Intraday Patterns in High Frequency Trading: Evidence from the UK Equity Markets

Transcription

1 Intraday Patterns in High Frequency Trading: Evidence from the UK Equity Markets Kiril Alampieski a and Andrew Lepone b 1 a Finance Discipline, Faculty of Economics and Business, University of Sydney, Sydney, New South Wales, Australia 2006 b Fondazione European Capital Markets Cooperative Research Centre Abstract High Frequency Traders (HFT) generate returns on their proprietary capital, in part, from performing the functions of a market maker. This study examines the intraday variation in (HFT) participation in the combined London Stock Exchange, Chi-X and BATS equity markets, and seeks to reconcile their behaviour to that of market makers as presented in the academic literature. This study uses a proprietary dataset provided by the UK Financial Services Authority that contains trade data and order book data from LSE, Chi-X Europe and BATS Europe for all constituents stocks in the FTSE 100 for 30 trading days in 2010 which is anonymised at the platform level and aggregates traders as either HFT or OT. This paper shows that intraday HFT participation in the UK order book conforms to traditional models of liquidity provision. HFT actively manage their risk of exposure to information asymmetry through their speed, and the posting of lower depth levels in morning trade, while HFT post significant depth at the best quotes throughout afternoon trading, until the close. HFT quotes contribute the vast majority of price discovery. HFT trade participation is also lowest in morning and afternoon trading; however, this is driven predominantly by their participation as liquidity demanders, with HFT supplying significant liquidity in the morning, and rising throughout the afternoon, supporting traditional inventory-based market maker models. Keywords: Market Microstructure, Market Quality, High Frequency Trading 1 We would like to thank the Financial Services Authority (FSA) and Capital Markets Co-operative Research Centre (CMCRC) for providing the data and funding for this research. The London Stock Exchange (LSE), Chi-X Europe and BATS Europe are not party to this research or responsible for the views expressed in it. References in this report to data from the LSE, Chi-X Europe and BATS Europe refer to information provided by the LSE, Chi-X Europe and BATS Europe to the FSA, which regulates the LSE, Chi-X Europe and BATS Europe, at the FSA s request. The data provided by the LSE, Chi-X Europe and BATS Europe to the FSA relates to historic trading data from 2010 and such data was made anonymous, aggregated and did not identify member firms or their activity. 1

2 1. Introduction High Frequency Trading (HFT) has emerged in the last decade to dominate trading volumes across the world s largest trading venues. From being virtually non-existent as a participant in markets at the turn of the millennium, HFT now constitute the majority of trading volume in all developed markets. However, after the May 6, 2010 Flash Crash, regulators worldwide have moved to curb high frequency trading on fears that HFT may exacerbate volatility. This is despite the growing academic literature which shows that HFT, in their role as market makers as well as arbitrageurs, effectively dampen price volatility. This study adds to the academic literature by analysing HFT throughout the trading day in the UK. Using a unique dataset provided by the UK Financial Services Authority containing trade data and order book data from the LSE, Chi-X Europe and BATS Europe for all constituents stocks in the FTSE 100 for 30 trading days in 2010, HFT behaviour is studied in the combined LSE, Chi-X and BATS national order book. High Frequency Traders, a subset of Algorithmic Trading (AT) participants, use computer algorithms and low latency infrastructure to generate and execute trading decisions for the purpose of generating returns on proprietary capital. Their primary strategies are statistical arbitrage, market-making and directional trading. Their main advantage lies in their ability to react to changing market conditions much faster than traditional market participants. Recent moves by regulators to reduce the competitive advantage of HFT, such as by imposing order-to-trade ratios and minimum resting times, has seen HFT exiting markets. 2 To determine the effect this may have, it is important to understand how HFT affect market quality. This study aims to address this by analysing how HFT participate as passive liquidity providers in the limit order book, as well as trading participants in the FTSE 100, across the LSE, Chi-X and BATS. The combination of these three platforms allows for an analysis of the majority of activity in the FTSE 100, while prior analysis of HFT and AT in US and Europe has only captured a fraction of HFT activity in a given sample of securities. This allows for greater robustness in the analysis of HFT. 2 HFT curbs may take Europe back 7 years. Financial Times 8/05/12 2

3 Analysis of HFT in the limit order book shows that HFT participation increases as the trading day progresses, both in terms of depth at the best quotes, and time at the best quotes. This is largely in line with the theories put forth by Ho and Stoll (1983) and Harris (1990) addressing intraday patterns in spreads and depth. Using the methodology put forth by Hasbrouck (1995), HFT are shown to contribute the vast majority of information into quotes. As an active trading participant, HFT focus their participation predominantly in the period commencing at 1:30pm, increasing after US markets open at 2:30pm London time. Interestingly, HFT participation as a liquidity demander is lowest after UK markets open, and just prior to the close, while HFT liquidity supply is strong both at the commencement of trading and just before the London close, in line with both the inventory model of Ho and Stoll (1983) and the information models of Copeland and Galai (1983) and Glosten and Milgrom (1985). The rest of the paper is structured as follows: Section 2 reviews the literature on intraday patterns in financial markets and the literature on high frequency trading, Section 3 describes the dataset used, Section 4 presents the methodology used to analyse HFT presence in the UK order book, Section 5 and Section 6 present the order book and trading results, and Section 7 concludes. 2. Literature Review 2.1 Liquidity Provision, the Bid-Ask Spread and Depth The bid-ask spreads posted by liquidity providers, particularly market makers, are seen to be primarily affected by inventory levels and information asymmetry. Inventory-based models focus on the effect of inventory imbalances on a market makers quoted prices and spreads, while information-based models analyse the effect of posting quotes in a market with high information asymmetry, resulting in higher adverse selection costs. 3

4 Tinic (1972) pioneered the use of inventory-based models to highlight the risk faced by market makers from holding an undiversified portfolio. Stoll (1978) states that spreads exist to compensate liquidity providers (particularly market makers) for undertaking the risk of holding unwanted inventory in their portfolio, and the cost of the spread is equivalent to the cost of holding an undiversified portfolio. The market maker s inventory position relative to their desired inventory position has an effect on the spread and prices set by market makers, as argued in Amihud and Mendelson (1980). At the point where the market maker s inventory position reflects the desired inventory position, spreads are minimal, while spreads will widen as inventory imbalances develop. Ho and Stoll (1983) present a model of competitive dealers who differ in their quoted spreads due to their idiosyncratic risk assumptions, as well as their inventory levels. Controlling for these unique risk levels, a market maker will naturally adjust their bid and ask quotes to bring their actual inventory position towards their desired inventory position. In their analysis of intraday quotes on Nasdaq, Chan, Christie and Schultz (1995) find that inventory effects are particularly acute at the close of trading, as dealers face the risk of holding undesired inventory overnight, and seek to return to their desired inventory levels. This can drive market makers with long inventory positions to post more competitive ask quotes, while short inventory positions will encourage market makers to post more competitive bid quotes; this results in a narrowing of the inside spread at the close of trading. Supporting the inventory model of Ho and Stoll (1983), the authors report that inside spreads on NASDAQ narrow significantly at the close of trading, and that this arises from a minority of dealers moving within the spread. Other studies looking at how the inventory imbalances and risk levels affect market maker quotes include Hansch, Naik and Viswanathan (1998), who empirically test Ho and Stoll (1983) using data provided by the LSE, and Bessembinder (2003), who uses data from the NYSE. Hansch, Naik and Viswanathan (1998) analyse the model put forth by Ho and Stoll (1983) and find empirical support that quote adjustments are highly correlated to changes in inventory positions, and that 4

5 inventory positions mean revert. Similarly, Bessembinder (2003), analysing on-nyse and off-nyse order imbalances finds that, in addressing order imbalances from the two markets, inventories of market makers move to sub-optimal levels, leading to greater inventory holding costs, reflected in spreads. Chordia, Roll, and Subrahmanyam (2001) and Bessembinder (2003) suggest that market makers also use competitive quotes to return inventory holdings to desired levels, such as posting aggressive bid quotes to decrease inventory or posting aggressive ask quotes to increase inventory. Inventory-driven quote competitiveness is most likely to occur at the close of trading as liquidity providers attempt to return to desired inventory levels, resulting in bid-ask spreads narrowing significantly at the market close. Duffy, Frino and Stevenson (1998) examine intraday patterns in spreads on the Sydney Futures Exchange, and find that spreads are highest at the open and lowest at the close. The low spreads at the close of trading is attributed to inventory management through the aggressive posting of quotes on the part of liquidity providers. Where inventory-based models are concerned with aliquidity provider s cost to hold surplus positions, information-based models focus on the potential adverse selection costs faced by liquidity providers in the presence of information asymmetry. Liquidity providers, as defined by Bagehot (1971), have less information regarding the fundamental value of securities, and as such they can expect to lose money when transacting against the informed and attempt to mitigate these risks by increasing spreads. Copeland and Galai (1983) argue that the dealer's quotedspread allows them to recoup potential losses to informed traders by trading against uninformed traders. They compare the pricing strategy of the dealer to an out-of-the-money straddle option for a fixed number of shares for a fixed time interval. Glosten and Milgrom (1985) show that, after controlling for transaction costs, dealer competition and idiosyncratic risk levels, adverse selection has an effect on the size of the spread. They also find that, when information asymmetry is particularly high, competing dealers 5

6 can elect not to post bid and ask quotes, while monopolistic dealers can subsidise losses to informed traders by adjusting their quotes when dealing with uninformed traders. Madhaven (1992) posits that as information asymmetry is highest at the commencement of trading, market makers will widen their spreads to account for the risk of trading against an informed trader. However, as the trading day progresses and information is impounded into securities, information asymmetry is reduced and therefore spreads are reduced. Building on the work of Madhaven (1992), Foster and Viswanathan (1994) develop a dynamic model that analyses strategic trading between relatively uninformed and informed traders. Assuming uninformed traders expect to trade against informed traders at the commencement of trading, the informed trader will only trade on widely known information in the early part of the trading day, and on their private information later in the trading day. Focusing on the U-shaped trade volume, Brock and Kleidon (1992) present a model where the demand for liquidity is both most inelastic and peaks at the open, driven by new overnight information that informed traders seek to act on and the close of trading, driven by the need to transact prior to the imminent close of trading. This allows a monopolistic market maker to widen spreads to meet the liquidity needs of traders and factor in the risk of trading against an informed trader. Chung and Chuwonganant (2001) examine the role of limit order traders in the marketmaking process on the hybrid NYSE, where specialists compete against other market participants to provide liquidity in the limit order book. The authors find that the U-shaped pattern in bid-ask spreads is driven by limit orders. The intraday pattern of specialist spreads are widest at the open, narrow until late morning, and then level off for the rest of the day. The empirical results are consistent with the prediction of Madhavan (1992) and Foster and Viswanathan (1994). As trading 6

7 continues, private information is impounded into prices, and specialists narrow their spreads as their informational handicap declines. McInish and Wood (1992) examine the intraday behaviour of time-weighted bid-ask spreads on the NYSE. Using a linear regression model controlling for trading activity, risk, information content and competition as significant determinants of the spread, as well as dummy variables for each half hour of trading, the authors find that spreads are higher at the start and end of the trading day. They conclude that other determinants drive the intra-day variability of spreads, supporting the contention of Brock and Kleidon (1992) that U-shaped spreads are driven by liquidity demand at the open and close of trading. Similarly, Foster and Viswanathan (1993) decompose the bid ask spread and focus on the intraday variation in the adverse selection cost component. The results show adverse selection costs follow the U-shaped pattern outlined in Brock and Kleidon (1992). A positive relationship is found between adverse selection costs, volatility and volume. These results support information asymmetry models of bid-ask spread patterns. Easley and O Hara (1987) and Glosten (1989) add the consideration of trade size as a tool for mitigating losses to informed traders. They show that, as informed traders will trade larger sizes to capitalise on their information advantage, market makers will offer less favourable prices to offset losses from trading against an informed trader. Harris (1990) points out that liquidity provision has both a price and quantity dimension, arguing that a market maker can adjust their liquidity by changing both their quoted bid-ask spread their quoted depth. Further, Ye (1995) and Kavajecz (1999) find that when the probability the specialist is providing liquidity to an informed trader increases, liquidity providers will both widen the spread and reduce depth to mitigate their potential losses to informed traders. 7

8 Lee, Mucklow and Ready (1993) posit that inferences regarding liquidity provision must contain an analysis of both spreads and depth. Lee, Mucklow and Ready (1993) analyse the relationship between spreads, depth and volume on the NYSE. They also analyse the effect of an information event (provide by earnings announcements) on these three variables. Volume and spreads follow an intraday U-shaped pattern while depth exhibits an inverse U-shaped pattern. The authors attribute these patterns to liquidity suppliers proxying the level of volume for the likelihood of trading against informed traders, thereby increasing their volume as greater presence of quoted spreads and reducing their depth to minimise their exposure to informed traders while recouping possible losses by trading against uninformed traders. Similarly, wider spreads and lower depth are observed before earnings announcements, consistent with information models that predict an increase in information asymmetry before information events. Foucault (1999) develops a model of price formation and order placement within a limit order market, wherein traders can post either limit or market orders. Foucault (1999) finds that the mix between market and limit orders is determined by the degree of price volatility. In periods of high price volatility, the likelihood of trading against an informed trader increases and as such, limit order traders are more likely to post less competitive quotes and/or lower depth to compensate for the risk of being picked off by informed traders. This leads to a direct relationship between price volatility and the increase in spreads and decrease in depth. Ahn and Cheung (1999) examine the daily spread and depth patterns on the Hong Kong Stock Exchange, which is a purely order driven market. They find a U-shaped pattern with spreads and an inverse U-shaped pattern with depth. Unlike the NYSE, there are no specialists, with the wider spreads and lower depth at the start and end of trading credited to limit order book liquidity suppliers who seek to recoup potential losses to informed traders by increasing the cost of the liquidity they provide. Similar intraday trend in spreads and depth are found in Vo s (2007) analysis on the Toronto Stock Exchange, 8

9 Chung and Zhao (2004) analyse the quote revision behaviour of NASDAQ market makers. The authors find that quote revisions peak in the morning and in the late afternoon, and posit that this is consistent with theinventory models put forth by Amihud and Mendelson (1980) and Ho and Stoll (1983), with the large number of quote revisions reflecting the market maker s attempt to reach desired inventory levels. 2.2 Low Latency, Algorithmic and High Frequency Trading Low-latency trading, the forebear of what is now classified as algorithmic trading and high frequency trading, has been an increasingly prevalent feature of financial markets since the advent of electronic trading. The academic literature focuses on these three main areas. Firstly, studies such as Riordan and Storkenmaier (2009) and Hendershott, Jones and Menkveld (2011) use proxies such as low-latency for algorithmic trading and study the effects of lower-latency in markets. Hendershott and Riordan (2009), Groth (2011) and others focus on algorithmic trading. Finally, Brogaard (2010), Jarnecic and Snape (2010) and Frino, Lepone and Mistry (2011) focus specifically on high frequency trading. With the increase in access to trading data, academics have been able to isolate the behaviour of algorithmic traders from broad low-latency trading and, recently, high frequency traders from algorithmic traders. Studies of low-latency trading primarily employ event-study analyses of market quality around reductions in latency on exchanges. Riordan and Storkenmaier (2009) analyse market quality on the Xetra platform of the Deutsche Bourse after a decrease in platform latency, which they proxy as an increase in algorithmic trading activity. Using trade and order book data provided by SIRCA, the authors find an increase in liquidity after the systematic change, coinciding with lower price impact of trades and increased trading volume. Empirically, the authors deem this to be beneficial for both the exchange and participants. 9

10 Other studies have analysed the effect a reduction in latency has on market quality in earlier time periods. Easley, Hendershott and Ramadorai (2009) examine the impact on stock prices of an upgrade in the infrastructure on the NYSE introduced in The upgrade consisted of two phases; phase 1 introduced on 14 July, 1980 improved dissemination of quotes and the reporting of floor transactions to off-floor traders, phase 2 introduced a technology upgrade that reduced latency from 2 minutes pre-upgrade to 20 seconds post-upgrade. The upgrades reduced the trading option granted by limit order traders to the specialist on-floor traders. The authors hypothesise that limit order traders require compensation for adverse selection, the upgrades should be associated with positive abnormal stock returns. For phase 2, the results indicate that the total return over the next 20 days was 4 percent, and this excess return result is robust to Fama French, momentum and industry factors. A reduction in latency is therefore associated with a reduction in adverse selection risk and an improvement in market quality. Similarly, on 24 June, 2007, the NYSE converted to a hybrid market system, where trades could take place on the trading floor or electronically. The introduction of the Hybrid market reduced the execution time of market orders from 10 seconds to less than a second. Easley, Hendershott and Ramadorai (2009) find that the reduction in latency on the NYSE had mixed effects on market quality. On average, from the month prior to the stock s activation date to the month after, quoted spreads increase from 7.9 basis points to 8.3 basis points, and effective spreads increase from 5.6 basis points to 5.9 basis points. Decomposing the spread into the realized spread and adverse selection, the authors find that the higher spread is a result of adverse selection. However, the authors also note that price noise dropped after the introduction of the Hybrid system, indicating an improvement in price efficiency. Hasbrouck and Saar (2011) analyse trading activity in the millisecond environment. Using Nasdaq order-level data, every order entry including submission, cancellation and execution of an order are time-stamped to the nearest millisecond. Hasbrouck and Saar (2011) find that the millisecond environment is characterized by two sets of traders; traders who operate according to a schedule (e.g trading orders in smaller trade sizes), and traders that respond to market events. The 10

11 authors construct a measure of low-latency activity by tracking submissions, cancellations and executions, and analyse how this measure affects market quality. In a similar vein to the empirical literature on AT, market quality is assessed using traditional measures including quoted spreads, market depth, price impact and short term volatility. Employing a simultaneous equation framework, the authors find that a decline in latency is associated with tighter quoted spreads, increased depth, reduced price impact and lower volatility. Hasbrouck and Saar (2011) split the sample into two periods; October, 2007 during which prices were flat or increasing, and June, 2008 when prices were declining. A decrease in latency increases market activity across both market environments. Initial studies concerning algorithmic trading focus on the effect algorithmic trading can have on transaction costs. Kisell and Malamut (2006) argue that an important use of algorithmic trading models is to aim at achieving or beating a specified benchmark for their executions. Bertsimas and Lo (1998) find that the optimal strategy for traders attempting to transact large volumes with minimal execution costs is to break the order into smaller pieces. Konishi (2002) develops an optimal slicing strategy for VWAP trades prior to the rise of AT. Domowitz and Yegerman (2005) show algorithmic trading is less expensive than alternative means based on a measure of implementation shortfall. Algorithmic trading however underperforms human execution for order sizes greater than 10% of average daily volume. VWAP algorithms have an underperformance of 2bps relative to the VWAP benchmark, but the authors suggest that this can be compensated by the lower fees attached to computer algorithms relative to human brokers. Smith (2010) reveals the increase in algorithmic trading on U.S markets has resulted in a marked increase in the correlation structure of stock trading, leading to an increase in short-term volatility. Smith (2010) examines the Hurst exponent of traded value over short time scales (15 minutes or less). The Hurst exponent measures the autocorrelation of a time series and the rate at which these decrease as the distance between two values increases. Over short time scales, the 11

