00% 0.25% 0.00 179.84 

Std 1.51% 0.24% 0.39% 87.85 325.01 

Min 0.00% -9.25% -0.99% -1928.00 1.00 

P25 0.05% -0.06% 0.10% -14.38 63.11 P75 0.40% 0.06% 0.47% 15.00 344.30 

Max 163.64% 16.41% 26.48% 2674.98 6905.14 

Number of 

Traders Number of Locals Local Proportion (% Total Traders) Local Trades (% Total Trades) Local Volume (% Total Volume ) Median Local Closing Ratio 

13622 200 1.47% 44.97% 41.39% 0.00% 
28 

Table 2 – EMMs’ Trading Activity by Market Conditions: Univariate Analysis 

 This table presents univariate analysis of Electronic Market-Makers’ (EMMs) trading during periods of market 

stress- periods when market conditions (Eg: Volatility or CD Imbalance) are abnormally high (greater than 2 std. 

deviations) for prolonged period of time. For example, when Volatility-High is when 1-min Volatility (and/or CD 

Imbalance) over the past 1 hour has been greater than twice its standard deviation over the sample period. EMMs are 

traders who trade more than 2000 trades a day and carry less than 5% of their daily trading volume overnight. 

Customers are traders who are classified under the CTI (Customer Type Indicator) 4 categories in the dataset. EMM 

-Customer Volume is the sum of all trades during the minute where EMMs traded against Customers. EMM Liquidity 

Provision Volume is the sum of all trades during the minute where EMMs provided liquidity. EMM -Customer 

Liquidity Provision Volume is the sum of all trades during the minute where EMMs provided liquidity to Customers.

The analysis is conducted over using data from the time-period January to March, 2011.Two tailed p-values are also 

reported. 

 N 

NonEMM 

Volume 

Total 

Volume 

EMM -

Customer 

Volume 

Customer 

Volume 

EMM 

Liquidity 

Provision 

Volume 

Total 

Volume 

EMM -

Customer 

Liquidity 

Provision 

Volume 

Customer 

Volume 

Volatility-High 1232 30.92% 51.50% 40.14% 23.99% 

Volatility-Otherwise 23010 27.97% 55.83% 44.22% 27.27% 

Difference 2.96% -4.33% -4.08% -3.27% 

Percentage Difference 10.57% -7.75% -9.23% -12.01% 

p-value <0.001 <0.001 <0.001 <0.001 

CD Imbalance-High 1366 30.30% 53.51% 43.35% 26.33% 

CD Imbalance-Otherwise 22876 27.99% 55.73% 44.05% 27.15% 

Difference 2.31% -2.23% -0.70% -0.82% 

Percentage Difference 8.27% -4.00% -1.59% -3.01% 

p-value <0.001 <0.001 0.05 0.018 

(Volatility*CD Imbalance) - High 217 33.88% 49.37% 38.28% 22.53% 

(Volatility*CD Imbalance) -Otherwise 24025 28.07% 55.67% 44.07% 27.14% 

Difference 5.81% -6.29% -5.79% -4.61% 

Percentage Difference 20.71% -11.30% -13.14% -16.99% 

p-value <0.001 <0.001 <0.001 <0.001 

BidAsk Spreads -High 771 28.57% 54.06% 43.45% 26.89% 

BidAsk Spreads - Otherwise 23471 28.10% 55.66% 44.03% 27.11% 

Difference 0.46% -1.60% -0.58% -0.22% 

Percentage Difference 1.64% -2.88% -1.32% -0.80% 

p-value 0.32 0.003 0.208 0.631 
29 

Table 3 – Locals’ Trading Activity by Market Conditions: Univariate Analysis 

This table presents univariate analysis of Locals trading during periods of market stress- periods when market 

conditions (Eg: Volatility or CD Imbalance) are abnormally high (greater than 2 std. deviations) for prolonged 

period of time. For example, when Volatility-High is when 1-min Volatility (and/or CD Imbalance) over the past 1 

hour has been greater than twice its standard deviation over the sample period. Locals are traders who trade more 

than 25 trades a day and are categorized under CTI (Customer Type Indicator) 1 category. Customers are traders 

who are classified under the CTI (Customer Type Indicator) 4 categories in the dataset. Local -Customer Volume is 

the sum of all trades during the minute where Locals traded against Customers. Local Liquidity Provision Volume is 

the sum of all trades during the minute where Locals provided liquidity. Local - Customer Liquidity Provision 

Volume is the sum of all trades during the minute where Locals provided liquidity to Customers. The analysis is 

conducted over using data from the time-period January to March, 2011. Two tailed p-values are also reported. 

