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Algorithmic Trading and the Market for Liquidity

Published online by Cambridge University Press:  19 September 2013

Terrence Hendershott
Affiliation:
[email protected], