The Impacts of Automation and High Frequency Trading on Market Quality 1

Transcription

1 The Impacts of Automation and High Frequency Trading on Market Quality 1 Robert Litzenberger 2, Jeff Castura and Richard Gorelick 3 In recent decades, U.S. equity markets have changed from predominantly manual markets with limited competition to highly automated and competitive markets. These changes occurred earlier for NASDAQ stocks (primarily between 1994 and 2004) and later for NYSE-listed stocks (mostly following Reg NMS and the 2006 introduction of the NYSE hybrid market). This paper surveys the evidence of how these changes impacted market quality and shows that overall market quality has improved significantly, including bid-ask spreads, liquidity, and transitory price impacts (measured by short-term variance ratios). The greater improvement in market quality for NYSE-listed stocks relative to NASDAQ stocks beginning in 2006 suggests causal links between the staggered market structure changes and market quality. Studies using proprietary exchange provided data sets that distinguish activity by high frequency trading firms show they contributed directly to: narrowing bid-ask spreads, increasing liquidity, and reducing intra-day transitory pricing errors and intra-day volatility. Corresponding Author: Jeff Castura [email protected] 1 The views presented in this paper are the authors only and do not necessarily represent the views or opinions of any other person or entity. The authors would like to thank Richard Lindsey for his historical perspectives on market structure and Amy Litzenberger, Cameron Smith, Bill O Brien and Chris Smith for their helpful comments. 2 Robert Litzenberger is affiliated with the University of Pennsylvania and RGM Advisors, LLC 3 Jeff Castura and Richard Gorelick are affiliated with RGM Advisors, LLC 1

2 Table of Contents Introduction Pre- Electronic Trading and Institutional Investors Leveling the Playing Field Bid- Ask Spreads Posted Liquidity Trade Sizes Market Efficiency Profitability of HFT and Transaction Taxes Volatility Technology and Latency Messaging Rates Concluding Remarks References 2

3 Introduction Trading in financial instruments has seen dramatic changes over recent decades. Advances in computing power and improvements in telecommunications networks have facilitated the development of highspeed, interconnected electronic trading platforms and have enabled a dramatic increase in automated trading. As the ability to easily connect to electronic trading platforms has grown, traders have adapted their methods to seek new opportunities to profit. One such method is high frequency trading (HFT) which has become widespread and has received significant attention in the media and from policy makers. There has not, to date, been a consistent academic or regulatory definition of high frequency trading. People use the term in different ways and for different purposes, which has made discussions about modern, electronic trading and its impact on markets more difficult. However, the term HFT is often used to refer to types of automated trading that trade frequently. HFT is also commonly characterized as having a sensitivity to speed (i.e., latency) and transaction costs; however, many types of trading fall within this broad characterization, including without limitation, modern electronic market making, short-term directional trading, and various forms of arbitrage and statistical arbitrage. For most of these types of trading, low- 3

4 latency, high-throughput trading technology is important. While these types of trading are often conducted on a proprietary basis, much of the algorithmic trading conducted on an agency basis uses the same technologies and tools as those conducted on a principal basis and for some purposes is referred to as HFT. Additionally, proprietary HFT is often, but not always, characterized as trading with relatively high rates of position turnover and small risk positions held outside of regular trading hours (relative to total trading volumes). The growth of new electronic trading platforms and the increasing use of automation and advanced computing technology have raised questions about the effect these changes have had on the state, functioning and integrity of markets. Initiatives such as the U.S. SEC s concept release on equity market structure in 2010 (U.S. SEC 2010b), the UK government s ongoing Foresight Project on the Future of Computer Trading in Financial Markets (BIS 2011) and the European Commission s MiFID II review in Europe (European Commission 2011), are all attempts to gain a better understanding of the true impact of these changes. To provide a benchmark for evaluating the changes of recent decades, the structure of pre-electronic markets is briefly discussed in the initial section of this paper. The regulatory and competitive changes that leveled the playing 4

5 field are discussed next. Then the characteristics of HFT are discussed and contrasted with the pre-electronic precursors, and testable implications for market quality are developed. Studies that provide a theoretical foundation for understanding the impacts of HFT and their empirical implications are discussed next. The remainder of the paper surveys the empirical literature. Sections on bid-ask spreads, posted liquidity, trade size, market efficiency, profitability and volatility are presented. The separate impacts on market quality of latency and messaging rates are then discussed, followed by some concluding remarks. At its essence, the purpose of a marketplace is to facilitate buying and selling, and the quality of a market is often expressed as the degree to which this is accomplished without imposing additional costs or frictions on market participants. There are many ways to measure the quality of markets. This paper surveys recent empirical studies relevant to the impact of HFT and other modern trading practices on market quality. The U.S. equity markets are large, liquid markets that led the rise of automated trading and as such, much of our discussion focuses on these markets. Each study takes a unique approach, often using different definitions of, or proxies for, HFT, which together paint a consistent picture of markets being improved by competition and automation. 5

6 Pre- Electronic Trading and Institutional Investors In order to understand the impact of the automation of markets and the growth of HFT, it is helpful to consider earlier market structures. In the 1960s the NYSE s dominant market share and its system of exclusive specialists, floor brokers, floor traders, fixed commissions, and minimum tick sizes of 1/8th of a dollar, were put under pressure by the growth in the size and turnover rates of institutional shareholdings. One critique of modern HFT is that it somehow disadvantages institutional investors by exacerbating the market impact of their large orders. It is important to recognize that similar concerns have been expressed for a long time, going back well before automated trading. The SEC study of institutional investors during the late 1960s (the Institutional Study ) found that sales of large blocks of stock often resulted in a large price impact (Kraus & Stoll 1972). In an attempt to mitigate the price impact, floor brokers often worked larger orders throughout the day, but their physical presence on the floor often revealed to floor traders and specialists that they were engaged in a large buy or sell program. During this era, there were concerns that being close to the center of price discovery benefited certain market participants at the expense of institutional investors. An SEC special study of floor trading on the NYSE 6

7 (the Special Study ) during the early 1960s observed that: familiarity with the trading techniques of specialists or floor brokers...combined with knowledge that a large block of stock is being accumulated or distributed... facilitates the trading activities of the floor trader (Smidt 1985, p. 78). The Special Study further noted that NYSE members having access to the floor were able to observe trading activity minutes before it was printed on the tape and bids or offers in a matter of seconds (Ibid, pp ). It found that floor traders are generally buyers in rising markets and sellers in declining markets, with respect to both the market as a whole and to individual stocks (Ibid, p. 77). While the Special Study viewed this behavior as destabilizing and proprietary floor trading was subsequently prohibited, Smidt noted that: The special study's use of the term destabilizing to describe the price effects of stock exchange floor trading implies that in the absence of this trading the price might not have moved and that with the trading a temporary movement occurs that is subsequently reversed. Nothing in the systematic data in the various SEC studies supports either of these implications of the word destabilizing. The evidence is also consistent with the hypothesis that floor trading accelerates price movements that would otherwise have taken place more slowly (Ibid, p. 78). 7

8 These differing interpretations sound familiar to modern ears, as the HFT discussion has brought up similar questions about whether faster price discovery is better and how such price discovery impacts investors. As an alternative to gradually implementing a large buy or sell program on the exchange floor, large orders by institutions were often arranged through block positioners at a price negotiated off the floor of the exchange and then crossed on the exchange. The negotiated price reflected a discount (on a sell order) or a premium (on a buy order) and the institution paid a double commission for the services of the block positioner. Kraus & Stoll (1972) dichotomized the price impact of a block trade into a permanent component, which they denoted an information effect, and a transitory component, which they denoted a distribution effect. The price at which these sell blocks were traded was, on average, 1.14% less than the specialist s bid immediately prior to the trade. The stock prices then recovered 0.71% by the end of the trading day (Ibid, Fig. 1). They interpreted the recovery as a distribution effect that may be viewed as a transitory pricing error that reverted to zero by the end of the trading day. The 0.43% difference between the initial price drop and the subsequent recovery was viewed as the information effect and is a permanent price impact of the block sale. Several of the studies surveyed in this paper 8

9 showed that this distribution effect has declined substantially contemporaneous with the adoption of automated and competitive trading. In a 1969 letter to the SEC, the leading third market firm, Weeden & Co., commented on types of trading possible with technology: With today s electronic miracles available to the industry, all market makers wherever located could be combined into a central, interrelated market for fast and efficient access by investors to all of its segments. The true central marketplace demands access to all available pools of positioning capital for maximum liquidity (Weeden 2002, p. 50). The evidence surveyed in this paper bears out these predictions by showing that electronic miracles have indeed tied together numerous sources of liquidity from diverse locations to the benefit of investors. Leveling the Playing Field Despite these early concerns and sporadic reforms, by the mid 1990s, the vast majority of trading in NYSE-listed and Amex-listed stocks was still done manually by traders on trading floors. The vast majority of trading in NASDAQ stocks was facilitated by electronic trading platforms, but still implemented through predominantly manual keyboard entry. 9

10 In subsequent years, trading became a highly automated process and trading transitioned from a small number of markets, each with dominant market share in trading of its own listings, to a system of multiple exchanges and alternative trading systems (ATSs) competing for market share. Quoting increments reduced from eighths and quarters to sixteenths and ultimately pennies. The role of professional intermediaries changed from a small number of firms (i.e., exclusive specialists, floor traders and block positioners associated with the NYSE and NASDAQ market makers) whose business models were capital and relationship intensive and who were shielded from competition by regulation and exchange rules, to dozens or hundreds of trading firms competing in more open and transparent markets. As a general matter, the markets for NASDAQ stocks made these transitions first, mostly between 1996 and 2004, while the markets for NYSE-listed stocks did not see their biggest changes until the implementation of Regulation National Market System (Reg NMS) beginning in For NASDAQ stocks, market automation was originally driven by institutional customers who, concerned about the conflicts of interest and expense of executing large orders through dealers, gravitated to early-stage systems that offered an automated alternative. The earliest and most predominant of such systems was Instinet (formerly Institutional Networks), which, after 10

11 being founded in 1969, began to gain critical mass in the late 1980s. Select non-institutional participants were also provided with access to the platform and by early 1996, Instinet accounted for over 15 percent of all trading volume in NASDAQ stocks (U.S. SEC 1997). Instinet s increasing popularity and institution-oriented business model prompted regulatory concern over the existence of a two tier market that could prevent smaller investors from receiving the best possible prices for their stock trades. In addition, concern over anti-competitive market practices also spurred regulatory reform intended to make markets more competitive. An antitrust suit initiated by the U.S. Justice Department against NASDAQ market makers in 1996 was motivated in part by a highly publicized academic study by Christie & Schultz (1994) that suggested that the consistent avoidance of odd-eighth quotes by NASDAQ market makers was indirect evidence of collusion. The antitrust settlement and several major SEC regulatory rulings prompted by this suit provided additional catalysts for the evolution of greater price transparency, automated high speed order matching and increased competition between trading venues and liquidity providers. In 1996 and 1997, the SEC adopted the Order Handling Rules that were intended to enhance the quality of published quotations for securities and promote competition and pricing efficiency. NASDAQ market makers were 11