 N 

NonLocal 

Volume 

Total 

Volume 

LocalCustomer 

Volume 

Customer 

Volume 

Local 

Liquidity 

Provision 

Volume 

Total 

Volume 

Local -

Customer 

Liquidity 

Provision 

Volume 

Customer 

Volume 

Volatility-High 315 14.37% 77.73% 53.75% 41.90% 

Volatility-Otherwise 21445 24.56% 68.64% 47.23% 36.33% 

Difference -10.19% 9.08% 6.53% 5.57% 

Percentage Difference -41.50% 13.23% 13.82% 15.33% 

p-value <0.001 <0.001 <0.001 <0.001 

CD Imbalance-High 1256 18.74% 75.21% 50.20% 38.82% 

CD Imbalance-Otherwise 20504 24.76% 68.37% 47.14% 36.26% 

Difference -6.02% 6.84% 3.06% 2.56% 

Percentage Difference -24.31% 10.00% 6.49% 7.06% 

p-value <0.001 <0.001 <0.001 0.001 

(Volatility*CD Imbalance) - High 7 23.66% 66.49% 47.22% 37.95% 

(Volatility*CD Imbalance) -Otherwise 21753 24.41% 68.77% 47.32% 36.41% 

Difference -0.75% -2.29% -0.10% 1.54% 

Percentage Difference -3.06% -3.32% -0.21% 4.24% 

p-value 0.945 0.853 0.991 0.873 

BidAsk Spreads -High 1175 15.69% 77.19% 52.63% 41.32% 

BidAsk Spreads - Otherwise 20585 24.91% 68.29% 47.02% 36.13% 

Difference -9.22% 8.91% 5.61% 5.19% 

Percentage Difference -37.02% 13.04% 11.93% 14.36% 

p-value <0.001 <0.001 <0.001 <0.001 
30 

 Table 4 – EMMs’ Trading Activity by Market Conditions: Multivariate Analysis 

This table presents a (Heckman) 2-stage regression analysis of Electronic Market-Makers’ (EMMs) trading during 

periods of market stress. The first stage models the probability of an EMM trading during the minute. The second 

stage models the direction and magnitude of the EMM’s trading, conditional on trading. The analysis is conducted 

over using data from the time-period January to March, 2011. Periods of market stress are identified as periods when 

market conditions (Eg: Volatility or CD Imbalance) are abnormally high (greater than 2 std. deviations) for 

prolonged period of time. For example Volatility-High is a binary variable equal to 1when 1-min Volatility (and/or 

CD Imbalance) over the past 1 hour has been greater than twice its standard deviation over the sample period. 

EMMs are traders who trade more than 2000 trades a day and carry less than 5% of their daily trading volume 

overnight. Two tailed p-values are also reported. 

Parameter 

Model 1 Model 2 

Selection 

Equation Delta Inv Selection Equation Delta Inv 

Intercept -1.059 -0.094 -1.085 -0.125 

 <0.001 <0.001 <0.001 <0.001 

Abs Inventory 0.812 0.813 

 <0.001 <0.001 

Inventory -0.226 -0.163 

 <0.001 <0.001 

Inventory*Abs Inventory High -0.136 

 <0.001 

Trading Revenue Low 0.112 -0.012 0.111 -0.017 

 <0.001 0.191 <0.001 0.060 

Volatility -0.034 -0.047 

 <0.001 <0.001 

Volatility* VolatilityHigh 0.034 

 <0.001 

Return -0.002 -0.003 

 0.258 0.038 

Return*ReturnHigh 0.006 

 0.088 

Abs CD Imbalance -0.097 -0.055 

 <0.001 <0.001 

Abs CD Imbalance* ACDI_High -0.063 

 <0.001 

CD Imbalance -0.003 -0.005 

 0.026 0.011 

CD Imbalance* ACDI_High 0.007 

 0.023 

BidAsk Spread 0.012 0.016 

 <0.001 <0.001 

BidAsk Spread* SpreadHigh -0.024 

 <0.001 

Volume 0.056 0.056 

 <0.001 <0.001 

Mills Ratio 0.140 0.191 

 <0.001 <0.001 

Chi Sq <0.001 <0.001 

R Sq 0.113 0.123 

N 1257308 445990 1257308 445990 
31 

Table 5 – Locals’ Trading Activity by Market Conditions: Multivariate Analysis 

This table presents a (Heckman) 2-stage regression analysis of Locals trading during periods of market stress. The 

first stage models the probability of a Local trading during the minute. The second stage models the direction and 

magnitude of the Local’s trading, conditional on trading. The analysis is conducted over using data from the timeperiod January to March, 2006.Periods of market stress are identified as periods when market conditions (Eg: 