12 Hurst exponent is found to have changed from a previous Gaussian white noise value of 0.5 to greater than 0.5. This implies that correlations that occurred over hours or days now occur over time scales of seconds or minutes. The author shows that the increase in the Hurst exponent of U.S stocks occurs prominently after the implementation of Order Protection Rule (Rule 611). This rule mandates that trades are to automatically trade at the best price offered across all exchange venues, and lead to a substantial growth in AT. A Hurst Exponent greater than 0.5 points leads towards increasing volatility on the U.S market, as more participants in the market generate more volatility, not more predictable behaviour. Jovanovic and Menkveld (2010) develop as a theoretical model of algorithmic traders as market makers in electronic limit order markets, and assess the effect this role has on investor welfare. In limit-order markets without middlemen, newly placed limit orders are either matched with existing limit orders or are added to the order book. The placement of a limit order faces the risk that the order becomes stale due to the arrival of new information, creating a trading option that may be picked off by a later investor. Traders in limit order markets therefore face adverse selection costs, which hampers trading activity. As AT is the use of computer algorithms to analyse market data and make trades, the introduction of algorithmic traders to a limit order market may reduce information friction if the information between two investor arrivals is hard, machineprocessable information. To take the case of a seller, the seller passes off his security to an AT instead of placing a limit order, with the AT instead posting the limit order who can immediately update the quote based on incoming hard information, thus removing adverse selection costs. Alternatively, AT may reduce investor welfare if the there is no information friction between the early and late investor with respect to hard information. Jovanovic and Menkveld (2010) assess the validity of this model using the natural experiment provided by the introduction of Chi-X to compete with Euronext. The features of Chi-X make it attractive to AT, as it provides a subsidy to a quote that leads to execution, relative to Euronext who charge a fee for price quote changes. The authors find 12

13 that entry of middlemen to the market was accompanied by a 23% reduction in adverse selection costs and a 17% increase in trade frequency. Hendershott, Jones and Menkveld (2011) analyse algorithmic trading after the introduction of autoquote on NYSE and their study produces qualitatively similar results to Riordan and Storkenmaier (2009). They proxy the level of algorithmic trading with the normalised level of message traffic and their findings suggest a relationship between increased algorithmic trading, lower trading costs and increased quote information. Gsell (2008) uses simulated data to determine the effect of low-latency algorithmic trading on volatility. The author s model shows that low-latency trading can reduce volatility, specifically in times of low volumes. Conversely, periods of high volume resulted in low-latency trading having an adverse effect on volatility. Hendershott and Riordan (2009) study the role of algorithmic trading in the price discovery process using order-level data from the Deutsche Bourse s Automated Trading Program, which allows them to identify algorithmic traders. Algorithmic traders supply and demand liquidity at approximately equal levels, although their provision of liquidity increases as it becomes more expensive. Like Prix, Loistl and Huett (2007), they find broad trade patterns in algorithmic trading, specifically trade clustering, which indicates that algorithmic traders could be following similar strategies. Groth (2011) studies 30 DAX stocks to determine the effect of algorithmic traders on volatility and, like Gsell (2008) finds that algorithmic traders do not have a greater impact on volatility than human traders and they do not withdraw liquidity during periods of stress. Unlike Hendershott and Riordan (2009) and Prix, Loistl and Huett (2007), Groth (2011) finds that algorithmic traders employ diverse strategies, like their human counterparts. Gsell (2008), using a sample of trades and orders in DAX 300 stocks with identifiers for algorithmic traders, finds that algorithmic traders use smaller order sizes that are frequently modified to stay at the best quotes in the order book. 13

14 Focusing on foreign exchange markets, Chaboud, Chiquoine, Hjalmarsson, Vega (2009) analyse the effect of algorithmic traders on volatility and price discovery. The authors find little relation between algorithmic trading and volatility, with most price variation coming from human traders. Algorithmic trades are shown to execute strategies that are correlated (similar to Hendershott and Riordan, 2009 and Prix, Loistl and Huett, 2007). During periods of exogenous impacts on liquidity (proxied by US macroeconomic announcements), the authors show that algorithmic traders provide a greater proportion of liquidity. Using a return order-flow dynamics approach in a vector autoregressive (VAR) framework, the authors find that algorithmic traders do not have a significant effect on price discovery in the foreign exchange market. Focusing specifically on high frequency traders (HFT), which are viewed as entities using proprietary capital in conjunction with low-latency algorithmic trading to generate a return on capital, Cvitanic and Kirilenko (2010) build the first theoretical model to address how HFT impact market conditions. They model an electronic market populated by low frequency traders (humans) and then add a high frequency trader (machine). This machine is assumed to be uninformed, similar to a market maker. The advantage of the machine relative to a human trader is its higher speed in submitting and cancelling orders. The authors find that the presence of high frequency traders yield transaction prices that differ from the HFT-free price; when a HFT is present, the distribution of transaction prices will have thinner tails and are concentrated near the mean. Their second finding is that as humans increase their order submissions, intertrade duration decreases and trading volume increases in proportion to higher human order arrival rates. The implication is that the presence of high frequency traders leads to an increase in liquidity. Borgaard (2010) conducts a study on the NasdaqOMX market. Trade and order book data identifies high frequency traders. He finds that HFT demand and supply liquidity in equal proportions, with HFT more likely to trade in higher capitalisation stocks, with lower spreads and greater depth. HFT post quotes at the best prices approximately 50% of the time and HFT liquidity 14

