HFT Identification: Literature Review

By Maria Novikova

Regulatory definition of HFT

One of the first regulatory definitions of HFT was proposed by the Security Exchange Commission (SEC, 2010). HFT is referred to professional traders acting in a proprietary capacity that engage in strategies generating a large number of trades on a daily basis. Key characteristics attributed to HFT are:

• Extraordinarily high-speed and sophisticated computer programs for decision making, order initiation, generation, routing, or execution, for pursuing a number of different strategies, ranging from market making to arbitrage without constant human control;
• High message traffic. HFTs can be characterized by high participant-to-market message rates (orders submissions, amendments, and cancellations), a high number of trades, high daily portfolio turnover, and others metrics;
• Low latency. Latency is required for implementation and execution of successful strategies depending crucially on the ability to be faster than competitors. HFTs use co-location services and individual data feeds offered by exchanges to minimize network and other types of latencies;
• Near-flat end-of-day net positions. HFTs typically do not carry significant, unhedged positions overnight, trying to end the trading day as close to a flat position as possible;
• Short time frame for trading for establishing and liquidating positions. HFT positions are often held for as little as a fraction of a second;
• Proprietary trading. HFTs could be organized in a variety of ways, typically as a proprietary trading firm, a proprietary trading desk of a multi-service broker-dealer or as a hedge fund.

Major regulatory HFT definitions are summarized in Table 1.

Table 1. Key characteristics of HFT definitions

Academics papers (see for example Brogaard 2010; Fabozzi, Focardi, Jonas 2010; Chlistalla 2011; Golub 2011) commonly characterize HFTs as fastest algorithms which generate a large number of orders and trades with a short holding period of positions.

Predefined HFT identification

There are many studies examining the behavior and impact of HFT on the financial market. Most researchers use transaction and order flow data with HFT attributed records flagged by exchange or other data providers.

For example, there is a large set of studies analyzing data provided by NASDAQ OMX and New York Stock Exchange (NYSE). NASDAQ OMX provides the HFT data to academics under a non-disclosure agreement. These datasets contain trades records time-stamped to the millisecond and identifying the liquidity demander and supplier as a high-frequency trader or non-high-frequency trader. Firms are categorized as HFT based on NASDAQ OMX knowledge of their customers and analysis of firms’ trading, such as how often their net trading in a day crosses zero, their order duration, and their order to trade ratio (i.e. NASDAQ OMX cannot identify all HFTs). Firms not included are those that also act as brokers for customers and engage in proprietary lower-frequency trading strategies, e.g., Goldman Sachs, Morgan Stanley, and other large integrated firms. HFT who route their orders through these large integrated firms cannot be clearly identified so they are also excluded. The 26 HFT firms in the NASDAQ OMX data are best thought of as independent proprietary trading firms (Hendershott, Riordan, 2009; Brogaard 2011a; 2011b; 2012; Zhang 2010).

There is more detailed information available from NASDAQ OMX, which identifies trading by individual HFT firms (Hirschey, 2013). Trade records on exchanges include a Market Participant Identifier (MPID) indicating the broker/dealer making the trade. One consequence of these arrangements is that it is possible to observe the activity of sponsored access customers directly rather than at the aggregated broker level. The firms classified as HFTs typically use low-latency connections and trade more actively than other investors.

Hagströmer and Nordén (2013) use unique data from the NASDAQ OMX Stockholm exchange, allowing them to associate each message with a trader identity. NASDAQ OMX in‐house expertise grouped market member firms into three categories:

1. Members who are primarily HFTs, i.e. engage only in proprietary trading and who use algorithms in their trading strategies;
2. Members who primarily trade for clients;
3. Members who engage in both proprietary and client trading.

The advantage of classification with respect to the US dataset is that, along with the HFT group, they also identify a group that is free from HFT activities (members who primarily trade for clients).

Benos and Sagade (2012) analyze data from the SABRE database. The SABRE database is maintained by the UK Financial Services Authority (FSA) and contains information on transaction prices, sizes, time, location and counterparty identity. It also identifies the buyer and the seller in each transaction as well as whether the counterparties execute a given transaction in a principal or agent capacity. The SABRE database describes the trading activity of all firms directly regulated by the FSA as well as that of firms that trade through a broker (because brokers are regulated and as such must report their clients’ transactions). SABRE include the trades of some of the largest HFTs. Knowledge of the identities of the counterparties in their sample allows identifying HFTs. To do that, they use press reports and the companies’ websites to see which of them are described as HFTs or define themselves as such. For instance, if a company defines itself as a “High-Frequency Trader (HFTs)” (or alternatively a “low-latency trader” or an “electronic market maker”) then they classify it as an HFT.

Other examples of predefined agent types in data come from datasets from EBS and Xetra, which divided all agents into algorithmic trading agents (AT) and human agents (HA) (Chaboud, Chiquoine, Hjalmarsson, Vega, 2009; Hendershott and Riordan, 2012). In a research on the EBS market, authors use transactions data at a one-minute frequency. The data contains information about:

• volume of trade
• direction
• computers and humans flag, which informed about counterparties of deals
• maker and taker flag, which informed who initiated the deal

This is informative data, but agents have predefined types. The article on Xetra market distinguishes algorithmic trading using information about fees. In fact, in December of 2006, the Deutsche Börse (DB) introduced its fee rebate program for automated traders. Agents using this program were flagged as algorithmic traders.

Algorithmic HFT identification

While most of the authors conduct research on market data resolved by agents’ types, there is a small, but important, number of studies that implement algorithmic HFT identification based on agent-resolved data. This is particularly relevant for us since we have access to agent-resolved data for the Singapore market.

1. Kirilenko-Kyle methodology

The approach of Kirilenko, Kyle, Samadi & Tuzun (2011) divides all agents into six categories based on their trading activity:

• High-Frequency Traders (high volume and low inventory)
• Intermediaries (low inventory)
• Fundamental Buyers (consistent intraday net buyers)
• Fundamental Sellers (consistent intraday net sellers)
• Small Traders (low volume)
• Opportunistic Traders (all other traders not classified)

An account is identified as Fundamental Traders when its end of day net position exceeds 15% of its total trading volume during the day. It is further categorized as Fundamental Buyers or Fundamental Sellers depending on their trading directions.

Intermediaries accounts are identified with the following two thresholds: 1) net holdings fluctuate within 1.5% of its end of day level, and 2) end of day net position is no more than 5% of its daily trading volume.

HFTs are defined as the top 7% of Intermediaries according to daily trading volume.

Small Traders are defined as accounts with a trading volume of 9 or fewer contracts. All other accounts are named Opportunistic Traders.

By using this classification, authors represented graphically the ecology of different account with the plot shown in Figure 1. On the x-axis authors reported the net end of day position scaled by market trading volume, while on the y-axis they reported the total volume traded by the account. The clouds of points in the plane are then clustered in the 5 groups of large trader described above. It is clear that HFTs emerge as very active accounts (in terms of traded volume), but ending the day with an inventory very close to zero. According to the authors, HFTs, are clearly distinguishable from the other types of accounts.

Figure 1. Trading account classification based on Trading Volume and Net Position Scaled by Market Trading Volume (Kirilenko et al., 2011)

This approach was later developed by Baron, Brogaard & Kirilenko (2012). The following four criteria must be satisfied each day by an account to be considered as an HFT:

• trade more than 10,000 contracts;
• have an end-of-day inventory position of no more than 2% of the total contracts the firm traded that day;
• have a maximum variation in inventory scaled by total contracts traded of less than 15%;
• labeled as engaging in HFT activity in at least 50% of the days it trades and must trade at least 50% of possible trading days.

2 IIROC methodology

The Investment Industry Regulatory Organization of Canada presented the study of High-Frequency Trading Activity on Canadian Equity Marketplaces (IIROC, 2012). The study identifies High Order-to-Trade (HOT) group, which is considered as a proxy for the group of HFT agents having an order-to-trade ratio greater than approximately 11:1.

Figure 2 shows the scatter plot of order-to-trade ratio vs the number of trades for each account in the period from 1 August to 31 October 2011. For each account for each month, the number of trades is plotted against the number of new and amended orders. A total of 947 observations (marked red) are included in the HOT group, representing 11% of the agents’ population.

The HOT Group accounted for 94% of the new and amended orders entered during the study period, 42% of the number of trades and 32% of the traded value.

Figure 2. The order-to-trade plot of Canadian market by account (IIROC, 2012)

The major drawback of HOT classification as a proxy for HFT is that it doesn’t imply message rate threshold, therefore (a) embraces slow human agents with low message traffic (e.g. less than 100 per month), (b) drops off high message rate (i.e. 1 million orders per month) agents with lower order-to-trade ratio.

3 ASIC methodology

In its recent research, the Australian Securities and Investments Commission has provided a benchmark view on dark liquidity and high-frequency trading (ASIC, 2013). ASIC defined HFT by using a set of metrics:

• order-to-trade ratio, which is the number of orders submitted to market (new orders, amendments, and deletions) divided by the number of trades executed;
• percentage of turnover traded within the day, which is one minus the residual position divided by total turnover in each security;
• total turnover per day, which is the total dollar value bought plus the total dollar value sold;
• the number of fast messages, which is the absolute number of messages successfully submitted within a 40-millisecond window from a defined event;
• holding time, which is volume weighted time that a position is held;
• at-best ratio, which is the number of orders that are placed at the best price plus the number of orders priced at market divided by the total number of orders.

Very small and infrequent traders with less than 1,000 orders or less than $1,000 total turnover or an average holding time were excluded from analysis. Each trader was scored on its relative position within the distribution of each metric. A value in the top quartile was assigned four points, a value in the third quartile was assigned three points, and so on. Summing all scores for each trader gave a high-frequency trading index for that trader for that day. Scores ranged from 24 to 6. Traders were then ranked by score and the highest 15% were classified as HFT for that day. The number of accounts classified as HFT varied between 45 and 70.

The analysis was based on trading of equity securities within the S&P/ASX 200. The S&P/ASX 200 was chosen because it represents 95% of total equity turnover in the Australian market, and it includes the most liquid securities. The period analyzed was from 1 January to 30 September 2012. The study has found that high-frequency trading in Australia is dominated by a small group of trading entities, with the 20 largest high-frequency trading entities accounting for approximately 80% of all high-frequency trading turnover (or 22% of total equity market turnover).

Figure 3 shows a scatter plot of orders-to-trades ratio versus the number of trades for both high-frequency traders and all other traders across a large number of securities during a period from 2 to 6 July 2012.

Figure 3. Order-to-trade plot of the Australian market by account (ASIC, 2013)

The main drawback of ASIC methodology is that the threshold (top 15%) seems quite arbitrary and it is not well justified. Moreover, dynamically defined classification may result in the inconsistency of the HFT group in time, since population stability of the group is not controlled.

4 HFT classification

Various studies propose approaches to build HFT classification based on their anticipated strategies. HFT firms are private and information on how they trade and what strategies they use is not public, hence some specific methods of trading can have lack of description. Description of HFT typology can be useful for further classification of HFT strategies in Singapore market. However, the classification is market-specific, because some strategies can be implemented only in some markets. For example, rebate trading requires a favorable fee structure, which is present only in some markets (for example in the US).

CFTC Technology Advisory Committee (2012b) defines 4 groups of strategies, which may be used by HFT (see Figure 4). This classification is one of the most complete typology of HFT. All these strategies depend on latency aspects of execution because market inefficiencies or some arbitrage opportunity exist only in the scale of seconds and milliseconds.

Figure 4. A Taxonomy of HFT strategies (CFTC, 2012b)

ASIC (2010) proposes a similar classification of HFT strategies:

• Statistical arbitrage — seeks to exploit pricing inefficiencies either between related assets or markets.
• Electronic liquidity providers — involves making a two-sided market with a view to profiting by earning the bid-ask spread.
• Liquidity detection — seeks out whether there are large orders existing in a matching engine. Some liquidity detection strategies are described as ‘predatory’ in nature.

Brandon Rowley (t3live.com — see here) classifies HFT agents into:

1. Liquidity Providing

• Rebate Trading — provides liquidity in stocks by posting bids and offers in order to collect ECN rebates without requiring capital gains;
• Market Making — a regulatory exemption to reduce spreads by creating a more liquid market;

2. Trading the Tape

• Filter Trading — monitors stocks that show significant changes in movement and/or volume;
• Momentum Trading — identifies temporary supply-demand imbalances and trades with short-term momentum as equilibrium is restored;

3. Statistical Trading

• Statistical Arbitrage — exploits pricing differentials between correlated securities and markets;
• Technical Trading — discovers technical setups based on pre-defined recurring patterns in stocks prices.

Hagströmer and Nordén (2013) propose the following metrics to classify HFT agents:

• Absolute day‐end inventory (the sum of all signed trading volumes) divided by daily trading volume;
• Order‐to-trade ratio, defined as the number of quotes (limit order submissions) divided by the number of trades (executions) during the continuous trading session for a given day and stock;
• Minimum limit order lifetime;
• Passive trades, which denotes the fraction of all account’s trades being passive;
• Passive volume, which is its volume‐weighted version of the former metric;
• Market-making presence as the fraction of snapshots where the account has a limit order posted at either side of the bid-ask spread.

Using these metrics, they divided HFTs into market makers and opportunistic traders (arbitrage or directional strategies). As a result, agents with more than 20% market-making presence on average across stocks are likely to have market making as their main business model.

Kirilenko, Kyle, Samadi, Tuzun, (2011) identify three different subcategories of HFTs based on their aggressiveness, noting that the aggressiveness of a given HFT firm is highly persistent across days. The subcategories are Aggressive, Mixed, and Passive. The identification is based on how frequently the HFT firm initiates a transaction. Aggressive HFT is required to initiate at least 40% of the trades it entered into. It also must do so for at least 50% of the trading days in which it was active. To be considered a Passive HFT a firm must initiate fewer than 20% of the trades it entered into and must do so for at least 50% of the trading days during which it is active. Those HFTs that meet neither the Aggressive nor the Passive definition are labeled as Mixed HFTs.

Prospective approach for HFT agents clusterization was proposed by Steve Yang et. al. (2011) based on the reward function that in some sense better explains the observed behavior of an agent. They use different classification techniques based on the learned reward functions i.e. Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Multi-Gaussian Discriminant Analysis (MDA) to distinguish HFT from other trading strategies. This approach is not applicable for identification if we rely on the formal HFT definitions, but it can be useful for clusterization based on the typology of HFT.

References

Abramovich F., Benjamini Y., Donoho D. L. and Johnstone I. M., (2006). Adapting to unknown sparsity by controlling the false discovery rate. The Annals of Statistics 34(2), 584–653.

Acharya V.V., Pedersen L.H, Philippon T. and Richardson M., (2010). Measuring systemic risk. Working Paper 1002, Federal Reserve Bank of Cleveland.

Adrian T. and Brunnermeier M., (2010). CoVaR. Staff Report 348, Federal Reserve Bank of New York.

Ait-Sahalia Y. and Jacod J., (2009a). Testing for Jumps in a Discretely Observed Process. Annals of Statistics, Vol. 37, 184–222.

Ait-Sahalia Y. and Jacod J., (2009b). Estimating the Degree of Activity of Jumps in High Frequency Data. Annals of Statistics, Vol. 37, 2202–2244.

Ait-Sahalia Y., (2004). Disentangling Diffusion from Jumps. Journal of Financial Economics, Vol. 74, 487–528.

Ait-Sahalia Y., Cacho-Diaz J., and Hurd T., (2009). Portfolio Choice with Jumps: A Closed Form Solution. Annals of Applied Probability, Vol. 19, 556–584.

Ait-Sahalia Y., Jacod J., and Li J. (2012). Testing for jumps in noisy high frequency data. Journal of Econometrics, 168, 207–222.

Andersen T. and Bondarenko O., (2011). VPIN and the Flash Crash. White Paper.

Andersen T. G., Bollerslev T., and Diebold, F. X., (2002). Parametric and Nonparametric Volatility Measurement. In Y. Ait-Sahalia and L.P. Hansen (eds.), Handbook of Financial Econometrics. Amsterdam: North-Holland.

Andersen T. G., Bollerslev T., and Diebold F. X., (2007). Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility. Review of Economics and Statistics, Vol. 89, Issue 4, 701–720.

Angel J.J., Harris L.E. and Spatt C.S., (2010). Equity Trading in the 21st Century. Working Paper, Carnegie Mellon.

Australian Securities and Investment Commission (ASIC), (2010). CP 145: Australian equity market structure: Proposals.

Australian Securities and Investments Commission (ASIC), (2011). Background paper: ASIC’s regulatory framework on competition in exchange markets.

Australian Securities and Investments Commission (ASIC), (2013). Report 331: Dark liquidity and high-frequency trading.

Avramovic A., (2010). Sizing up US equity microstructure. Credit Suisse: AES Analysis.

Bajgrowicz P. and Scaillet O., (2010). Detecting Spurious Jumps in High-Frequency Data. Swiss Finance Institute Research Paper №11–36

Bajgrowicz P. and Scaillet O., (2011). Jumps in High-Frequency Data: spurious detections, dynamics, and news, Swiss Finance Institute, Occasional Paper Series №11–36.

Bank for International Settlements (BIS), (1994). Annual Report, 1993–1994. Technical Report.

Bank for International Settlements (BIS), (1999). Market liquidity: research findings and selected policy implications. Technical Report, Monetary and Economic Department, Committee on the Global Financial System, 1999.

Barndorff-Nielsen O.E. and Shephard N., (2004a). Measuring the Impact of Jumps on Multivariate Price Processes Using Bipower Variation. Discussion paper, Nu¢ eld College, Oxford University.

Barndorff-Nielsen O.E. and Shephard N., (2004b). Power and Bipower Variation with Stochastic Volatility and Jumps. Journal of Financial Econometrics, 2, 1–37.

Barndorff-Nielsen, O.E. and Shephard, N., 2006. Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics, 4, 1–30.

Baron M., Brogaard J., and A. Kirilenko, (2012). The Trading Profits of High Frequency Traders, Working paper.

Barton and Emily, (2013).

Becketti S. and Roberts D.J., (1990). Will increased regulation of stock index futures reduce stock market volatility? Economic Review, Federal Reserve Bank of Kansas City, November Issue, 33–46.

Benos E. and Sagade S., (2012). High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market. Working paper №469, Bank of England.

Bershova, N. and D. Rakhlin, (2012), High-Frequency Trading and Long-Term Investors: A View from the Buy Side, White Paper.

Billio M., Getmansky M., Lo A. W., and Pelizzon L., (2010). Econometric measures of systemic risk in the finance and insurance sectors. NBER Working Paper 16223.

Bisias D., Flood, M., Lo A. W., and Valavanis, S., (2012). A Survey of Systemic Risk Analytics. Technical Report, Working Paper, 0001, U.S. Department of Treasury.

Bollerslev T. and Jubinski D., (1999). Equity trading volume and volatility: latent information arrivals and common long-run dependencies. Journal of Business & Economic Statistics, 17, 9–21.

Bollerslev T., Law T.H., and Tauchen G., (2008). Risk, Jumps, and Diversification. Journal of Econometrics, Vol. 144, Issue 1, 234–256.

Bormetti G., Calcagnile L. M., Treccani M., Corsi F., Marmi S., and Lillo, F., (2013). Modelling systemic cojumps with Hawkes factor models. White Paper.

Bouchaud J-P., Kockelkoren J., and Potters M., (2004). Random walks, liquidity molasses and critical response in financial markets. Science & Finance (CFM) working paper archive 500063, Science & Finance, Capital Fund Management.

Bozdog D., Florescu I., Khashanah K., Wang J., (2011). Wilmott Journal, 74–81.

Broadie M. and Jain A., (2008). The effect of jumps and discrete sampling on volatility and variance swaps. International Journal of Theoretical and Applied Finance, Vol. 11, №8, 761–797.

Brogaard J. A., (2010). High Frequency Trading and its Impact on Market Quality. Working Paper, Northwestern University.

Brogaard J., (2011a). High frequency trading and market quality. Working paper.

Brogaard J., (2011b). The activity of high frequency traders. Working paper.

Brogaard J., (2012). High frequency trading and volatility. Working paper.

Brogaard J., Hendershott T., and Riordan R., (2013), High frequency trading and price discovery. Working paper, ECB.

Brownlees C. T. and R. Engle (2011), Volatility, Correlation and Tails for Systemic Risk Measurement. Working paper, NYU Stern School of Business.

Chaboud A., Hjalmarsson E., Vega C., and Chiquoine B., (2009). Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. Federal Reserve Board International Finance Discussion Paper №980.

Chae J., Khil J., and Lee E.J., (2012). Who makes markets? Liquidity providers versus algorithmic traders. Journal of Futures Markets, 33, 397–420.

Chan-Lau J., Espinosa, M., and Sole, J., (2009). On the use of network analysis to assess systemic financial linkages. Imf working paper, International Monetary Fund.

Chlistalla M., (2011). High-frequency trading. Better than its reputation? Report, Deutche Bank Research.

Cont R. and Bouchaud J-P., (2000). Herd behavior and aggregate fluctuations in financial markets. Macroeconomic Dynamics, Vol. 4, 170–196.

Cont R. and Tankov P., (2004). Non-parametric calibration of jump–diffusion option pricing models. Journal of Computational Finance, 7 (3), 1–49.

Copeland T. and Galai D., (1982). Information effects on the bid-ask spread. Journal of Finance, 38, 1457–1469.

Cornell B. and Sirri E.R., (1992). The reaction of investors and stock prices to insider trading. Journal of Finance, Vol. 47, Issue 3, 1031–1060.

Council of the European Union (EU), (2013). Proposal for a directive of the european parliament and of the council on markets in financial instruments repealing Directive 2004/39/EC of the European Parliament and of the Council, European Commission, June 18, 2013,

Crockett A., (2000). Marrying the micro- and macro-prudential dimensions of financial stability. Speech, BIS.

Cutler D.M., Poterba J.M., and Summers L.H., (1989). What moves stock prices? Journal of Portfolio Management, 15, 4–12.

Cvitanic J., and Kirilenko A., (2010). High Frequency Traders and Asset Prices. Working Paper, California Institute of Technology.

Dacorogna M., Gençay R., Müller U., Olsen R., and Pictet O., (2001). An introduction to high-frequency finance. Academic Press, San Diego.

Danielsson J., Kevin R., Valenzuela M and Ilknur K., (2012). Dealing With Systemic Risk when We Measure It Badly. White Paper.

Danielsson J. and Zer I., (2012). Systemic risk arising from computer based trading and connection to the empirical literature on systemic risk. Technical Report Foresight Driver Review, DR 29, Foresight.

Danielsson, J., K. R. James, M. Valenzuela and I. Zer, (2011). Model Risk of Systemic Risk Models. White Paper.

Drehmann M. and Tarashev N., (2011). Measuring the systemic importance of interconnected banks. Technical Report Working Papers No 342, BIS.

Easley D., Lopez de Prado M., and M. O’Hara, (2011). The microstructure of ‘Flash Crash’: Flow Toxicity, Liquidity Crashes and the Probability of Informed Trading. The Journal of Portfolio Management, Vol. 37, №2, 118–128.

Engle R.F. and Russell J.R., (1998). Autoregressive Conditional Duration: a New Model for Irregularly Spaced Transaction Data. Econometrica, 66, 1127–1162.

Fabozzi F. J., Focardi, S. M., and Jonas, C., (2011). High-frequency trading: methodologies and market impact Reviews of Futures Markets, 19, The Institute for Financial Markets.

Fama E., (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, Vol. 25, 383–417.