Volatility or CD Imbalance) are abnormally high (greater than 2 std. deviations) for prolonged period of time. For 

example, Volatility-High is a binary variable equal to 1when 1-min Volatility (and/or CD Imbalance) over the past 1 

hour has been greater than twice its standard deviation over the sample period. Locals are traders who trade more 

than 25 trades a day and are categorized under CTI (Customer Type Indicator) 1 category. Two tailed p-values are 

also reported. 

Parameter 

Model 1 Model 2 

Selection 

Equation Delta Inv Selection Equation Delta Inv 

Intercept -2.626 -0.259 -2.652 -0.280 

 <0.001 <0.001 <0.001 <0.001 

Abs Inventory 0.546 0.545 

 <0.001 <0.001 

Inventory -0.269 -0.220 

 <0.001 <0.001 

Inventory*Abs Inventory High -0.069 

 <0.001 

Trading Revenue Low 0.130 0.020 0.126 0.016 

 <0.001 0.451 <0.001 0.536 

Volatility -0.046 0.047 

 <0.001 0.002 

Volatility* VolatilityHigh -0.081 

 <0.001 

Return -0.013 -0.019 

 <0.001 0.002 

Return*ReturnHigh 0.021 

 0.003 

Abs CD Imbalance 0.049 0.115 

 <0.001 <0.001 

Abs CD Imbalance* ACDI_High -0.078 

 <0.001 

CD Imbalance -0.011 -0.009 

 <0.001 0.050 

CD Imbalance* ACDI_High -0.004 

 0.488 

BidAsk Spread 0.130 0.072 

 <0.001 <0.001 

BidAsk Spread* SpreadHigh 0.008 

 0.280 

Volume 0.448 0.436 

 <0.001 <0.001 

Mills Ratio 0.271 0.296 

 <0.001 <0.001 

Chi Sq <0.001 <0.001 

R Sq 0.132 0.134 

N 1088000 116071 1088000 116071 
32 

Table 6 – EMMs’ Trading Activity by Market Conditions (1 minute): Multivariate Analysis 

This table presents a (Heckman) 2-stage regression analysis of Electronic Market-Makers’ (EMMs) trading during 

periods of market stress. The first stage models the probability of an EMM trading during the minute. The second 

stage models the direction and magnitude of the EMM’s trading, conditional on trading. The analysis is conducted 

over using data from the time-period January to March, 2011. Periods of market stress are identified as periods when 

market conditions (Eg: Volatility or CD Imbalance) are abnormally high (greater than 2 std. deviations) over the 

previous minute. For example, Volatility-High-1min is a binary variable equal to 1when 1-min Volatility (and/or CD 

Imbalance) over the past 1 minute has been greater than twice its standard deviation over the sample period. EMMs

are traders who trade more than 2000 trades a day and carry less than 5% of their daily trading volume overnight. 

Two tailed p-values are also reported. 

Parameter 

Model 1 Model 2 

Selection 

Equation 

Delta 

Inv Selection Equation Delta Inv 

Intercept -1.166 -0.083 -1.238 -0.117 

 <0.001 <0.001 <0.001 <0.001 

Abs Inventory 0.809 0.809 

 <0.001 <0.001 

Inventory -0.226 -0.163 

 <0.001 <0.001 

Inventory*Abs Inventory High -0.135 

 <0.001 

Trading Revenue Low 0.100 -0.013 0.099 -0.018 

 <0.001 0.159 <0.001 0.044 

Volatility_1min -0.037 -0.043 

 <0.001 <0.001 

Volatility_1min * VolatilityHigh_1min 0.022 

 ] <0.001 

Return_1min -0.003 -0.006 

 0.049 0.038 

Return_1min *ReturnHigh_1min 0.009 

 0.088 

Abs CD Imbalance_1min 0.075 0.286 

 <0.001 <0.001 

Abs CD Imbalance_1min * ACDI_High_1min -0.242 

 <0.001 

CD Imbalance_1min -0.021 -0.070 

 <.0001 0.011 

CD Imbalance_1min * ACDI_High _1min 0.066 

 0.023 

BidAsk Spread_1min 0.004 0.024 

 0.028 <0.001 

BidAsk Spread_1min * SpreadHigh_1min -0.059 

 <0.001 

Volume_1min 0.139 0.125 

 <0.001 <0.001 

Mills Ratio 0.123 0.176 

 <0.001 <0.001 

Chi Sq <0.001 <0.001 

R Sq 0.114 0.126 

N 1257360 446007 1257360 446007 
33 

Table 7 – Longer-Term HFTs’ Trading Activity by Market Conditions (1 minute): Multivariate Analysis 