12 effectively required to include price quotes from electronic communication networks (ECN) into their quotations. As ECNs and other alternative trading systems (ATS) began to develop, the SEC adopted Regulation ATS in 1998 to bring these venues under regulatory control. These rules integrated ECNs and other ATSs into the national market system. This period saw the growth of alternative trading systems Instinet, Island, Archipelago and BRUT that provided faster and less expensive venues for traders and investors. The Island ECN, established in 1997, was particularly influential in shaping market structure. Island offered an innovative fee structure (later referred to as maker-taker pricing) that encouraged the posting of resting orders. In addition, it provided free efficient market data feeds containing full orderbook information as well as low-latency, automated order entry protocols. It also was the first market to offer co-location through which Island s customers could decrease latency by having their computers located in the same building as Island s matching engine. Within a few years, many of these features became commonplace in the US equity markets and in other electronic markets around the world. In early 2001, Congress mandated that U.S. equity markets transition from fractional to decimal pricing for stocks, a change referred to as decimalization. This change dramatically reduced the minimum tick 12

13 increment for stocks, allowing finer-grained pricing and smaller bid-ask spreads. As the U.S. equity markets opened up and modernized, new ECNs gained market share and both NASDAQ and (to a lesser extent) NYSE automated their market data and order entry systems. These changes provided catalysts for the establishment during the late 1990s and early 2000s of independent proprietary trading firms like Quantlab Financial, Getco, Tradebot, RGM Advisors, Hudson River Trading, EWT, Sun Trading and Allston Trading. These firms and dozens of others would soon compete with more established trading desks at investment banks and other trading firms like Knight and Citadel to reshape the role of professional market intermediaries. By early 2005, ATSs accounted for over half the trading volume in Nasdaq stocks, but only a small fraction of NYSE-listed volume (Jickling 2005, p. 2). Island and Instinet merged and the combined company, re-named Inet, took a 25% market share in trading of NASDAQ stocks, but only 1% of the trading volume in NYSE-listed stocks (Ibid, p.2). The overwhelming majority of NYSE-listed stock trading still occurred manually through the facilities and on the trading floors of the NYSE. While other exchanges and markets offered NYSE-listed trading, a number of rules and practices limited 13

14 competition. Competition from ATSs was disadvantaged by an antiquated trade through rule that required that all orders in NYSE listed shares be exposed to the floor for a 30-second period for price improvement (Ibid, p. 3). This 30-second window gave the specialist and floor brokers a collective option to accept the trade with a penny price improvement and negated the advantages of ATS trading, which are speed, anonymity, and certainty of execution (Ibid, p.30). In 2004, the NYSE's seven specialist firms, whose employees matched buy and sell orders on the floor, paid $247 million to settle regulatory claims that they violated trading rules (Ortega 2006). This settlement provided further impetus for regulatory reform that would open up the trading of NYSE listed stocks to greater competition. Reg NMS was approved by the SEC in 2005 and fully implemented by early 2007, effectively leveled the playing field for ATSs and other exchanges to compete with the NYSE in the trading of NYSE-listed stocks. Under the revised order protection rule, priority was given to the national best bid or offer (NBBO) available immediately (less than a second) and automatically. Some of the stated goals of Reg NMS (U.S. SEC 2005) were to foster intermarket competition, to improve confidence that investors are being treated fairly, to decrease transaction costs and to improve market stability and 14

15 liquidity. The studies surveyed by this paper suggest that at least in the areas of inter-market competition, transaction costs, market stability and liquidity, the markets have achieved many of the SEC s objectives. Characteristics of HFT We now describe general characteristics of various types of HFT strategies, which encompass more traditional notions of market making, directional trading, arbitrage and relative value trading. These descriptions, however, are necessarily overly simplistic. Actual strategies deployed by modern traders tend to incorporate various aspects from multiple categories. Nonetheless, we believe that describing the stylized versions of some of these strategies may help readers to interpret some of the evidence discussed in this paper. Some HFT strategies trade primarily using resting orders, often quote twosided prices and rapidly adjust their quotes in response to market conditions. This style of trading is often referred to as market making. In theory, these strategies earn a gross profit from bid-ask spreads, which is partially offset by losses on their inventories due to adverse selection resulting from their quotes being traded with by those with better information (i.e., informed traders). In practice, bid-ask spreads (including any rebates or fees), adverse selection, other transaction costs and 15

16 positioning are all important factors in the profitability of strategies relying on resting orders. Traders that use resting orders do not choose when they trade, but instead provide options to other market participants to trade with them. Adverse selection is mitigated by limiting the size quoted at the inside, quoting at multiple price levels and rapidly adjusting or removing price quotes in response to order flow, price, and volume changes. As such, messaging rates for strategies that use resting orders tend to be relatively high as these traders rapidly adjust their quotes to reflect changing market conditions and risk exposure. Other HFT strategies, sometimes referred to as directional strategies, trade primarily with marketable orders, which differ from market orders because they have a price limit and are intended to interact immediately with resting orders. Directional strategies include mean reverting strategies that attempt to profit from transitory pricing errors and momentum strategies that attempt to profit from trends. Profits are generated when asset prices move favorably and sufficiently to exceed execution costs. The use of marketable orders implies a lower average messaging rate than trading with resting orders. The stylized distinctions between market making and various forms of directional trading are useful in interpreting some of the empirical research 16

17 surveyed in this paper. For example, some tests use messaging rates as a proxy for HFT activity, while other studies measure separate effects of HFT marketable and resting orders. However, in practice the distinctions between market making and directional trading are less clear. For instance, market makers often use directional predictions to adjust their quotes to mitigate the impact of adverse selection and often use marketable orders to manage positions. Conversely, directional traders may enter or exit positions using resting orders to reduce execution costs, and rapid adjustment of these resting orders are often required in response to market activity. Arbitrage and statistical arbitrage could involve entering positions through quoting strategies that resemble market making or by crossing bidask spreads with marketable orders based on directional predictions. Theory and Testable Implications for Market Quality While this paper focuses primarily on empirical evidence, there have been some recent studies that attempt to develop a theoretical basis for better understand the impacts of high frequency trading. Gerig & Michayluk (2010) developed a model that can explain several characteristics of high frequency liquidity provision: why we should expect this type of trading to exist, why firms with highly skilled employees dominate the space, why they trade in large volumes, why prices are more 17

18 efficient as a result of their trading, and finally who benefits from their presence. They extended the well known Glosten & Milgrom (1985) model of traditional market making by including multiple securities and automated liquidity provision. They assumed that automated liquidity providers use price and volume information about all securities in contrast to traditional market makers who use only information about the individual security they trade. They contended that this assumption captures the main advantage that machines have over their human counterparts: they can quickly and accurately process large amounts of relevant information when setting prices (Ibid, p. 2). These liquidity providers trade with both informed traders, who are able to correctly value specific securities, and uninformed traders. They showed that automated market makers are better able to distinguish informed from uninformed order flow thereby setting prices more precisely; and that traditional market makers are not able to compete. Finally, they showed that extending their model to allow uninformed investors to trade more readily when their transaction costs are low,...[results in] increased trading volumes and decreased overall transaction costs (Ibid, p. 4). The predictions made by their model are largely consistent with the empirical data surveyed. Some authors have recently developed theoretical models that suggest a positive association between HFT trades and short-term price movements is 18

19 evidence of negative externalities borne by low frequency traders. For example, Jarrow & Protter (2011) assumed that individual high frequency traders behave as price takers and observe a common signal. In their model, the collective response to the signal generated a pricing error as high frequency traders drive prices away from fair value. This type of model could be useful in analyzing crowded trades and in understanding the significant losses experienced by low frequency quantitative strategies in August 2007, which is discussed in Khandani & Lo (2007). However, this model assumes that the high frequency traders transact at precisely the same instant and that the stock price only moves after their orders are filled (Jarrow & Protter 2011, pp. 3-4). In reality, the result of large numbers of traders reacting to a common signal and targeting the same posted liquidity with marketable limit orders priced at the inside would be a very low fill rate. If high frequency traders were to use market orders without regard to price then their collective impact could indeed cause a transitory pricing error; however, they would not be able to fill their entire order prior to the price moving. This would imply a positive association between HFT marketable order flow and transitory pricing errors, and mean reversion in mid-market price movements. The authors argue that high frequency traders create abnormal profit opportunities that they exploit to the disadvantage of the ordinary investors (Ibid, p. 3). They 19

20 seem to imply that the reaction of slow investors to the HFT order flow would create transitory price trends, which would imply subsequent mean reversion after the transitory trends. These predictions are not supported by our review of the empirical literature in subsequent sections. Biais, Foucault & Moinas (2011) postulated a theoretical model in which high frequency traders could process information faster than slow traders and in which high frequency trading involved large fixed costs. They showed that in such a model, small institutions who chose not to invest in HFT technology would be less well informed and ultimately would exit the market. This model does not distinguish the trading objectives of different classes of market participants, such as long term investors who seek returns over periods of months or years and high frequency traders who seek returns over seconds or minutes. For example, Hendershott & Riordan (2012, Fig. 1) show that HFT net order flow does not have significant predictive content beyond 10 seconds; in contrast, Bennet, Sias, & Starks (2003) found that quarterly institutional order flow is positively correlated with future quarterly stock returns. The Biais, Foucault & Moinas (2011) analysis would predict that net HFT order flow should predict returns over intervals measuring longer than seconds, which is not consistent with our subsequent review of the literature. Finally, 20

21 their predictions that small institutional investors are disadvantaged assumes that HFT technology is only available to institutional investors at a large fixed cost. In reality, execution algorithms that use all of the tools of HFT are available to small and large institutions on a variable cost basis from institutional brokers. HFTs and longer-term investors are not in competition with each other. They use completely different types of information and predict returns over very different horizons. Indeed, HFT firms can be thought of as competing among themselves to provide liquidity and price discovery services to longterm investors. The empirical evidence surveyed in subsequent sections suggests that this competition is working to the benefit of long-term investors. Bid- Ask Spreads The bid-ask spread is an important component of the cost of trading and, all else being equal, smaller spreads are evidence of a better cost structure for investors. Conversely, market makers and other traders using resting orders generate revenue through earning spreads. There are many ways to measure bid-ask spreads, including quoted spreads and effective spreads. No matter the specific version measured, however, the evidence suggests 21

22 that bid-ask spreads have declined dramatically over recent decades as markets have become increasingly competitive and automated. Moreover, several studies provide evidence that algorithmic trading or HFT is at least partially responsible for these improvements. The quoted spread is most relevant for trade sizes that do not exceed the amount available at the inside bid and offered prices. Therefore, for individual investors, the quoted bid-ask spread is typically the dominant component of transaction costs. For institutional investors with larger orders however, a more comprehensive view of liquidity is important. For these market participants, bid-ask spreads should be evaluated along with the amount of posted liquidity, which is examined in the next section, and transitory price impact, which is examined in following sections. Quoted spreads are typically measured in absolute terms as the difference between the best ask price and the best bid price or in relative terms as the absolute spread over the midpoint price. This measure represents a cost averaged over all times, which may not accurately reflect the actual spread that a trader experiences at the times they trade. Effective spreads attempt to account for the actual spread at the time of a trade, thereby more accurately reflecting actual trading costs. It is typically defined as twice the absolute difference between the price of a trade and the midpoint price at 22

23 the time of the trade. This metric is available through SEC Rule 605 reports, with the midpoint price computed from the NBBO at the time of order receipt. As we survey in this section, numerous studies show that over the past decade effective spreads for large, and mid/small cap stocks have narrowed. Studies of both quoted and effective spreads showed that they have decreased substantially in the U.S. and reached historically low values in 2010 and The SEC order handling rules, the growth of competitive ECNs in the late 1990s and decimalization in 2001 coincided with a decline in average effective spreads on the NYSE over the period as shown in Figure 1 below which is reproduced from Chordia, Roll & Subrahmanyam, (2008, p. 256). Since the NYSE s automated execution and quoting systems (Direct Plus, Autoquote and the Hybrid market model) were introduced after this period, most forms of automated trading and HFT in NYSE-listed stocks were not yet practicable. Thus, these early changes in effective spreads cannot be directly attributable to automated order matching or HFT, and may be primarily due to the reduction in tick increments. In response to Reg NMS, the NYSE introduced its Hybrid market model in 2006, with subsequent technology improvements made over the following years. This less restricted, electronic trading system made automated 23

24 trading more feasible. At the same time, NYSE market share in the trading of its own listings declined dramatically, from nearly 80 percent in early 2006 to about 25 percent by the middle of 2008 and remained relatively constant thereafter (Angel, Harris & Spatt 2010). This lost market share was picked up by competitive, electronic exchanges and ECNs that already had high degrees of HFT participation. Thus, the period after 2006 is more relevant to assess the impact of electronic trading platforms and HFT on bidask spreads and other measures of market quality for NYSE-listed stocks. In contrast, conditions suitable for automated trading in NASDAQ stocks were prevalent since the early 2000s. Figure 2 shows that from 2006 to 2009 the average effective spread for NYSE-listed stocks dropped more significantly than for NASDAQ stocks. Thus, the introduction of automated order matching and gradual improvements in execution times that facilitated HFT and other forms of automated trading in NYSE-listed stocks coincided with a decline in effective bid-ask spreads. The upward spike in spreads in 2007 and 2008 relates to the high volatility during the financial crisis. Figure 3 plots, for the time period, the means of the quoted spreads for the large cap stocks included in the Russell 1000 partitioned into its NYSE and NASDAQ components and for mid/small cap stocks included in 24

25 the Russell 2000 partition into its NYSE and NASDAQ components. The large cap stocks percentage decline in mean of the quoted spread between 2006 and 2009 was greater for NYSE-listed stocks than for the NASDAQ stocks. In contrast, the percentage decline for mid/small cap stocks was similar. The minimum tick increment of $0.01 for most U.S. stocks limits how low these spreads can go, and significant further declines are unlikely without reductions to tick increments for the most liquid and low priced stocks constrained by the penny tick. For a large trade the difference between an effective spread and a quoted spread is influenced by the impact of the trade on the mid market price. In the next section, we show that the liquidity posted within a 6-cent band around the NBBO has increased by a much higher percentage for large cap stocks than for mid/small cap stocks. In the market efficiency section, we show that a greater reduction in the transitory price impact has occurred for NYSE-listed than for NASDAQ stocks. Thus, the greater percentage decline in effective spread for NYSE stocks relative to NASDAQ is not solely attributed to the greater percentage decline in quoted spreads for large cap NYSE stocks relative to NASDAQ stocks. Market makers adjust their quoted spreads in response to changes in market volatility; therefore, the time series plot of quoted spreads is smoother and the decline more dramatic when adjusted for the level of volatility. Castura, 25

26 Litzenberger, Gorelick & Dwivedi (2010) regressed quarterly spreads on the CBOE Volatility Index (VIX) and removed the difference in spreads explained by difference in the VIX. This was updated to include new data in Castura, Litzenberger & Gorelick (2012). This more clearly showed trends in spreads that were not influenced by macroscopic volatility. The VIX-adjusted spreads are in shown in Figure 4 for the NYSE-listed Russell 1000 and Russell 2000 stocks. This evidence is consistent with the theory that technology enhances the ability of liquidity providers to manage risk, and enables them to quote smaller spreads as posited in Gerig & Michayluk (2010). There have been a number of studies that present more direct evidence of a link between automated trading and the reduction in spreads. These papers consistently find a positive relationship between automated trading activity and narrower bid-ask spreads, a representative group of which are discussed below. Consider first the introduction of the NYSE s autoquote system in This limited step towards automation enabled some forms of automated trading. Defining Algorithmic Trading (AT) as the use of algorithms to submit and cancel orders, Hendershott, Jones & Menkveld (2011) investigated the rise of AT over a 5-year period spanning 2001 to 2005, with emphasis on the 26

27 introduction of the autoquote system and its impact on various measures of spreads. Electronic message traffic on the NYSE was used as a proxy for AT activity. This metric was normalized to a per $100 of trading volume value in order to mitigate the effect of rising volumes. The authors used the TAQ and CRSP databases as their data sources. Since the rollout of the autoquote system was staggered over several months with subsets of stocks transitioned at different times, the authors were able to control for idiosyncratic and other contemporaneous events that may have confounded the causal relationship. Adopting a panel regression framework, the authors found that quoted spreads and effective spreads decreased by a significant amount due to increasing AT. The reported decline in quoted spreads was consistent across all market-cap quintiles, halving over that time period. Similarly, they found a significant reduction in share-volume-weighted effective spreads across all market-cap quintiles, with most quintiles falling to a third of their beginning level. Direct evidence of a causal link between high frequency market making and the narrowing of spreads on Dutch stocks is provided in Menkveld (2011). He analyzed the impact on spreads of the establishment of an electronic pan-european stock market, Chi-X, in 2007 and the contemporaneous commencement of market making activities in Dutch stocks by a single high frequency market maker in The competitive impact of Chi-X was 27

28 demonstrated by the rapid growth in its market share in Dutch stocks at the expense of the incumbent NYSE-Euronext. He showed that Chi-X s growth in market share was attributable to the activity of the high frequency market maker. This high frequency market maker participated in about 40% of all trades in Dutch stocks on Chi-X in the 14-month sample period (9/07 11/08) and 14% of all trades in Dutch stocks across both markets. Menkveld found that the market maker used resting orders in 80% of its trades, that its net position between Chi-X and Euronext fluctuated around zero several times per day, and the firm typically ended the day flat. Chi-X began trading Dutch stocks about one year prior to trading Belgian stocks, while both traded throughout this time period on Euronext. Thus, the high frequency market maker could not trade Belgian stock on Chi-X over this year. During this time, and coincident with the high participation rates of the new market maker, bid-ask spreads in Dutch stocks fell by 50% relative to Belgian stocks. This is interpreted as direct evidence that high frequency market making reduces bid-ask spreads. The direct role of quoting by multiple HFT firms on improving the NBBO (quoted bid-ask spreads) was studied by Brogaard (2011a). His study was based on millisecond time-stamped orders for a stratified random sample of 120 stocks consisting of 60 NASDAQ stocks (20 small, mid and large cap buckets) and 60 NYSE-listed (chosen from the same size buckets). For this 28

29 sample of stocks proprietary transaction data was provided for all trading days in 2008 and 2009 as well as 2/22/10 to 2/26/10. Data for the same set of stocks was separately provided by NASDAQ and BATS, with high frequency trader flags set on trades and quotes which the respective exchanges believed came from a group of firms that they understood engaged primarily in HFT. The non-hft group certainly contained firms that engaged in algorithmic or HFT, e.g., large institutional firms that engage in both HFT and non-hft were part of the non-hft group. The sample, therefore, may not be representative of all HFT participants, and may possibly mis-classify participants depending on the criteria used to identify the HFT firms. The NASDAQ dataset (referred to subsequently as the NASDAQ-HFT dataset) identified 26 HFT firms which met criteria specified by NASDAQ as representative of HFT behavior; volume traded, order duration, position turnover and order cancellation ratios were all used. The BATS dataset (referred to subsequently as the BATS-HFT dataset) identified 25 HFT firms using similar criteria, though it is unknown the extent to which the HFT firms chosen by BATS overlap those from the NASDAQ-HFT dataset. Looking at resting orders, Brogaard measured how often HFT resting orders were priced at least as favorably as non-hft resting orders. He created two 29

30 separate books, one consisting only of HFT resting orders, the other consisting only of non-hft resting orders. Comparing the two books and looking at the NASDAQ data, he found that HFT matched or improved the non-hft inside prices 65% of the time and HFT strictly improved the non- HFT inside prices 19% of the time, ultimately resulting in a tighter bid-ask spread. For BATS, HFT matched or improved the non-hft inside price 55% of the time, and strictly improved the non-hft inside price 26% of the time. Partitioning the data by market capitalization, Brogaard showed that HFT appeared more active on large cap stocks. On NASDAQ, HFT matched or improved the non-hft inside prices 83% of the time and strictly improved non-hft inside prices 14% of the time for large cap stock, while the numbers, respectively, are 51% and 22% for small cap stocks. Since many large cap stocks have small spreads (at or near the minimum tick increment of $0.01) it may be more difficult (or impossible) to strictly improve. These results, along with other results that Brogaard provides, show that HFT acts in such a way that spreads are narrowed and liquidity is improved. The evidence suggests that bid-ask spreads have declined over the post period that also saw dramatic growth of HFT. Several studies have shown that technological improvements are associated with narrower spreads. This empirical data is consistent with the hypothesis that the 30

31 improved ability of short-term liquidity providers to mitigate the adverse selection (associated with informed liquidity takers) through high speed adjustment of their quotes in response to market information allows them to prudently quote tighter spreads. The Menkveld study demonstrated the direct impact of a single, large high frequency market maker on a new trading venue on the narrowing of spreads on the established exchange. The posting of tighter spreads by the high frequency market maker and competition among exchanges resulted in a halving of spreads. The Brogaard study, which identified HFT orders on NASDAQ and BATS, demonstrated that the use of resting orders by HFT directly narrowed spreads in about 20% of their resting orders for the U.S. stocks. This is consistent with other studies, including Hasbrouck & Saar (2011), which linked episodes of high low-latency activity with smaller spreads, and Lepone (2011) who found that HFT activity on the Australian Stock market (ASX) resulted in smaller spreads. Together, there is a strong body of evidence suggesting that HFT acts in such a way that reduces bid-ask spreads. While this may be the case, an often cited concern is that liquidity may be impaired, transitory price impacts may be more prevalent or liquidity may be fleeting, resulting in less resilient markets in times of stress. We investigate changes in posted liquidity in the 31