Fama E.F. and MacBeth J.D., (1973). Risk, return and equilibrium: Empirical tests. Journal of Political Economy, 81, 607–636.

Fan J. and Wang Y., (2007). Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data. Journal of the American Statistical Association, 102, 1349–1362.

Farmer, J. D. and Skouras S., (2011). An Ecological Perspective on the Future of Computer Trading. Technical Report Foresight Driver Review, DR 6, Foresight.

Farmer, J.D., (2002). Market force, ecology and evolution. Industrial and Corporate Change, Vol. 11, 5, 895–953.

Filimonov V. and Sornette D., (2012). Quantifying reflexivity in financial markets: towards a prediction of flash crashes.

Foresight, Government Office for Science, (2012). The Future of Computer Trading in Financial Markets (Final Project Report). Working Paper.

Foucault T., (2012). Algorithmic Trading: Issues and Preliminary Evidence. In Abergel F., Bouchaud J.-P., Foucault T., Lehalle C.-A., and Rosenbaum M. Market Microstructure: Confronting Many Viewpoints, John Wiley & Sons, Ltd.

Gallant A.R., Rossi P.E., and Tauchen G.E., (1992). Stock prices and volume. The Review of Financial Studies, 5, 199–242.

Gatheral J., (2006). Volatility surface: A practitioner’s guide. John Wiley & Sons, Inc., New Jersey.

Gençay R., Selcuk F. and Whitcher B. (2002). An introduction to wavelets and other filtering methods in finance and economics. Elsevier, San Diego.

Gerig A., (2012). High-Frequency Trading Synchronizes Prices in Financial Markets. Working paper.

Giesecke K. and Kim B., (2011). Risk Analysis of Collateralized Debt Obligations. Operations Research, Vol. 59, №1, 32–49.

Golub A., (2011). Overview of High Frequency Trading. Working paper.

Gomber P. and Schweickert U., (2002). The market impact — liquidity measure in electronic securities trading. Deutsche Börse AG, Xetra Research, 2002.

Gordy M. B. and Howells B., (2004). Procyclicality in Basel II: Can We Treat the Disease Without Killing the Patient?. Board of Governors of the Federal Reserve System.

Gravelle T. and Li F., (2011). Measuring Systemic Importance of Financial Institutions: An Extreme Value Theory Approach. Technical Report 2011–19, Bank of Canada.

Grossman S.J. and Miller M.H., (1988). Liquidity and market structure. Journal of Finance, 17, 617–633.

Hachicha N., (2010). New Sight of Herding Behavioral through Trading Volume. Discussion Paper №2010–11.

Hagströmer B. and Nordén L. (2013). The diversity of high frequency traders. Journal of
Financial Markets, 16, 741–770.

Haldane A. G., (2011). The race to zero. Technical Report, Bank of England.

Hanousek J., (2011). The identification of price jumps. Working Paper Series 434.

Hardiman S., Bercot N., and Bouchaud J.-P., (2013). Critical reflexivity in financial markets: a Hawkes process analysis. White Paper.

Harris L. (2003). Trading and exchanges: Market microstructure for practitioners. Oxford University Press, New York.

Hartmann P., Straetmans S. and de Vries C. G., (2004). Asset Market Linkages in Crisis Periods. The Review of Economics and Statistics, 86, 313–326.

Hasbrouck J. and Saar G., (2013), Low-latency trading. Journal of Financial Markets, 16, 646–679.

Hasbrouck J., (1991a). Measuring the information content of stock trades. Journal of Finance, Vol. 46, Issue 1,179–207.

Hasbrouck J., (1991b). The summary informativeness of stock trades: An econometric analysis. Review of Financial Studies, Vol. 4, Issue 3, 571–595.

Hasbrouck J., (1995). One security, many markets: Determining the contributions to price discovery. Journal of Finance, Vol. 50, Issue 4, 1175–1199.

Hendershott T. and Riordan R., (2009). Algorithmic Trading and Information. Working Paper, Updated in 2012.

Hendershott T. and Riordan R., (2012). Algorithmic trading and the market for liquidity. Working Paper.

Hendershott T., Jones C. M. and Menkveld A. J., (2011). Does Algorithmic Trading Improve Liquidity?, Journal of Finance, 66, 1–33.

Heston S. L., (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. The Review of Financial Studies, Vol. 6, №2, 327–343.

Hirschey N. H. (2013). Do High-Frequency Traders Anticipate Buying and Seling Pressure?. Working Paper, London Business School.

Hirshleifer D. and Teoh S.H., (2003). Herd behaviour and cascading in capital markets: a review and synthesis. European Financial Management, Vol. 9, №1, 25–66.

Huang R. and Stoll H., (1997). The components of the bid-ask spread: A general approach. Review of Financial Studies, 10, 995–1034.

Huang X., Zhou H. and Zhu H., (2010). Measuring Systemic Risk Contributions of Large and Complex Financial Institutions.

