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 jcastura@rgmadvisors.com 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