32 following section and survey evidence that suggests liquidity has improved with growing HFT activity. Posted Liquidity Liquidity is an important indication of the quality of a market. The ability for trading participants to obtain desired inventory positions with minimal transitory price impact and small execution costs is the essence of market liquidity. We review studies that examine trends in liquidity in the U.S. equity markets and the impact HFT has on this market quality measure. Findings suggest a strong, positive relationship between HFT and improved posted liquidity measures. One common measure of liquidity is the number of shares available to trade at the cross-market inside market, referred in the U.S. equity markets to as the National Best Bid and Offer (NBBO). Credit Suisse showed that the median displayed depth at the NBBO for the Dow Jones Industrial 30 stocks increased by 75% between 2006 and 2010 (Avramovic 2010). Their findings for these large cap stocks are consistent with the findings of Angel, Harris & Spatt (2010) who showed that the median displayed depth at the NBBO rose by about 50% across all stocks from 2006 to The growth rate was higher for large cap stocks than for small cap stocks. The median displayed 32

33 depth at the NBBO increased by 125% for the S&P 500 stocks. In contrast, the median displayed depth for the Russell 2000 increased by about 30%. The authors also showed the depth available within the first six cents from the inside and see the same trend toward increased posted liquidity. For stocks with a penny spread, a six-cent band on both sides of the NBBO is comparable to the historical 1/8 th dollar minimum spread. The median depth within the six-cent band is more than an order of magnitude greater than available at the NBBO, and shows substantial growth in the liquidity available for institutional investors requiring immediacy for large orders. The graphs in Figures 5 and 6 shown below indicate the growth in quoted liquidity has increased substantially for large cap stocks over the same years that HFT trading in those stocks has grown. For small cap stocks, liquidity improved, but the growth was more moderate. For most S&P 500 stocks a marketable order substantially greater than 10,000 shares and a value above $1 million can be executed within the 1/8 th dollar minimum spread that was common 15 years ago. The institutional investor study commissioned by the SEC in the late 1960s considered trades larger than 10,000 shares with a value greater than $1 million to be a large block whose sale required the services of an upstairs block positioner to arrange off the exchange floor (Kraus & Stoll 1972). The greater percentage improvement in liquidity for the larger cap stocks is consistent with the result that 33

34 effective spreads have improved more for NYSE-listed stocks than the NASDAQ stocks. The above liquidity metric does not account for cross-sectional differences in stock prices. For example, 20,000 shares of a stock selling at $30 correspond in dollar amounts to 2,000 shares of a stock selling at $300. The average dollar amount of stock quoted is a better measure of liquidity. Clearly, an investor considering allocating a portfolio to different stocks would need to translate available liquidity into dollar amounts. Moreover, value based measures should be expected to be more meaningful across events such as the Citigroup, Inc. 10:1 reverse stock split in Castura, Litzenberger, Gorelick & Dwivedi (2010) measured available liquidity as the dollar amount at the NBBO at any instant in time, and averaged over each quarter. Castura, Litzenberger & Gorelick (2012) updated the results to include data from Figures 7 and 8 graph the average dollar amount of liquidity for the Russell 1000 and 2000 components, respectively, partitioned into NYSE-listed and NASDAQ stocks. Both graphs show a reduction in available liquidity during the financial crisis of 2007/2008 (an expected outcome during periods of high volatility) and a movement to historically high levels of available liquidity by

35 Using the NASDAQ-HFT dataset, Brogaard (2011a) found that resting orders of a designated group of 25 high frequency traders participated in 41.1% of dollar volume traded, while their marketable orders participated in 42.0% of dollar volume traded. HFT was a net supplier of liquidity across both exchanges. Additionally, Brogaard showed that HFT increased the inside dollar value at the inside on NASDAQ and BATS by about 50% on average over all stocks. Trade Sizes While the amount of liquidity posted at or near the NBBO has increased, individual trade sizes have actually decreased. Angel, Harris & Spatt (2010) also showed that the mean trade size has decreased from 2004 through 2009, falling from about 700 shares in 2004 to about 300 shares in This decrease is consistent with institutional investors fragmenting large trades rather than negotiating trades (off the exchange floor) with a block positioner. Institutional investors frequently use computer algorithms to break up large trades in order to gradually acquire or liquidate positions. These algorithms are analogous to floor brokers of by-gone days working large orders on the exchange floor. 35

36 Kraus & Stoll (1972), who were staff economists on the institutional study commissioned by the SEC in the late 1960s, provided an interesting look into the future: There appears to be a cost to the seller over and above the commission charge, which is particularly evident in the within-day price return. This cost may be reduced if more investors are given the opportunity and incentive to participate in blocks. Elimination of the fixed minimum commission and permitting and encouraging competing specialists would be steps in the right direction (Kraus & Stoll 1972, p. 588). There are now highly competitive liquidity providers, no fixed commissions, and institutions only infrequently resort to block sales. Under this evolved market structure, institutions frequently break blocks into smaller trade sizes, thereby giving more investors the opportunity to compete and mitigating the transitory price impact. This order-splitting is possible because of a competitive fee structure and intra-day liquidity and price discovery services provided by HFT. If this current market structure reduces distribution costs, mean reversion in stock prices should be less and transitory price impacts should be mitigated by the liquidity provided by HFT. However, the permanent component should be rapidly reflected in the 36

37 stock prices. The rapid adjustment of prices to the information contained in large position changes by informed traders would level the playing field for less informed traders. One manifestation of smaller trade sizes is investigated by O Hara, Yao & Ye (2011), who used the NASDAQ-HFT data set to examine how the HFT participants use odd-lots in their trading. Because odd-lots are not protected quotes under Reg NMS, they do not appear in consolidated market data feeds such as the Consolidated Quote System (CQS). They found that odd-lots accounted for a substantial fraction of all trades, particularly in high-priced, low-liquidity stocks. As an example, they found that 35% of all trades in Google (which traded between $300 and $600 in the data sample) were odd-lots. They also found that odd-lots accounted for 30% of price discovery, which suggests that a significant amount of informed trading is not seen in the consolidated feeds. HFT was found to be more likely to use odd-lots. Since the marginal cost of automated order processing and submission is so small, the cost to split order flow has become more economical, thereby resulting in more odd-lots and smaller trade sizes. It may be that as markets have become increasingly automated and efficient, the regulatory distinction between odd and even lots has become anachronistic and all quotes and trades should be treated equivalently, regardless of size. 37

38 Order splitting and the increasing use of odd-lots are natural consequences under the conjectures of Kraus & Stoll. The next section examines the empirical evidence concerning these conjectures, and shows that the price discovery process has improved in conjunction with these changes. Market Efficiency In addition to bid-ask spreads and liquidity, institutional investors with large trades are also concerned about the price impact of their trades. We previously noted that the price impact of a large trade is often viewed as consisting of a permanent component and a transitory component. If a market is both liquid and efficient, there should be little or no transitory price impact as a result of a large trade. The extent to which prices exhibit mean reversion then represents a cost to trading for these large traders. In a classic contribution to efficient market theory, Samuelson (1965) showed that properly anticipated prices should be indistinguishable from a random process. Intuitively this makes sense since any predictability in prices will be acted upon to the extent that it is profitable to do so, thereby driving prices toward apparent randomness. Mean reversion as a result of trading activity represents an inefficiency in this context as the negative 38

39 autocorrelation in the price time series would be a predictable effect. Many studies directly measure auto serial correlations of monthly, weekly and daily returns. This approach requires a specification of the appropriate lag structure, which would be difficult for high frequency data. A pioneering, empirical study by Lo & Mackinlay (1988) sidestepped the need to specify a lag structure by using a variance ratio test. A variance ratio test uses the fact that a random walk has a variance that is proportional to the time interval used in the measurement. By taking a ratio of variances computed using two different time intervals, it is possible to detect autocorrelation in the returns of a stock. They provided a test statistic that is robust to heteroscedasticity and non-normality, two common features of financial time series. Using daily return data their variance ratio tests rejected the random walk hypothesis. Their sample period ended in 1988, prior to the structural and regulatory changes that have dramatically changed the U.S. equity markets, and so may no longer be representative of modern markets. A study of high frequency returns measured in minutes and seconds is a better way to access the impacts of automation of trading and the growth HFT. A study by Castura, Litzenberger, Gorelick & Dwivedi (2010), updated in 2012 to include data through 2011 (Castura, Litzenberger & Gorelick 2012), 39

40 applied the variance ratio tests to high frequency return data, examining the ratio of the variance of log returns measured over 1-second observations with the variance of log returns measured over 10-second observations for the 6 years between 2006 and To avoid the impact of bid-ask bounce -- the problem that trades do not occur at exact 1-second intervals, and the problem of the absence of trades in some 1-second intervals -- the returns were calculated using mid-market prices (means of NBBO prices). The intuition for the variance ratio test is straightforward. Under a random walk, log returns observed over 1-second time intervals would have zero auto correlations for 1 to 10-second lags. The log of a 10-second return is the sum of the logs of the ten 1-second returns that it spans, and absent autocorrelation its variance is equal to the sum of the variances of the 1- second returns. Thus, under a random walk, an estimate of variance per unit of time would have the same expected value whether estimated using a time series of 1-second returns or a time series of 10-second returns. However, under a random walk any finite sample of 1-second returns could by chance display either negative or positive sample autocorrelation. Therefore, it is appropriate to test whether any differences between variance per unit of time estimates based on 10-second returns are statistically different from those based on 1-second returns. 40

41 Consider the types of market imperfections that could cause such differences. First, consider a possible imperfection that could result in a variance ratio less than unity. If marketable orders exceeding the inside size cause temporary price pressure impacts, subsequent reversals would result in negative autocorrelations and the variance of 10-second return observations would be less than 10 times the variance of the 1-second returns observations. Under these conditions, the ratio of estimates of variance per unit of time based on 10-second observations to those based on 1-second observations would be less than unity. Conversely, under asymmetric information, informed traders could cause price momentum and positive autocorrelations as they gradually traded in or out of large positions. This would result in the corresponding variance ratio being greater than unity. The sample of stocks used by Castura, Litzenberger, Gorelick & Dwivedi consisted of 1000 large cap stocks (the Russell 1000 components) and 2000 mid and small cap stocks (the Russell 2000 components). The stocks included in these indices were taken as of Q Both the Russell 1000 stocks and the Russell 2000 stock were sub-partitioned into two sets; NYSElisted stocks and NASDAQ stocks. For the entire 6-year period studied, the NASDAQ stocks traded on low latency electronic trading platforms with strong competition among platforms. In contrast, the NYSE-listed stocks 41