15 supply is shown to smooth volatility, while HFT liquidity demand does not exacerbate volatility. Employing a similar VAR framework to that of Chaboud, Chiquoine, Hjalmarsson, Vega (2009), the author finds that HFT trades and quotes contribute more to price discovery than non-hft. Kirilenko et al. (2011) study the role HFT had in the flash crash of 6 May, The flash crash resulted in the biggest one day point decline (998.5) in the history of the Dow Jones Industrial Average. The cause of the crash, according to Kirilenko et al. (2011), was a sell order initiated by a large fundamental trader at 2.32pm on the E-Mini S&P 500 futures contract. The sell order consisted of a total of 75,000 E-Mini contracts, used to hedge an existing equity position. This sell order, one of the largest of the year, was executed rapidly over the next twenty minutes. This lead to a large fall in the E-Mini Index and equity markets of approximately 3% in just 4 minutes, from the beginning of 2:41 p.m. through the end of 2:44 p.m. The report noted that computerized trading was a contributing factor of the flash crash, with HFT being net sellers as prices declined, accentuating the fall in prices. HFTs were thought to be a primary contributor to this period of intense volatility. HFTs contributed to the price decline as they were initial buyers of the sell order, but quickly became aggressive net sellers to balance their inventory positions. The results show that HFTs exhibit a number of characteristics that can have a negative impact on market stability. They exhibit trading patterns inconsistent with traditional market makers, trading aggressively in the direction of price changes, and do not accumulate significant inventory positions. Thus, HFTs do not supply liquidity when prices move against their trading position. Further, they can exacerbate price movements by competing for liquidity as they try to rebalance their inventory positions. Employing two proprietary datasets from Chi-X and Euronext, that contain anonymised broker IDs for trades in Dutch index stocks, Menkveld (2012) examines the impact of HFT on these two markets. The author identifies a trader that enters both markets simultaneously, who fits the profile of an HFT. The trader has an upper bound latency of 1.67 milliseconds, engages in proprietary trading, generates a high number of trades, and finishes the trading day with a net zero inventory 15

16 position. Menkveld (2012) notes that the entry of the HFT trader coincided with a 50% fall in the bidask spread, though causality is not proven. The activities of the market maker show it acts primarily as a multi-venue market maker. The HFT contributed to liquidity across both markets, supplying liquidity 80% of the time. Jarnecic and Snape (2010) analyse the characteristics and determinants of HFT on the London Stock Exchange. Using time-sequenced messages indicating the nature of the participant, they find that HFT execute significant volume on the LSE, particularly in large capitalisation stocks with greater fragmentation of order flow (ie across competing platforms), higher intraday volatility and smaller tick sizes. HFT supply liquidity when spreads are wider and during periods of high volatility (as found in Brogaard, 2010). Conversely, HFT are less prevalent around corporate news announcements and high volume periods. Frino, Lepone and Mistry (2011) analyse HFT in the context of the Australian equity market. Focusing on the ASX 200, the study focuses on HFT trends, the relationship between HFT and volatility and the determinants of HFT activity. They find an exponential increase in HFT activity in the Australian market in the period, with HFT trades characterised by small volumes, often being net suppliers of liquidity. HFT liquidity demand is positively correlated to volatility, whilst HFT liquidity supply is negatively correlated with volatility. Overall HFT participation increases when higher volumes are traded in a deeper order book, when prior day HFT participation was high and in higher capitalisation stocks. Higher dollar volumes, higher proportionate bid-ask spreads and higher volatility reduce HFT participation. 16

17 3. Data This study analyses HFT activity in the FTSE 100 throughout the UK trading day. The data used for this study is provided by the UK Financial Services Authority and contains trade data and order book data from the London Stock Exchange (LSE), Chi-X Europe and BATS Europe for all constituents stocks in the FTSE 100 for 30 trading days in The data has been anonymised at the platform level and aggregated into two categories of trading firms: HFT Other Trader. Prior studies, such as Hendershott, Jones and Menkveld (2010), Brogaard (2010) and Jarnecic and Snape (2010) focus on a single platform in a fragmented market, while these platforms account for the vast majority3 of volume executed in the UK, presenting a unique opportunity to analyse the trading and order book behaviour of High Frequency Traders in a consolidated market. The platform-level order book reconstruction uses time-stamped (nearest millisecond) order, trade, cancellation and amendment data to reconstruct the top three levels of the platformlevel order book at each new event. Each message is classed by the platform as being submitted by either HFT or OT based on the above definition of HFT and anonymised in-house. At each price level of the order book, the total HFT and Other Trader (OT) volumes are displayed. The combined order book is then constructed by sampling price and volume levels at the platform level at each new event. The final order book contains the national best bid and ask prices, HFT and OT volumes. The consolidated trade data is an amalgamation of all platform-level trades, with the liquidity supplier and liquidity seeker classed as either HFT or OT in-house, based on the definition of HFT given above. Each trade message contains the date, time (to the nearest millisecond), price, volume, buyer type, seller type and initiator type. In order to class a trader as a HFT or OT, the platform and the FSA co-operate to class the list of participants in each platform as either HFT or OT, based on the platform s understanding of the business of the participant, with reference to the provided definition of HFT. The FSA provides the 3 According to Thompson Reuters Market Share Report, LSE, Chi-X and BATS accounted for 86% of total lit volume in the UK in