This table presents a (Heckman) 2-stage regression analysis of the trading of Electronic Market-Makers (EMMs) 

with slower rates (lowest quartile) of mean-reversion in inventories during periods of market stress. The first stage 

models the probability of an EMM trading during the minute. The second stage models the direction and magnitude 

of the EMM’s trading, conditional on trading. The analysis is conducted over using data from the time-period 

January to March, 2011. Periods of market stress are identified as periods when market conditions (Eg: Volatility or 

CD Imbalance) are abnormally high (greater than 2 std. deviations) for prolonged period of time. For example

Volatility-High is a binary variable equal to 1when 1-min Volatility (and/or CD Imbalance) over the past 1 hour has 

been greater than twice its standard deviation over the sample period. EMMs are traders who trade more than 2000 

trades a day and carry less than 5% of their daily trading volume overnight. Two tailed p-values are also reported. 

Parameter 

Model 1 Model 2 

Selection 

Equation 

Delta 

Inv Selection Equation Delta Inv 

Intercept -1.763 0.009 -1.812 0.008 

 <0.001 0.002 <0.001 <0.001 

Abs Inventory 1.298 1.299 

 <0.001 <0.001 

Inventory -0.003 0.000 

 <0.001 0.5775 

Inventory*Abs Inventory High -0.018 

 <0.001 

Trading Revenue Low 0.150 0.001 0.150 0.001 

 <0.001 0.226 <0.001 0.152 

Volatility_1min 0.021 0.022 

 <0.001 <0.001 

Volatility_1min * VolatilityHigh_1min 0.009 

 0.342 

Return_1min 0.000 0.000 

 0.294 0.289 

Return_1min *ReturnHigh_1min 0.000 

 0.556 

Abs CD Imbalance_1min 0.054 0.202 

 <0.001 <0.001 

Abs CD Imbalance_1min * ACDI_High_1min -0.167 

 <0.001 

CD Imbalance_1min -0.002 -0.004 

 <.0001 0.011 

CD Imbalance_1min * ACDI_High _1min 0.003 

 0.023 

BidAsk Spread_1min 0.001 0.019 

 0.028 <0.001 

BidAsk Spread_1min * SpreadHigh_1min -0.055 

 <0.001 

Volume_1min 0.100 0.089 

 <0.001 <0.001 

Mills Ratio -0.014 -0.013 

 0.001 <0.001 

Chi Sq <0.001 <0.001 

R Sq 0.031 0.012 

N 314340 86099 314340 86099 
34 

Table 8A – EMMs’ Liquidity Provision by Market Conditions: Multivariate Analysis 

This table presents multivariate analysis of Electronic Market-Makers’ (EMMs) liquidity provision. EMMs are 

traders who trade more than 2000 trades a day and carry less than 5% of their daily trading volume overnight. 

Customers are traders who are classified under the CTI (Customer Type Indicator) 4 categories in the dataset. EMM 

-Customer Volume is the sum of all trades during the minute where EMMs traded against Customers. EMM Liquidity 

Provision Volume is the sum of all trades during the minute where EMMs provided liquidity. EMM -Customer 

Liquidity Provision Volume is the sum of all trades during the minute where EMMs provided liquidity to Customers.

The analysis is conducted over using data from the time-period January to March, 2011.Two tailed p-values are also 

reported. 

Parameter 

EMM Liquidity 

Provision Volume / 

Total Volume 

 

EMM-Customer 

Volume / Total 

Customer Volume 

 

EMM - Customer 

Liquidity Provision 

Volume /Total 

Customer Volume 

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 

Intercept -0.092 -0.095 -0.102 -0.107 -0.080 -0.083 

 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 

Abs Inventory 0.152 0.152 0.168 0.168 0.133 0.133 

 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 

Abs Inventory*Diff Sign -0.006 -0.006 0.000 0.000 -0.004 -0.004 

 0.014 0.014 0.876 0.876 0.088 0.087 

Volatility -0.007 -0.005 -0.012 -0.012 -0.006 -0.006 

 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 

Volatility* VolatilityHigh -0.004 -0.001 -0.002 

 0.024 0.4402 0.3932 

Abs CD Imbalance -0.006 0.002 -0.008 0.000 -0.006 0.000 

 <0.001 0.308 <0.001 0.9511 <0.001 0.8708 

Abs CD Imbalance * ACDI_High -0.011 -0.013 -0.010 

 <0.001 <.0001 <0.001 

BidAsk Spread 0.003 0.003 0.000 0.000 0.003 0.002 

 <0.001 0.008 0.890 0.677 <0.001 0.168 

BidAsk Spread* SpreadHigh 0.000 0.001 0.003 

 0.867 0.7025 0.1177 

R Sq 0.011 0.011 0.014 0.014 0.009 0.009 

N 1254084 1254084 1254084 1254084 1254084 1254084 
35 

Table 8B – Locals’ Liquidity Provision by Market Conditions: Multivariate Analysis 

This table presents multivariate analysis of Locals’ liquidity provision. Locals are traders who trade more than 25 

trades a day and are categorized under CTI (Customer Type Indicator) 1 category. Customers are traders who are 

classified under the CTI (Customer Type Indicator) 4 categories in the dataset. Local -Customer Volume is the sum 

of all trades during the minute where Locals traded against Customers. Local Liquidity Provision Volume is the sum 

of all trades during the minute where Locals provided liquidity. Local - Customer Liquidity Provision Volume is the 

sum of all trades during the minute where Locals provided liquidity to Customers. The analysis is conducted over 

using data from the time-period January to March, 2006.Two tailed p-values are also reported. 

Parameter 

Local Liquidity 

Provision Volume / 

Total Volume 

 

Local - Customer 

Volume / Total 

Customer Volume 

 

Local - Customer 

Liquidity Provision 

Volume /Total 

Customer Volume 

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 

Intercept -0.073 -0.070 -0.047 -0.045 -0.058 -0.055 

 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 

Abs Inventory 0.105 0.105 0.072 0.072 0.084 0.084 

 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 

Abs Inventory*Diff Sign 0.002 0.001 -0.004 -0.004 0.001 0.001 

 0.517 0.598 0.219 0.197 0.632 0.692 

Volatility -0.001 0.033 -0.001 0.019 -0.001 0.026 

 0.754 <0.001 0.715 <0.001 0.691 <0.001 

Volatility* VolatilityHigh -0.026 -0.016 -0.022 

 <0.001 <0.001 <0.001 

Abs CD Imbalance 0.014 0.023 0.007 0.009 0.010 0.014 

 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 

Abs CD Imbalance * ACDI_High -0.017 -0.006 -0.009 

 <0.001 0.010 <0.001 

BidAsk Spread 0.030 0.020 0.018 0.011 0.020 0.010 

 <0.001 <0.001 <0.001 0.002 <0.001 0.003 

BidAsk Spread* SpreadHigh -0.027 -0.012 -0.015 

 <0.001 <0.001 <0.001 

R Sq 0.091 0.095 0.038 0.039 0.056 0.057 

N 1061800 1061800 1049000 1049000 1049000 1049000 
36 

Table 9 – EMMs’ Trading Activity During the 2008 Crisis: Univariate Analysis 

This table presents univariate analysis of Electronic Market-Makers’ (EMMs) trading during the financial crisis of 

2008. EMMs are traders who trade more than 2000 trades a day and carry less than 5% of their daily trading volume 

overnight. Customers are traders who are classified under the CTI (Customer Type Indicator) 4 categories in the 

dataset. EMM -Customer Volume is the sum of all trades during the minute where EMMs traded against Customers. 

EMM Liquidity Provision Volume is the sum of all trades during the minute where EMMs provided liquidity. EMM -

Customer Liquidity Provision Volume is the sum of all trades during the minute where EMMs provided liquidity to 

Customers. The analysis is conducted over using data from the time-period April to December, 2008.Two tailed pvalues are also reported. 