42 transitioned from being primarily traded manually on the NYSE floor to being primarily traded by competitive, low latency, electronic trading platforms with the NYSE and its affiliates accounting for substantially less than half of the volume of these stocks. Thus, the NASDAQ stocks were effectively a control group for determining the combined impacts on the efficiency of the market for NYSE-listed stocks due to the transition from manual trading to low latency electronic trading, along with increasing competition among electronic trading platforms and the coincident growth of automated trading and HFT. HFT was active in NASDAQ stocks throughout the entire period, while HFT was less active in NYSE-listed stocks initially but grew to a large portion of the total volume as execution speeds and message-rate capacity increased on the platforms trading these stocks, and trading shifted to competitive trading platforms. The log returns were measured for each stock at 1-second, 10-seconds and 1-minute intervals. The first 10 minutes and last 10 minutes of each day were omitted to prevent opening and closing activities from influencing the results. Inside bids and asks were calculated based on composite quote data across the NASDAQ, NYSE, NYSE ARCA, Direct Edge and BATS markets. The volume traded on these venues represented a significant fraction of all shares traded in the U.S. and was representative of overall market activity. 42

43 Figure 9 separately graphs the average 10 to 1-second variance ratios for the 800 large cap NYSE-listed stocks and for the 200 large cap NASDAQ listed stocks. The average variance ratio for large cap NASDAQ stocks was about 0.9 at the beginning of the period and increased gradually to about In contrast, the average variance ratio for the large cap NYSE-listed stocks was initially below 0.6 and increased to over 0.95 by 2009, where it has stabilized through to the end of This again demonstrates the improvement in market quality of NYSE-listed stocks as they transitioned to trading with a more automated, competitive structure. Figure 10 separately graphs the average variance ratios of the groups of mid/small cap NYSE-listed and NASDAQ stocks. The average variance ratios for the group of the mid/small cap NYSE-listed stocks show a greater increase toward 1.0 than the same average for the group of mid/small cap NASDAQ stocks. The average variance ratios for each of the groups of mid/small cap stocks are lower than the corresponding groups of larger cap stocks. The significance test used by Lo & Mackinlay was applied to the variance ratios of the individual stocks and the percentage that were significantly less than 1.0 at the 5 percent level was plotted with confidence bounds based on a binomial sign (Castura, Litzenberger, Gorelick & Dwivedi 2010). For large NYSE-listed stocks the decrease in the percentage of stock having variance ratios significantly below 1.0 rapidly approached 5%, the 43

44 percentage anticipated in an efficient market. The greater upward movement in the variance ratios toward 1.0 for NYSE stock relative to NASDAQ stock is consistent with the greater improvement in the effective spreads for NYSE stocks. The authors found similar results for other variance ratios, such as that for 1-minute over 10-seconds. This is inconsistent with the Jarrow & Protter (2011) conjecture that the 10-second predictive power of net HFT marketable order flow is evidence of transitory pricing momentum. That is, transitory pricing momentum over 10-second intervals, the predictive horizon of net HFT marketable order flow, would predict mean reversion for 10-second returns. These results suggest that the combined impact of the adoption of low latency electronic trading platforms, the increased competition between trading venues and the increased participation of HFT have contributed to improvements in market efficiency and have lowered the transitory price impacts of large trades. In a well known review of empirical research on efficient markets, Fama (1970) characterized tests into three groups: weak-form efficiency tests that examine whether past returns can predict future returns; semi-strong form efficiency tests that examine whether public information can predict future returns; and strong-form efficiency tests that examine whether private 44

45 information can predict future returns. Under this taxonomy the above application of the variance ratio test to 1-second return observations would be classified as a weak-form efficiency test. A possible explanation of the movement of the variance ratios toward 1.0 is that HFT based on public information used to predict returns over short horizons results in a more rapid adjustment to a semi-strong-form efficient price. However, the distinction between public and private information is unclear. The proprietary valuation models of high frequency trading firms, like security analysis by asset managers, effectively converts public information to private information. However, the work of Grossman & Stiglitz (1980) argues that strong-form market efficiency is inconsistent with the search costs associated with analyzing public information. Perhaps competitive interactions of informed traders with different information should be viewed as moving prices to a Diamond & Verrechia (1981) noisy rational expectations equilibrium, in which price is not a sufficient statistic for the information of informed traders and these traders earn normal profits to compensate them for their search costs. As discussed above, a stock s price can be split into a permanent price component and a transitory price component. The permanent component should be viewed as the noisy rational expectations equilibrium price and therefore is modeled as a martingale, hence its changes are serially 45

46 uncorrelated. The transitory component mean reverts toward zero as trading by informed investors results in the asset price moving to its full information, efficient price. This implies changes in the transitory component would be negatively autocorrelated. The Castura, Litzenberger, Gorelick & Dwivedi finding of 10-second variances per unit of time being less than 1-second variances per unit of time is consistent with a transitory pricing error that reverts to zero, and the observed movement in the ratio towards 1.0 is suggestive of its declining importance. The Grossman & Stiglitz and Diamond & Verrechia reasoning would predict that HFT trades are negatively correlated with the transitory price component and positively correlated with future changes in the permanent price component. This argument implies a causal link between the observed increase in the efficiency of the market for NYSE-listed stocks and HFT. Hendershott & Riordan (2012) used the NASDAQ-HFT and BATS-HFT data sets to examine this link. Their model specified the changes in the efficient price, m!,!, as a linear function of unanticipated HFT resting order flow and unanticipated marketable order flow. The use of unanticipated net order flow removes the auto-correlated component of net order flow, which is consistent with price change in the permanent price component following a martingale. The transitory pricing error, s!,!, was specified as a linear function of net HFT marketable order flow, H!"#$!,!, and the net HFT resting 46

47 order flow, H!"##!,!. The HFT trades for each 10-second interval were aggregated over the 26 HFT firms separately for marketable and resting orders. For each order type, net HFT order flow, H!,!, was measured as aggregated HFT buys minus aggregated HFT sales. The predicted components of net HFT order flows were based on an AR(2) model of the net HFT order flows. The unanticipated HFT net order flows, H!,!, were calculated by subtraction of the anticipated net HFT order flows from the observed net order flows. Our discussion focuses on the 40 large cap stocks in their sample, which are more relevant for interpreting the variance ratio test results for large cap stock in which high frequency traders are most active. Using 10-second return observations from the first week of each quarter in 2008 and 2009, they found that the net HFT marketable orders were negatively correlated with the transitory pricing errors and that the unanticipated net HFT trades were positively correlated with changes in the permanent price component. These results suggest that the improvement in efficiency evidenced by the above discussed variance ratio results may be partially attributable to HFT marketable orders, increasing the speed of adjustment of the permanent price component to new information and reducing the transitory noise in the price. The negative association of net HFT marketable order flow with transitory pricing error is inconsistent the Jarrow & Protter conjecture the 47

48 HFT cause transitory price momentum. No significant association between net HFT marketable order flows beyond a 10-second change in the permanent component is inconsistent with the Biais, Foucault & Moinas assumption that HFT competes for the same information as institutional investors. Hendershott & Riordan found that the signs were reversed for net HFT resting order flow. In a world of diverse information where short-term trading profits are a zero sum game it is difficult to generate gross profits when informed traders have the option of taking or leaving resting orders in the book. The negative association with changes in the permanent price component may be associated with adverse selection by informed traders. The positive association with the transitory price changes may be caused by high frequency traders providing liquidity with resting orders for large position changes by institutional investors, who typically fragment their desired position changes into smaller trades executed over time using trading algorithms. High frequency traders mitigate (but do not completely offset) the impact of these sources of adverse selection by limiting the size posted at a given point in time and rapidly adjusting their quotes to the realtime information contained in changes in trade prices, posted liquidity, and other relevant signals. 48

49 The Hendershott & Riordan state space model and the result of its application to the sample of HFT flagged NASDAQ trades in the 40 large cap stocks for the first week of each quarter in 2008 and 2009 are presented in Table 1 below. Their state space model is reproduced below for reference: 𝑝!,! = 𝑚!,! + 𝑠!,! 𝑚!,! = 𝑚!,!!! + 𝑤!,!!"#$!"## 𝑤!,! = 𝜅!!"#$ 𝐻!,! + 𝜅!!"## 𝐻!,! + 𝜇!,!!"#$!"## 𝑠!,! = 𝜙𝑠!,!!! + 𝜓!!"#$ 𝐻!,! + 𝜓!!"## 𝐻!,! + 𝜈!,! Their model can be more clearly understood by restating it in a reduced form. The change in stock price may be expressed as: 𝛥𝑝!,! = 𝑝!,! 𝑝!,!!! = 𝑤!,! + 𝛥𝑠!,! 𝐼𝑛𝑖𝑡 𝑃𝑎𝑠𝑠 𝐻𝑃𝑎𝑠𝑠 + 𝜓𝐼𝑛𝑖𝑡 𝛥𝐻𝐼𝑛𝑖𝑡 + 𝜓𝑃𝑎𝑠𝑠 𝛥𝐻𝑃𝑎𝑠𝑠 + 𝜙 𝛥𝑝 = 𝜅𝐼𝑛𝑖𝑡 𝑖,𝑡 𝑖,𝑡 𝑖,𝑡 𝑖 𝐻𝑖,𝑡 + 𝜅𝑖 𝑖 𝑖 𝑖 𝑖,𝑡 1 + 𝑒𝑖,𝑡 𝑒!,! = 𝜙! 𝑤!,!!! + 𝛥𝑣!,! + 𝜇!,! Note that 𝑒!,! may be viewed as an error. The components 𝜙! 𝑤!,!!!, 𝛥𝑣!,! and 𝜇!,! are uncorrelated with 𝛥𝑝!,!, have zero autocorrelations and have zero serial correlations with each other; therefore 𝑒!,! is not autocorrelated. Thus, the reduced form model could have been estimated using ordinary least squares. 49

50 The regression of share price changes on changes in net order flow produces coefficients that would be expected to vary inversely with size. A net order flow of $1 million would have a larger impact on a small cap stock than a large cap. Indeed, the average coefficients that they reported for the group of 40 small cap stocks are two orders of magnitude (100 times) larger than those reported for large cap stocks. While this problem is mitigated within the cap size groups it is not eliminated. Thus, the interpretation of the reported t-values that are influenced by the cross-section variation in estimated coefficients between stocks with a wide variation in shares outstanding is unclear. A better approach may be to express the equation in percentage price change and net order flows, as a percentage of outstanding shares. Under this specification the expected value of the coefficients would be constant across cap sizes. Hirschey (2011) investigated the nature of HFT marketable order trading behavior using the NASDAQ-HFT data set. In particular, Hirschey focused on the relationship between marketable order trading by HFT and marketable order trading by non-hft. He found that HFT net marketable trading is positively correlated with lagged, contemporaneous and future net marketable trading by non-hft participants. Hirschey also showed that both HFT and non-hft marketable trading is positively correlated with future returns. This is consistent with the results of Hendershott & Riordan (2012), 50