18 platform with a list of all participants and their trader type mapping and the data is anonymised at the platform level to ensure confidentiality of participants. Out of a total of 1452 unique identifiers, 363 are classed as HFT for the purposes of this study, consisting of 52 firms. An inherent limitation of the data is that HFT desks within firms whose trading contains a mixture of proprietary and agency trading cannot be segregated. The result of this is an underestimation of total HFT activity as HFT arms within investment banks and other financial institutions are classified as OT. However, in light of the significant levels of HFT evident in the sample and at the risk of taking a subjective view as to what constitutes HFT in the remaining OT orderflow, this paper takes the approach of studies such as Brogaard (2010) and Jarnecic and Snape (2010). 4. Methodology 4.1 Univariate This study analyses HFT throughout the trading day in London. Continuous trading commences at 8:00am and ends at 4:30pm. Descriptive statistics are calculated on one-minute intervals across the stock sample. HFT participation in the order book is analysed to determine latent HFT liquidity supply, while HFT participation in trading is analysed to determine the changes in HFT participation, HFT liquidity supply and demand. The same analysis is conducted for OT. Order book analysis focuses on latent HFT liquidity supply in the order book, by analysing HFT depth at best bid and ask prices and the time spent at the best quotes. Depth at the best bid and ask price is calculated as the time-weighted depth at the national best prices for HFT and OT, per second and averaged per minute across all stocks in the FTSE 100. Depth is calculated as volume in shares. Time at the best bid and ask is calculated as the sum of milliseconds per second that HFT and OT are at the best bid and ask, averaged across stocks. 18

19 Participation in trading is calculated as the percentage of total volume in which a HFT (OT) was one or both sides of the trade. For each stock, the mean is taken for each interval and then averaged across all stocks and announcement and non-announcement days. Participation is calculated on a trade, volume and value basis. HFT (OT) liquidity supply is calculated as the total volume of liquidity supplied by HFT (OT) divided by total liquidity supplied, per minute, per stock. HFT (OT) liquidity demand is similarly calculated. 4.2 Information Share of HFT and OT The role of HFT and OT quotes in price discovery of cross-listed and non cross-listed securities across the trading day is analysed using the Information Share methodology pioneered in Hasbrouck (1995). Originally used to determine the contribution to price discovery by competing markets, Information Share has been used in Brogaard (2011) and Hendershott and Riordan (2009) to study the relative contribution to price discovery by HFT/AT and other trader types and is equated to the share of total price discovery. To determine the Information Share of HFT and OT quotes in a given security i, HFT and OT quote time series are constructed at each quote update time t from the midpoints of HFT and OT quotes: (1) The Information Share is measured as the contribution of HFT/OT price series variance to the total variance of the common random walk component in the below equations: (2) (3) 19

20 wherepi,t is the price vector of each trader type and mi,t is the common efficient price, following a random walk: (4) For each trader type, the price vector can be shown as a Vector Moving Average model: (5) (6) where and is the information being impounded by HFT and OT, with being a 2x1 vector of price innovations with a zero mean and a variance matrix of Ω. The variance of the random walk component is: where and represents the polynomial in the lag operator. Expanding equation 12, the variance of the random walk component becomes: (7) [ ] [ ] [ ] (8) As HFT and OT prices in the order book are updated every time either trader type changes their bid or ask price (timestamped to the nearest millisecond), the random-walk variance can be attributed to either HFT or OT. There should be no contemporaneous correlation between HFT and OT price changes. However, to control for instances where, at the millisecond level, price changes occur contemporaneously, the construction of upper and lower bounds as per Hasbrouck (1995) is adopted. In terms of the analysis of HFT and OT Information Shares, to control for contemporaneous correlation, Hasbrouck (1995) considers the upper bound HFT to be the assumption that all price variance is attributable to HFT and the lower bound HFT to be the assumption that all price variance is attributable to OT. The upper and lower bound for each stock are averaged to arrive at the Information Share for each trader type over the trading day. Statistical significance tests are run based on the 30 trading days, adjusting for heteroskedasticity and autocorrelation using the 20

21 methodology outlined in Newey and West (1994). Finally, mean Information Share is calculated across the FTSE Multivariate To control for both endogenous and exogenous factors, regressions are estimated inclusive of factors which have been identified to affect HFT activity. As per past HFT studies such as Brogaard (2010) and Chaboud, Chiquoine, Hjalmarsson, Vega (2009), the determinants of HFT presence in the limit order book and HFT participation in trades, such as volume traded, volatility and market capitalisation are included. The dependant variable, the control variables representing total volume traded and volatility are standardised as per Chan et al (1995b). This method controls for time-ofday changes in these variables, by comparing them to a benchmark period specified as 12:00pm 12:15pm on each day analysed. Specifically, each variable is standardised by subtracting the mean of the benchmark period and dividing the result by the standard deviation of the control period. The regressions are estimated for the thirty-four 15-minute intervals throughout the trading day (with the benchmark 15-minute interval excluded) to gain an understanding of HFT and OT activity throughout the trading day, relative to this benchmark. The standardised variables are regressed using the following regressions that utilise minute-by-minute means per stock, per day: ( ) ( ) ( ) (9) ( ) ( ) ( ) (10) Where ( ) is the standardised depth at the best bid and ask quotes for stock i at time interval t and ( ) is the standardised proportional time per second spent at the best bid and ask quotes for stock i at time interval t. The control variables isolate the effect of 21

22 endogenous and exogenous effects on the dependant variables, with ( ) being the standardised volume of shares traded in stock i at time interval t, ( ) is the standardised price volatility (measured as the natural logarithm of the highest traded price divided by the lowest traded price) stock i at time interval t and is the market capitalisation (in millions of British Pounds) for stock i as at January The thirty three binary variables each take the value of one if the observations occur during that interval, zero after, and is an error term. This multivariate estimation allows for the analysis of HFT and OT behaviour in each 15-minute interval, relative to volume, volatility, market capitalisation and benchmark HFT and OT behaviour. Similar multivariate regressions, aimed at analysing HFT participation, liquidity supply and liquidity demand throughout the trading day trading, are estimated on a trade level and a volume traded level: ( ) ( ) ( ) (11) ( ) ( ) ( ) (12) ( ) ( ) ( ) (13) where ( ) is the standardised percentage trading where a HFT takes one or both sides in the transaction, ( ) is the standardised percentage of all trading where HFT acts as the liquidity supplier, and ( ) is the standardised percentage of all trading where HFT acts as the liquidity demanding party. The independent variables are as described above. 22

23 5. Order book Results 5.1 Univariate Results Figure 1 presents the results of the analysis of the average HFT and OT depth at the best bid and ask quotes throughout the London trading day. Throughout the trading day, HFT depth at the best quotes is significantly lower than OT. Average depth for both market participants exhibits an upward gradient as the trading day progresses. HFT depth is lowest at the commencement of trading, with approximately 2,500 shares posted at the best quotes. HFT depth at the best quotes increases to approximately 10,000 shares by 11:00am, and stays flat until 1:00pm. From this point, HFT exhibits a broad increase for the remainder of the trading day, before experiencing a decline in the final 15 minutes of London trading. HFT depth also drops at 1:30pm as half the sample days contain US macroeconomic announcements which affect London trading. Further, with the commencement of US trading at 2:30pm, HFT depth exhibits a slight decline, followed by a U-shaped recovery in the last two hours of London trading. Figure 1 Depth at Best Bid and Ask Quotes (Shares) 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5, :01 8:31 9:01 9:31 10:01 10:31 11:01 11:31 12:01 12:31 1:01 1:31 2:01 2:31 3:01 3:31 4:01 HFT OT 23

24 OT depth at the best quotes, spiking at the commencement of the London trading day, follows an otherwise similar pattern to HFT depth throughout the trading day. Interestingly, at 1:30pm, OT depth experiences a significant decline, recovering just prior to the commencement of US trading, at which time it also exhibits a U-shaped pattern to the close of trading. Both HFT and OT depth fall significantly in the closing minutes of London trading. As noted in Lee, Mucklow and Ready (1993), after the commencement of US trading, London depth follows the traditional U-shaped pattern for both HFT and OT, as London appears to fall in line with US trading and price discovery. Figure 2 presents results of the analysis of the average HFT and OT time at the best bid and ask throughout the London trading day. HFT spend significantly less time at the best bid and ask quotes than OT. As was evidenced in the analysis of depth at the best quotes, HFT time at the best quotes is lowest in the first half hour of trading. By 9:00am, HFT time at the best quotes approaches the 45 50% range, where it stays until the commencement of US trading at 2:30pm. After the US markets open, there is a significant spike in HFT time at the best bid and ask quotes, and a structural shift in HFT time at the best quotes, to approximately 55%, trending up until the last 15 minutes of trading, where it trends down at the end of the day to less than 50%. Like HFT, OT time at the best quotes is lowest at the start of the trading day. From 10:00am, until the hour before the commencement of US trading, OT time at the best quotes stabilizes at approximately 85%. OT time at the best bid and ask quotes experiences significant volatility from the hour prior to the commencement of US trading, until the close of London trading at 4:30pm. At 1:30pm, OT time at the best quotes declines considerably, to approximately 78%, and after the commencement of US trading, exhibits a U-shaped pattern in the last two hours of London trading, before finishing the day at approximately 73%. HFT participation in the order book is quite stable throughout the trading day, escalating after the commencement of US trading (2:30pm London time). 24

25 Figure 2 Time at Best Bid and Ask Quotes 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 8:01 8:31 9:01 9:31 10:01 10:31 11:01 11:31 12:01 12:31 1:01 1:31 2:01 2:31 3:01 3:31 4:01 HFT OT 5.2 Information Share Results Using the methodology pioneered by Hasbrouck (1995), and used in Brogaard (2010), Hendershott and Riordan (2009) and Chaboud, Hjalmarsson, Vega and Chiquoine (2009), the Information Share of HFT and OT for each stock in the FTSE 100 is analysed. This approach analyses how HFT and OT quotes contribute to price discovery. The results for each stock, and the average across the FTSE 100, are given in Table 1. For brevity, only the average of the mid-quotes is shown for HFT and OT, with HFT and OT Information Share summing to one. The t-statistics are based on the difference in Information Share for HFT less OT, and the time series correlation is accounted for using Newey-West standard errors. Of the 100 cross-listed securities, HFT information Share is significantly higher (at the 5% level) in 91 stocks, and no stocks exhibit a significantly higher OT Information Share. Across the entire FTSE 100 sample, HFT Information Share is on average 66.11%, significantly higher than the OT Information Share of 33.89%, at the 1% level of significance. 25