Hwang S., and Salmon M. (2004). Market Stress and Herding. Journal of Empirical Finance, 11(4), 585–616.

International Organization of Securities Commissions (IOSCO), (2011). Regulatory Issues Raised by the Impact of Technological Changes on Market Integrity and Efficiency. Final Report (IOSCOPD361), Technical Committee of IOSCO.

Investment Industry Regulatory Organization of Canada (IIROC), (2012). The HOT Study Phases I and II of IIROC’s Study of High Frequency Trading Activity on Canadian Equity Marketplaces.

Jiang G. and Oomen R., (2008). Testing for Jumps When Asset Prices Are Observed with Noise: A Swap Variance Approach. Journal of Econometrics, Vol. 144, №2, 352–370.

Johnson N., Zhao G., Hunsader E., Meng J., Ravindar A., Carran S. and Tivnan B., (2010). Financial black swans driven by ultrafast machine ecology. White Paper.

Jones C., (2013). What do we know about high-frequency trading? Working paper, Columbia Business School.

Joulin A., Lefevre A., Grunberg D., and Bouchaud J.-P., (2010). Stock price jumps: news and volume play a minor role. Resource document.

Karpoff J., (1987). The relation between price change and trading volume: A survey. Journal of Financial and Quantitative Analysis, 22, 109–126.

Kaufman G. G., (2000). Banking and Currency Crises and Systemic Risk: A Taxonomy and Review. Staff Reports (discontinued) 48, Netherlands Central Bank.

Kennedy D.B., Sivakumar R. and Vetzal K.R., (2006). The implications of IPO underpricing for the firm and insiders: Tests of asymmetric information theories. Journal of Empirical Finance, Vol. 13, Issue 1, 49–78.

Kirilenko A. A., Kyle A. S., Samadi M., and Tuzun T., (2011). The flash crash: The impact of high frequency trading on an electronic market. Working paper.

Kleinert H., (2009). Path integrals in quantum mechanics, statistics, polymer physics, and financial markets, 5th Edition. World Scientific, Berlin.

Kritzman M., and Li Y., (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30–41.

Kritzman M., Li Y., Page S., and R. Rigobon, (2010). Principal Components as a Measure of Systemic Risk. Revere Street Working Paper Series: Financial Economics, 272–28.

Kyle, A. S., (1985). Continuous Auctions and Insider Trading. Econometrica, 53(6), 1315–35.

Lahaye J., Laurent S. and Neely C. J., (2009). Jumps, cojumps and macro announcements. Working Paper, Federal Reserve Bank of St. Louis, No 2007–032, Revised Version.

Lakonishok J., Shleifer A., and Vishny R. W. (1992). The impact of institutional trading on stock prices. Journal of Financial Economics, 32(1), 23–43.

LeBaron B., (2002). Short-memory traders and their impact on group learning in financial markets. Proceedings of the National Academy of Science, vol 99 suppl. 3, 7201–7206.

Lee E.J., (2013), High Frequency Trading in the Korean Index Futures Market. Working paper.

Lee S.S. and Mykland P.A., (2008). Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics. Review of Financial Studies, 21 (6), 2535–2563.

Leland H. E., (2011). Leverage, Forced Asset Sales, and Market Stability: Lessons from Past Market Crises and the Flash Crash. Technical Report Foresight Driver Review, DR 9, Foresight.

Lillo F. and Mantegna R., (2004). Dynamics of a financial market index after a crash. Physica A., 338, 125–134.

Litzenberger R., Castura J., and Gorelick R., (2012). The Impacts of Automation and High Frequency Trading on Market Quality. Working Paper.

Lo A. W. and Wang J., (2002). Trading volume: Implications of an intertemporal capital asset price model. Advances in Economic Theory: Eighth World Congress, 1–23.

Madhavan A., (2000). Market microstructure: A survey. Journal of Financial Markets, Vol. 3, Issue 3, 205–258.

May R. M., (1972). Will a Large Complex System be Stable?. Nature 238, 413–414.

Menkveld A., (2013). High frequency trading and the new market makers. Journal of Financial Markets, 16, 712–740.

Menkveld A. J., Koopman S. J., and Lucas A., (2007). Modeling around-the-clock Price Discovery for Cross-listed Stocks using State Space Models. Journal of Business & Economic Statistics, 25, 213–225.

Merton R. C., (1976). Option pricing when underlying stock returns are discontinuous. Journal of Financial Economics, Vol. 3, №1–2, 125–144.

Mu G.-H., Zhou W.-X., Chen W., and Kertesz J., (2010). Order flow dynamics around extreme price changes on an emerging stock market. Resource document.

Newey W.K. and West K.D., (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708.

Novotný J., (2010). Were Stocks during the Financial Crisis More Jumpy: A Comparative Study. White Paper.

Nyberg P., Wilhelmsson A., (2009). Measuring Event Risk. Journal of Financial Econometrics, Vol. 7, Issue 3, 265–287.