51 which showed that HFT traded in the direction of permanent price impact as well as Brogaard (2011b), which showed HFT positively contributing to the price discovery process. Profitability of HFT and Transaction Taxes Since well before the early periods discussed in the introduction to this paper, markets have relied upon professional intermediaries for liquidity and price discovery. These traders typically provided their services on trading floors and used a wide range of skills, information and technologies in a variety of market structures. Traditionally, they were given special status and shielded from competition. In recent years, the role of the professional intermediary in the U.S. equity market and in many other markets has become increasingly competitive and efficient. As surveyed in this paper, the impact of this competition has largely been improved market quality for investors. At the same time, it has resulted in a significant reduction in profitability per share for modern professional intermediaries. High frequency trading firms invest significant amounts of money in highly skilled staff and computer technology. This activity effectively allows them to obtain and process large volumes of public information and rapidly 51

52 incorporate it into their pricing. Grossman & Stiglitz (1980) noted that obtaining and processing information is costly and an investor will not incur these search costs if the information is already reflected in stock prices. Diamond & Verrecchia (1981) showed that when information differs across traders, informed investors earn a normal profit to compensate for the search costs they incur. Estimates of the average level of profitability of trades of independent HFT firms, which are discussed below, are less that 1 basis point, which does not seem in excess of the level of profitability necessary to compensate them for their large investment in technology and human capital. The profitability of independent HFT firms as a group has been estimated using various data sets, the most reliable probably coming from Brogaard (2011a). Using the NASDAQ-HFT data set, Brogaard found HFT made about 0.72 basis points per trade. Extrapolating to include the entire market, Brogaard estimated annual trading revenues for the 26 included HFT firms as a group to be $2.8 billion in 2008 and These estimates did not try to incorporate operational expenses of high frequency trading including labor, technology and other costs. They also did not include exchange fees, rebates and other regulatory fees that would, in net, bring these estimates down. Hendershott & Riordan (2012, p. 1), using the same NASDAQ-HFT data set, found that this profitability was earned with both marketable and 52

53 resting orders. HFT marketable orders informational advantage is sufficient to overcome the bid-ask spread and trading fees to generate positive trading revenues. They also found that HFT resting orders result in positive revenues as the costs associated with adverse selection are smaller than the bid-ask spread and liquidity rebates (Ibid, p.1). Industry estimates indicated that profitability for high frequency trading declined significantly after the high volume, high volatility period studied by Brogaard. For example, Rosenblatt Securities estimated that overall trading revenues from HFT declined to $1-2 billion in 2010, down by about 50% from their estimates for 2008 and 2009 (Rosenblatt 2011). To put these numbers in historical context, Brogaard also compared the profitability of HFT on a per-dollar traded basis against specialist profitability from immediately after decimalization in He reported that HFT margins in 2008 and 2009 were about one seventh that of specialists of that earlier period, suggesting that greater competition in recent years had dramatically reduced gross profit margins for professional trading firms. Given the recent discussions concerning a tax on stock transactions, the profitability of HFT firms relative to some proposed transactions taxes is relevant for assessing the potential impact on market quality. For example, 53

54 effective August 2012, France has approved a tax of 10 basis points for trading in large cap French stocks. Many implementation details for this tax remain uncertain, however, as of late June Similar taxes have been proposed in other jurisdictions, including in the United States, in Germany, and for the entire European Union. The proposed rates generally range from 3 basis points to 50 basis points and the terms and exemptions vary considerably. The United Kingdom has had in place a 50 basis point Stamp Duty Reserve Tax (SDRT) for many years. However, there are several important exemptions to the SDRT that effectively exempt financial intermediaries and many derivatives transactions. While a detailed survey of the literature on financial transaction taxes is beyond the scope of this paper, it is informative to compare the magnitude of the proposed taxes with the profitability estimates for high frequency trading, specifically the estimate of 0.72 basis points provided by Brogaard. Such taxes, if approved and implemented in a manner that applied to high frequency traders, would overwhelm the profitability of their current trading activities and would require dramatic adjustments in trading styles or their exit from those markets. The potential impacts on market quality for investors likely include wider bid-ask spreads and larger transitory price movements. Maintaining the same level of per share profitability would require an increase in the spread by about 2 cents on a $35 stock, and 54

55 transitory pricing errors would have to increase by twice this amount to account for the increased bid-ask spread and the transaction tax and to incentivize HFT marketable orders. This suggests that the price pressure impact of large trades would increase. Both government and academic studies have explored potential consequences of transactions taxes and several have concluded they could adversely impact liquidity, and increase volatility. For example, in a CRS Report for Congress it was observed that: The potential for increasing volatility may be due to the tax increasing the transaction cost and reducing the profitability of some trades. With [a transaction tax] in place, traders may have to wait for prices to make larger movements before it becomes profitable to enter the market. For example, a trader who may have previously found it profitable to trade when a stock s price jumped from $50 to $51 dollars, may now wait until the stock s price rises to $51.25, to cover the extra cost of the tax. The increased cost of trading could therefore lead to larger movements in the prices of securities and hence greater volatility (Keightley 2012, p.2). It should also be noted that there is already a transaction fee in the U.S. securities markets called the Section 31 fee that in many ways resembles a financial transaction tax. This fee is charged by the SEC to the self- 55

56 regulatory organizations such as FINRA and the national securities exchanges, and the fee is generally passed on to broker-dealers and in turn to their customers. The fee currently is about $22 per $1 million of stock sold, which, for traders that buy as much as they sell, is equivalent to about 0.11 basis points of the total value traded (as of June 2012). Based on Brogaard s estimates, this equates to about a 15% fee on HFT trading revenues in the period he studied. By implication, this fee eliminates some forms of trading that might otherwise improve market quality beyond the current status quo. In a letter to the SEC responding to their concept release on equity market structure in 2010, Vanguard Investments commented on their execution costs (Sauter 2011). As a large investment management firm holding over $1.4 trillion under management and serving over 23 million shareholder accounts, execution costs are of great importance to them and their account holders. They estimated that their transaction costs have declined by at least 50 basis points per trade over the previous fifteen years and noted that the market structure changes facilitated by the Commission s various regulatory initiatives and the knitting together of the marketplace by high frequency trading, have led to a significant decline in transaction costs for long-term investors over the past ten years through increased liquidity and tighter bid-ask spreads. They further estimated that these savings have 56

57 resulted in an additional 30% return for investors over a 30-year horizon. If HFT profits are less than 1 basis point and play a significant role in 50 basis points of savings, then it appears they impart a substantial positive externality to institutional investors. Volatility Market crashes and episodic periods of high volatility deserve attention and scrutiny. Since the flash crash of May 6, 2010, regulators in the U.S. (and globally) have given considerable thought toward market robustness, as well as safeguards and regulations that may help improve the resiliency of markets during times of high stress. There has been general concern among regulators and market observers that, as technology improvements enable trading at faster rates, volatility could increase and resiliency could degrade. Although this is not apparent from broad measures of volatility, lingering questions from the flash crash remain, and while the SEC-CFTC Joint Staff Report found that the primary cause of the high volatility that day was an extremely large automated trade from a mutual fund, the impact of HFT activity during the flash crash has been a matter of some discussion (U.S. SEC 2010b). 57

58 Trading patterns and quoting behavior convey information about future stock values and such information is part of the price discovery process. These induce responses from other market participants and thereby create price movements. In the rational trading models of Kyle (1985) and Admati & Pfleider (1988), private information revealed through trading causes volatility. Several empirical studies showed that common stock volatility is greater during trading than non-trading hours; e.g., Granger & Morgenstern (1970) and Christie (1981), and other studies have shown that these higher volatilities are not explained by more public information being released during trading hours; e.g., French & Roll (1986) and Barclay, Litzenberger & Warner (1990). Thus, volatility of the permanent component of a stock price is related to the information conveyed through trading. Many observers have asked whether certain forms of trading activity -- HFT in particular -- induce an abnormal amount of transitory volatility, and whether such activity may act in a destabilizing manner on the market. Some studies used periods of high messaging rates as a proxy for periods of high HFT activity, and found that these periods were more volatile even when adjusting for market volume (see, for example Biais & Wooley (2011) and Boehmer, Fong & Wu (2012)). However, liquidity and price discovery is in greater demand in more volatile periods, when immediacy of execution is more important to investors. Additionally, as prices traverse a larger 58

59 number of price points, traders in continuous markets using resting orders require larger numbers of quote updates to stay at or near the best market prices. A statistical association between volatility and high messaging rates does not indicate a causal relation between volatility and HFT activity. An understanding of the impact of the criteria used to construct proxies for HFT and the relevance of the time period examined is critical for interpreting the results of these studies. Using the NASDAQ-HFT and BATS-HFT data sets, Brogaard (2012) measured short-term volatility over intervals as short as 10 seconds, and examined how abnormal HFT activity varies with abnormal volatility. He measured abnormal volatility for each stock in every 10-second interval as the realized volatility in that interval divided by the realized volatility of the entire trading day. He then ranked the pooled sample of 10-second abnormal volatilities for each stock over every 10-second interval into ten bins. For each bin he calculated the average abnormal HFT participation rate. The abnormal participation rate was calculated for each stock in every 10-second interval as the HFT trading participation rate in that interval relative to the HFT participation rate for the entire trading day. He then separately considered HFT marketable order participation and HFT resting order participation. He found a consistent positive association between abnormal HFT marketable order participation and volatility. This is consistent with HFT directional 59

60 traders finding more frequent transitory pricing errors in more volatile periods. He found similar results for abnormal HFT resting order participation, however, the differences between the volatility bins were less pronounced. This suggests that the liquidity provided by high frequency traders using resting orders rose moderately in periods of high volatility. He then examined causality, testing the lead lag relationship between abnormal volatility and abnormal HFT activity. He found that abnormal volatility led abnormal HFT activity and that abnormal HFT activity led abnormal volatility. The former is consistent with abnormal volatility increasing trading opportunities for directional HFT and increasing the demand for liquidity supplied by HFT resting orders. The latter result is consistent with the information content of higher trading activity (including HFT) increasing the volatility of the permanent component of price impact. Hendershott & Riordan (2012) used the same NASDAQ-HFT data set to examine related issues. An important focus of this paper was the behavior of HFT during times of market stress. The authors looked at the 10% of days from their data set with the largest volatility. They showed that HFT participation, as a fraction of total trading volumes, increased on the highest volatility days, suggesting that HFT is more active during times of market stress. They also reported that the contribution of HFT toward price 60

61 discovery increased by a significant amount on these days, suggesting that HFT activity improved price discovery on more volatile days to an even greater extent than it did on less volatile days. In the wake of the flash crash on May 6, 2010, a great deal of attention was focused on the role HFT played on that day. Kirilenko, Kyle, Samadi & Tuzun (2011) provided an analysis of trading on the CME s E-mini S&P 500 futures contract on the days around May 6 using full audit-trail data to identify participants. Traders in the CME s E-mini S&P 500 futures contract were classified into one of 6 categories based on their trading patterns: High Frequency Traders, Intermediaries, Fundamental Buyers, Fundamental Sellers, Small Traders and Opportunistic Traders. The last group is a catch-all residual group that included the over two thirds of all the accounts that did not meet the criteria for inclusion in the other groups, and likely represents a wide range of trading styles. For the combined Intermediaries and High Frequency Traders groups, they selected every trading account whose end of day holdings was less than 5% of their daily volume. For each account and each day, they computed the square root of the mean-squared deviation of end-of-minute holdings from the end of day holding, and divided that by the volume of the day. These values were averaged for each account over May 61

62 3 5, and the accounts whose averages were less than 1.5% were chosen. They ranked those accounts by number of trades and broke out High Frequency Traders as those accounts that ranked in the top 7%. These criteria may have excluded high frequency traders who were trading the CME s E-mini S&P 500 futures contract as part of a trade offsetting the risk of other assets such as individual stocks or ETFs, and may have taken larger positions at certain times during the day as hedges to their positions in other markets. Similar to the NASDAQ-HFT and BATS-HFT data sets, large integrated firms that have HFT activities that are comingled with their other trading may also be excluded from this group. Under their stated criteria, these types of HFT activities would likely fall into the very large residual group, Opportunistic Traders. Using the three trading days prior to May 6 as a reference, the authors found that High Frequency Traders and Intermediaries did not seem to alter their trading strategies significantly during the flash crash. Particularly, they noted that the trading strategies of High Frequency Traders were unaffected by either the sharp price decline or the sharp recovery that followed. They showed that during the approximately 13 minutes of rapid decline, both High Frequency Traders and Intermediaries were net purchasers of 62

63 contracts while Fundamental Sellers were the largest source of selling pressure. During the approximately 23-minute recovery, both High Frequency Traders and Intermediaries were net sellers while Fundamental Buyers stepped into the market more aggressively. Superficially, these results seem to be consistent with the conclusion that trading of what they called High Frequency Traders stabilized prices during both periods; however, the period time lengths of 13 and 23 minutes are quite short and would exhibit considerable variation in trading patterns in a normal trading day. The calculation of the cumulative frequency distributions of the net position changes for each group as a percentage of aggregate trade volume for non-overlapping 13 and 23-minute periods over the prior three days would have provided an understanding of how atypical their results were. In addition, the authors noted that High Frequency Traders inventories were too small to have caused or prevented the Flash Crash. Since the selection criteria for their High Frequency Traders group included low inventories, this conclusion is not surprising. The authors also claimed that High Frequency Traders may have exacerbated volatility of the flash crash as a result of so-called hot-potato volume; that is, during a 14-second period of rapid price decline, High Frequency Traders seemed to trade with other High Frequency Traders more often than average, and therefore may have caused automated sell 63

64 programs to misinterpret the high trading volumes as high liquidity. It is unclear why the authors considered trades between independent High Frequency Traders as not part of liquidity. Would trade volume between mutual funds that had different dynamic asset allocation models also not be counted as part of liquidity? A bid or offer of a high frequency market maker was available to lower frequency traders as well as high frequency traders. High frequency traders rapidly turnover their positions and should be viewed as transferring intra-day liquidity, which should help to mitigate intra-day trade imbalances by other traders. Overall, this paper s conclusions paint a mixed picture on the impact of High Frequency Traders on volatility. On one hand, High Frequency Traders and Intermediaries had a dampening influence on prices during both the crash and recovery; however, they suggested that high levels of trading among different high frequency traders may have been misinterpreted by fundamental participants as indications of higher liquidity. Some statistical evidence on how abnormal the observed trading patterns were would have provided additional perspective. For example, the interesting observation that trading between High Frequency Traders was higher than average during a critical 14-second interval could have been tested for statistical significance using bootstrap techniques. 64

65 There have been other, similar episodes of high volatility that have been studied. Edward Backes, Head of Market Supervision, Eurex Frankfurt AG, examined a similar short-term price dislocation on Eurex on August 25, 2011, in which the benchmark futures contract, the FDAX, fell by about 4% in the span of 17 minutes only to recover by 2% within the following four minutes (Backes 2012). Given the similarity to the U.S. flash crash, this event garnered a great deal of attention. Looking at the actual trades made on Eurex during this time, Backes found that the fall was initiated by large sell orders by institutional clients, much like the U.S. flash crash. Furthermore, he found a great deal of liquidity was available during both the fall and rise of the FDAX, with a wide variety of participants acting both on the sell side and buy side. He stated that HFT as a group help in processing high volume orders in a way that protect[s] the market by placing a rapid succession of small, non-directional buy and sell orders, thus preventing abrupt price movements. Given their importance, it is useful to understand whether the prevalence of extreme market movements has grown in recent years as HFT has grown. A Credit Suisse study looked at how frequent extreme price movements, i.e., price changes of greater than 1% over a 1-minute period, have been since 2000 (Avramovic 2012). Partitioning their data into volatility groups, they 65

66 found that the rate of these extreme movements has actually declined noticeably since This suggests that major price dislocations are less common in modern electronic markets than they have been in the past. In contrast with the studies discussed above, Zhang (2010) investigated the longer-term impact of HFT activity on quarterly volatility. He did not provide a rationale for why HFT trading would have a significant impact on quarterly volatility. However, he did use a very broad definition of HFT: I essentially define HFT as all short-term trading activities by hedge funds and other institutional traders not captured in the 13f database (Zhang 2010, p.14). By including trades with a holding period of up to one calendar quarter, his definition of HFT is at odds with common usage. His sample consisted of all non-penny stocks included in the CRSP and Thomson Reuters Institutional Holdings databases for the first quarter of 1985 through the second quarter of The institutional holdings data was based on 13f form data and included long holdings of investment companies including banks, insurance companies, mutual funds, pension funds, endowments, and hedge funds; however, filing is not required for portfolios smaller than $100 million or positions less than 1,199 shares. He used the CRSP data to measure end of quarterly outstanding shares for each stock. He then used the Institutional Holdings data to calculate quarterly 66

67 institutional holdings for each stock and measured institutional turnover as the aggregate (absolute value of, sic.) net quarterly change in holdings of a company s shares across all institutional investors divided by an average of beginning and ending institutional holdings. He observed that value-weighted quarterly institutional turnover stayed around 20% for the entire sample, while stock turnover hovered around 17% between 1985 and 1994 and then gradually increased to 115% in He contended that this gradual increase was coincident with the emergence and rising popularity of HFT beginning in However, the electronic trading platforms needed to support HFT only began to emerge in 1997 and the rapid growth of HFT in NYSE-listed stocks occurred after The growth in stock turnover between 1994 and 2006 is likely more coincident with the rapid growth of equity hedge fund assets under management. Equity hedge funds have on average much higher turnover rates than long-only portfolio managers and ignoring the impact of the intraquarter position turnover would understate institutional volume by an amount that increases over the sample period. Moreover, institutions would be expected to trade more within a quarter as trading costs declined. Zhang divided his sample into a control period with no HFT, ; and a test period where HFT emerged and grew rapidly, For the control period individual volumes for each stock was measure as a residual 67

68 by simply subtracting inter-quarter institutional volumes from total volume and individual share holdings for each stock were estimated by subtracting institutional share holdings from shares outstanding. Thus, all volume not reflected in inter-quarter institutional trades reported in 13F report was denoted individual volume. Individual volume as a fraction of institutional volume was averaged across stocks and over the test period. For the test period, individual volume for each stock in each quarter was estimated by multiplying this average times institutional volume. For the test period HFT volume for each stock and in each quarter was measured as a residual by subtracting both institutional volume and individual volume for total volume. There are four problems with his proxy for HFT volume; (1) institutional volume is understated by the exclusion of intra-quarter turnover; (2) this understatement would be anticipated to increase over time because of the growth in AUM of equity hedge funds, who have higher turnover rates than long-only managers; (3) access to the markets by retail investors improved over this period due to substantial drops in costs and on-line access to trading, and (4) differences in the ratio of individual to institution turnover would differ across stocks. This raises the possibility that individual turnover has risen relative to institutional turnover and the average estimate of HFT trading volume is biased upward. Furthermore, difference in individual 68

69 turnover as a percentage of institutional turnover across stocks would result in are large errors in estimate of HFT volume for individual stocks. These problems result in biased and noisy estimates of HFT trading volume. For example, Zhang reported a lowest quartile value of , and a minimum value of , which makes no sense for estimates of volume per share outstanding, which should be positive. These problems cloud the interpretation of his results. Zhang used a variety of econometric models, regressing quarterly volatility estimates on his estimate of HFT volume per share, along with several control variables and found positive coefficients on HFT. Given the above discussed problems with his HFT measure, the interpretation of his results is unclear. Perhaps hedge funds and/or individual day traders are more active in more volatile stocks. The review of the empirical literature suggests that HFT tends to reduce intra-day transitory pricing errors and does not contribute to intra-day volatility. The automation of trading and the use of computer algorithms have increased the speed of price discovery and increased market efficiency. There is neither theoretical support nor empirical support for the conjecture that HFT (as conventionally defined), which is typically thought of as having 69

70 minimal overnight un-hedged positions, impacts longer-term measures of volatility. Technology and Latency The ability to quickly transmit information from one geographical location to another has always conferred informational advantages to traders and, over the years, technology has enabled ever-faster communications. The pioneering telegraph network of the 19th century at the NYSE has been replaced by a fiber network spanning the globe, and the semaphores and lanterns that once dotted the hilltops between the NYSE and the Philadelphia Stock Exchange have been replaced by fiber optic cables and microwave towers, but the underlying drive to move information quickly has not changed (Barnes 1911). These technology advances have played an important role in the improvements in market quality that have been observed over the past decade and several studies have attempted to quantify the impact that latency reductions have had on markets. In total these studies find that lower latency allows traders to manage risk better, thereby improving overall market quality. 70

71 Over recent years, the time to match orders has decreased dramatically as electronic trading platforms have adopted and upgraded their technologies. For a frame of reference, it is widely reported that trades on the NYSE prior to the deployment of their Hybrid system in 2006 typically took over ten seconds to match. Around the same time, several competing electronic trading platforms offered automated matching; with an average matching time on NASDAQ reported to be 10 milliseconds, while Inet s average matching time was reported to be about 5 milliseconds (Mearian 2005). At about the same time, BATS was reporting average matching times of about 1 millisecond. After the deployment of its Hybrid system in 2006, the NYSE s matching times dropped from 10 seconds to less than 1 second (Hendershott & Moulton, 2011). By 2010, NYSE had further reduced its matching time to about 5 milliseconds, and, by 2012, to about 250 microseconds. Over the same period BATS and NASDAQ reduced their average matching times to about 200 microseconds. These advancements coincided with the market quality improvements surveyed in this paper and, as shown in the remainder of this section, played an important role in those improvements. For example, Hendershott & Moulton (2011) studied the NYSE s Hybrid upgrade and showed that it resulted in more informative quoting and better price discovery for NYSElisted stocks, though they also found a transient increase in effective bid-ask 71