 N 

Non-EMM 

Volume 

Total 

Volume 

EMM -

Customer 

Volume 

Customer 

Volume 

EMM 

Liquidity 

Provision 

Volume 

Total Volume 

EMM -

Customer 

Liquidity 

Provision 

Volume 

Customer 

Volume 

Crisis Period 1: Sept,15 to Oct, 14 22 43.30% 43.78% 35.14% 22.71% 

Pre-Lehman 91 42.84% 44.27% 35.23% 22.43% 

Difference 0.47% -0.49% -0.09% 0.27% 

Percentage Difference 1.09% -1.11% -0.27% 1.22% 

p-value 0.32 0.592 0.884 0.519 

Crisis Period 2: Oct,14 to Nov, 30 32 55.16% 34.08% 26.54% 17.52% 

Pre-Lehman 91 42.84% 44.27% 35.23% 22.43% 

Difference 12.32% -10.19% -8.69% -4.91% 

Percentage Difference 28.77% -23.01% -24.68% -21.88% 

p-value <0.001 <0.001 <0.001 <0.001 

Crisis Period 3: Dec 1 to Dec, 31 22 64.32% 26.46% 20.66% 13.82% 

Pre-Lehman 91 42.84% 44.27% 35.23% 22.43% 

Difference 21.48% -17.81% -14.58% -8.61% 

Percentage Difference 50.14% -40.23% -41.37% -38.40% 

p-value <0.001 <0.001 <0.001 <0.001 
37

Table 10 – EMMs’ Trading Activity during the 2008 Crisis: Multivariate Analysis 

This table presents a 2-stage regression analysis of Electronic Market-Makers’ (EMMs) trading the 

financial crisis of 2008. We use the three exogenous shocks – proxied by variables Crisis Period 1, Crisis 

Period 2 and Crisis Period 3 - to the Crude Oil market to further examine the relation b/w EMMs’ liquidity 

provision and market variables of interest. Crisis Period 1 is a binary variable equal to 1between September 

15 (Lehman Bankruptcy) and October 14,2008. Crisis Period 2 is a binary variable equal to 1between 

October 15 (Retail Sales hit 3-year low and Bernanke says recovery will be slow) and November 30, 2008. 

Crisis Period 3 is a binary variable equal to 1 between December 1 (NBER Report Confirms Recession and 

US Manufacturing hits 26-year low) and December 31,2008. In the first stage we extract the components of 

market variables that are exogenous to EMM trading – the predicted components from the regression. In 

the second stage we regress different measures of EMM liquidity provision on the hence extracted 

exogenous components of market variables. The analysis is conducted over using data from the time-period 

April to December, 2008. EMMs are traders who trade more than 2000 trades a day and carry less than 5% 

of their daily trading volume overnight. Customers are traders who are classified under the CTI (Customer 

Type Indicator) 4 categories in the dataset. EMM -Customer Volume is the sum of all trades during the 

minute where EMMs traded against Customers. EMM Liquidity Provision Volume is the sum of all trades 

during the minute where EMMs provided liquidity. EMM -Customer Liquidity Provision Volume is the sum 

of all trades during the minute where EMMs provided liquidity to Customers. Two tailed p-values are also 

reported. 

First Stage Regression 

Independent\Dependent Variables Volatility CD Imbalance BidAsk Spreads 

 

Intercept -0.116 0.000 -0.133 

 <0.001 0.978 <0.001 

Crisis Period 1 0.178 -0.008 0.170 

 <0.001 0.478 <0.001 

Crisis Period 2 0.236 -0.001 0.287 

 <0.001 0.900 <0.001 

Crisis Period 3 0.383 0.009 0.442 

 <0.001 0.445 <0.001 

Lag Volatility 0.149 

 <0.001 

Lag CD Imbalance 0.0646 

 <.0.001 

Lag BidAsk Spreads 0.1165 

 <0.001 

N 66852 66852 66852 

R-Square 0.049 0.0042 0.0474 

 
38

Second Stage Regressions 

Independent\Dependent Variables 

Non-EMM 

Volume 

Total Volume

EMM -Customer 

Volume 

Customer 

Volume

EMM-Liquidity 

Provision 

Volume 

Total Volume

EMM -Customer 

Liquidity 

Provision 

Volume 

Customer 

Volume

 

Intercept 0.480 0.400 0.317 0.205 

 <0.001 <0.001 <0.001 <0.001 

Volatility(Predicted) 0.070 -0.062 -0.047 -0.029 

 0.098 0.076 0.104 0.091 

CD Imbalance(Predicted) 0.104 -0.238 -0.070 -0.116 

 <0.001 <0.001 <0.001 <0.001 

BidAsk Spreads (Predicted) 0.292 -0.110 -0.199 -0.062 

 <0.001 <0.001 <0.001 <0.001 

N 66852 66852 66852 66852 

R-Square 0.202 0.149 0.176 0.093