Osborne M. F. M., (1959). Brownian motion in the stock market. Operations Research, 7(2), 145–173.

Pan J., (2002). The jump-risk premia implicit in options: evidence from an integrated time-series study. Journal of Financial Economics, Vol. 63, 3–50.

Pirino D., (2009). Jump detection and long range dependence. Physica A, Vol. 388, 1150–1156.

Ponzi A., Lillo F., and Mantegna R., (2009). Market reaction to a bid-ask spread change: A power-law relaxation dynamics. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 80(1 Pt 2):016112.

Saichev A., Sornette D., and Filimonov V. (2009). Most Efficient Homogeneous Volatility Estimators, Swiss Finance Institute Research Paper №09–35.

Schwarcz S. L., (2008). Systemic Risk. Georgetown Law Journal, 97, 193–249.

Securities and Exchange Commission (SEC), (2010). Findings Regarding The Market Events of May 6, 2010. Technical Report.

Shiller R.J., (2005). Irrational Exuberance. Princeton University Press, Princeton.

Sorkenmaier A. and Wagener M., (2011). Do We Need a European “National Market System”? Competition, Arbitrage, and Suboptimal Executions. Working Paper, Karlsruhe Institute of Technology.

Sornette D. and von der Becke S., (2011). Crashes and High Frequency Trading. Technical Report Foresight Driver Review, DR 7, Foresight.

Sornette D., Johansen A., Bouchaud J.-P., (1996). Stock Market Crashes, Precursors and Replicas. J. Phys. I France, 6, 167–175.

Sun W., (2003). Relationship between Trading Volume and Security Prices and Returns. MIT LIDS Technical Report 2638.

Taleb N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House, New York.

Tarashev N., Borio C. and Tsatsaronis K., (2010). Attributing systemic risk to individual institutions. Technical Report Working Papers No 308, BIS.

Tinic S.M., (1995). Derivatives and stock market volatility: Is additional government regulation necessary? Journal of Financial Services Research, Vol. 9, issue 3–4, 351–362.

Trichet J. C., (2009). Systemic Risk. Working Paper.

Tse J., Lin X., and Vincent D., (2012a). High Frequency Trading — The Good, The Bad, and The Regulation. Market Commentary, Credit Suisse: AES Analysis.

Tse J., Lin X., and Vincent D., (2012b). What does “bad” HFT look like, how often does it happen, and how do we detect it? Market Commentary, Credit Suisse: AES Analysis.

U.S. Commodity Futures Trading Commission (CFTC), (2012a). Presentation by the CFTC Technology Advisory Committee, Sub-Committee on Automated and High Frequency Trading — Working Group 1. Electronic copy available at: http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/tac103012_wg1.pdf

U.S. Commodity Futures Trading Commission (CFTC), (2012b). Categorization and Data. Presentation by the CFTC Technology Advisory Committee, Sub-Committee on Automated and High Frequency Trading — Working Group 2. Electronic copy available at: http://www.cftc.gov/ucm/groups/public/@newsroom/documents/file/wg2presentation1_062012.pdf

U.S. Securities and Exchange Commission (SEC), (2010). Concept Release on Equity Market Structure. Concept Release, 17 CFR PART 242, Release №34–61358; File No. S7–02–10.

Vasicek O. A., (1991). The Limiting Loan Loss Probability Distribution. KMV Working Paper.

Wermers R. (1995). Herding, Trade Reversals, and Cascading by Institutional Investors. Working Paper, University of Colorado, Boulder.

Wermers, R., (1999). Herding and the Impact on Stock Prices. The Journal of Finance, Vol. 54, №2, 581–622.

Yang S. Y., Qiao Q., Beling P.A., Scherer W. T., Kirilenko A. A., and Cusimano J., (2012). Gaussian Process Based Algorithmic Trading Strategy Identification. White Paper.

Yang S., Paddrik M., Hayes R., Todd A., Kirilenko A., Beling P., Scherer W., (2011). Behavior Based Learning in Identifying High Frequency Trading Strategies,Working Paper.

Zawadowski A. G., Kertesz J., Andor G., (2004). Large price changes on small scales. Physica A., 344, 221–226.

Zhang S. S., (2012). Need for Speed: An Empirical Analysis of Hard and Soft Information in a High Frequency World. Working paper.

Zhang X. F., (2010). High-frequency trading, stock volatility, and price discovery. Working Paper.

Zhou C., (2010). Are banks too big to fail? Measuring systemic importance of financial institutions. International Journal of Central Banking, 6, 205–250.

Zigrand J.-P., Shin H. S., and Beunza D., (2011). Feedback effects and changes in the diversity of trading strategies. Technical Report Foresight Driver Review, DR 2, Foresight.

World Federation of Exchanges (WFE), (2013). Understanding High Frequency Trading (HFT). White Paper.