72 spreads. Unfortunately, the Hybrid system upgrade also changed many aspects of the NYSE s market structure, and so a direct measure of the impact of the latency reduction was difficult to isolate. Riordan & Storkenmaier (2011) were able to isolate the effects of exchange latency using a technology upgrade in 2007 on the European Xetra trading platform as a natural experiment. The upgrade affected only system latency, which dropped from 50ms to 10ms as a result. Measured quoted and effective spreads were shown to decrease after the upgrade, and the informativeness of quotes was shown to be significantly higher, suggesting higher price efficiency. These results were shown to hold both in high and low activity days. The Tokyo Stock Exchange (TSE), one of the highest volume markets in the world, upgraded its matching systems at the start of 2010, with their Arrowhead platform providing average match times of about 5 milliseconds, down from 2 to 3 seconds before the upgrade. Menkveld (2012) examined the impact that this upgrade had on market quality and found that: Overall, Arrowhead significantly improved price discovery and market quality in terms of better liquidity, reduced volatility, and greater speed of trading on the TSE (Ibid, p. 39). 72

73 Driven primarily by competitive pressures and by demand from traders, trading platforms have continued to invest in technologies that reduce their latencies. The studies presented above largely suggest that this has enabled traders to better manage risk and quote more informative prices, resulting in overall improvements in market quality. One related effect of these technological improvements has been a growth in messaging rates, which is explored in the subsequent section. Messaging Rates Recent years have seen a dramatic increase in the amount of messages sent to and from markets, including order requests, responses, cancellations and trades. The rising number of electronic trading platforms, the increasing use of algorithms to manage orders and split order flow and the growing number of participants in the markets have all contributed to this rise. Figure 11 provides an example of this growth, showing the 1-minute peak messages per second from NASDAQ s UQDF data feed, which provides price and size information for all NASDAQ stocks. There has been an external cost imposed on electronic trading platforms, market participants and market data providers who have had to invest in technology to keep pace with growing rates of market data. While this cost on a per message basis is minuscule, it is not zero, and historically, many 73

74 markets have largely ignored this external cost in their pricing structures. It is important therefore to consider the economics of messaging in the markets and whether opportunities exist for improved efficiencies. The optimal ratio of canceled orders is an open question that has not received much study, and perspectives vary. On the one hand, the ability to repeatedly reprice or otherwise modify resting orders is critical for traders to manage risk and therefore to be able to quote tighter bid-ask spreads and display more size. A significant amount of these messages, primarily orders and cancellations placed near the inside best market, therefore, clearly do contribute to improved liquidity and price discovery. On the other hand, high rates of canceled orders can complicate the process of trading for retail and institutional participants, or in certain circumstances, could potentially be evidence of market abuse. There has been some recent empirical work on the impact of high messaging rates on market quality. Hasbrouck & Saar (2011) used full order-book data from NASDAQ to identify low-latency trading, which they defined as trading that responds to events on a millisecond time frame. They identified specific instances of low-latency activity that they called strategic runs. These are short (typically seconds) periods of highly active sequences of order placements/cancels and trades that can likely be attributed to one or a few 74

75 participants. They used these periods of high messaging as representative instances of high low-latency activity and then determined what impact such activity had on various market quality metrics. They set up a two-stage regression in order to control for the potential endogeneity of low-latency activity by looking at trading activity on other exchanges, using the TAQ data feed. Generally, they found that low-latency activity is more prevalent on more liquid and less volatile stocks. In addition to other positive effects on spreads and liquidity, Hasbrouck & Saar found that high messaging rates associated with low-latency activity lowered short-term volatility at statistically significant levels. They also noted that these results were consistent in both the low-volatility and high-volatility periods, and suggested that this activity creates a positive externality in the market at the time that the market needs it the most. Additionally, they noted that increased low-latency activity improves traditional market quality measures such as short-term volatility, spreads, and displayed depth in the limit order book. Market regulators and exchanges have a responsibility to monitor the markets for abusive or manipulative behaviors. A purported practice that has received some attention is so-called quote-stuffing, which describes the rapid placing and canceling of orders not with the intent to trade, but 75

76 rather with the intent of slowing down public market data dissemination (Biais & Wooley, 2011). It seems unlikely that these practices are widespread, in part, because they are illegal and should be easy to detect by regulators and exchanges, given that all messages are preserved in an electronic audit trail that indicates who sent them. Regardless, message traffic remains an area that deserves attention from regulators and exchanges. It is important not only that they have the ability and capacity to detect and appropriately handle messaging abuses, but that they provide appropriate economic incentives to promote efficient messaging to minimize cost externalities. Several exchanges now enforce explicit limits on cancellation to fill ratios, imposing additional fees on participants who exceed these limits. For example, the Direct Edge exchanges have recently started their Messaging Efficiency Incentive Programs that impose increased fees on firms that do not maintain a sufficiently low cancellation to fill ratio (Direct Edge 2012). Other markets like NASDAQ and the Intercontinental Exchange have in place more elaborate programs that take into account the proximity to the best market prices in calculating member messaging rates (NASDAQ OMX Group 2012; Intercontinental Exchange 2012). Initiatives like these present opportunities to minimize wasteful messaging without discouraging valuable behaviors. 76

77 Concluding Remarks A large and growing body of evidence shows that the quality of the U.S. equity markets has improved significantly over recent decades. Not only have general market quality trends been positive, studies that link specific events or participants to market quality demonstrate that many of the benefits can be attributed to improved competition and automation, and indeed high frequency trading. These benefits include a reduction in quoted and effective bid-ask spreads, an increase in posited liquidity at NBBO and within 6 cents of the NBBO, and a decline in transitory price pressure impacts of large trades. These changes are consistent with the prediction of the Gerig & Michayluk model of computerized liquidity providers. Much of the empirical evidence on the direct impact of HFT on the U.S. equity markets has relied heavily on limited samples of proprietary data provided by NASDAQ and BATS to several financial economists. Broadening these samples to include other trading venues, more stocks and more time periods, and making this more widely available, would result in more empirical research being done on a larger data set and more reliable inferences drawn from this data. Further theoretical modeling of HFT, 77

78 extending the work of Gerig & Michayluk to include endogenous determination of the bid-ask spread and explicit modeling of HFT marketable orders, would increase our conceptual understanding of HFT. Based on the NASDAQ and BATS data sets, net HFT marketable order flow is shown to be positively related to permanent price movements and inversely related to transitory price movements. This suggests that HFT marketable orders contribute to price discovery and provide intra-day liquidity transfer services that reduce the price pressure impact of large buy or sell programs. Large buy or sell programs implemented through execution algorithms and facilitated by HFT are consistent with the observed decline in trade sizes. In contrast, net HFT resting orders are negatively related to permanent price movements and positively related to transitory pricing errors, which suggests that resting orders provide liquidity to informed traders and absorb adverse selection. Marketable orders are sufficiently profitable to offset executions costs and the bid-ask spread, and resting orders earn sufficient spreads and rebates to offset the impact of adverse selection. The average profit earned on HFT trades during 2008 and 2009 is estimated at 0.72 basis points, about one seventh of the profitability of specialist trades immediately after decimalization. In contrast, a major asset 78

79 management firm observed that regulatory reform and HFT have contributed to at least a 50 basis point saving in transaction costs over the last 15 years. The level of profitability of HFT trading may be viewed as a normal profit that compensates HFT firms for their large investment in technology and human capital. Proposed transaction taxes that exceed the average preshare profit of HFT would be expected to result in a reduction in HFT activity, an increase in bid-ask spreads, and an increase in the price pressure impact of large trades. The future of U.S. and global markets will be shaped, in large part, by the decisions and policies adopted by policymakers and regulators. The ability to understand the impact of proposed reforms and regulations is critical to help ensure that the improvements that have been made in the markets over recent decades are retained and extended. To this end, we hope that market operators and regulators make more data available for study. Additionally, we encourage thoughtful initiatives such as pilot programs and structured deployments of rule and technology changes to provide the opportunity for further study and better empirical understandings of markets. The functioning of markets is too important to investors, businesses and economies to base policy on anything but sound evidence. 79

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85 ψpass (t- stat) bps. / $ (4.18) 2.25 (6.49) (7.97) 9.17 (6.24) Figure 1: Effective spreads, NYSE-listed stocks between 1993 and Original source Chordia, Roll & Subrahmanyam (2008). Reproduced from Angel, Harris & Spatt (2010) 85

86 USD NYSE listed NASDAQ listed Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Figure 2: Effective Spreads between , NYSE-listen and NASDAQ stocks. Taken from Rule 605 reports from Thomson, Market orders, shares. 86

87 USD NYSE listed Russell 1000 NASDAQ listed Russell 1000 NYSE listed Russell 2000 NASDAQ listed Russell 2000 Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Figure 3: Quoted spreads of Russell 1000 and Russell 2000 stocks, NYSE-listed and NASDAQ stocks between 2006 and

88 NYSE listed Russell 1000 NYSE listed Russell 2000 USD Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Figure 4: VIX-adjusted Quoted spreads of Russell 1000 and Russell 2000 stocks, NYSElisted between 2006 and

89 Figure 5: Median displayed depth at NBBO between 2003 and Original source Knight Capital Group. Taken from Angel, Harris & Spatt (2010) 89

90 Figure 6: Median displayed depth within six cents of NBBO between 2003 and Original source Knight Capital Group. Taken from Angel, Harris & Spatt (2010) Available Liquidity: Russell 1000 Available Liquidity (1000's USD) NYSE listed NASDAQ listed Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Figure 7: Average dollar value posted at the inside for Russell 1000 stocks, NYSE-listed and NASDAQ stocks between 2006 and

91 Available Liquidity: Russell 2000 NYSE listed NASDAQ listed Available Liquidity (1000's USD) Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Figure 8: Average dollar value posted at the inside for Russell 2000 stocks, NYSE-listed and NASDAQ between 2006 and

92 10 second / 1 second Variance Ratio: Russell 1000 Average Variance Ratio NYSE listed NASDAQ listed Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Figure 9: Average 10-second to 1-second variance ratio for Russell 1000 stocks, NYSE-listed and NASDAQ stocks between 2006 and

93 10 second / 1 second Variance Ratio: Russell 2000 Average Variance Ratio NYSE listed NASDAQ listed Q1 06 Q2 06 Q3 06 Q4 06 Q1 07 Q2 07 Q3 07 Q4 07 Q1 08 Q2 08 Q3 08 Q4 08 Q1 09 Q2 09 Q3 09 Q4 09 Q1 10 Q2 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Figure 10: Average 10-second to 1-second variance ratio for Russell 2000 stocks, NYSElisted and NASDAQ stocks between 2006 and

94 Figure 11: Peak 1-minute messages per second for UQDF. Source: Financial Information Forum ( 94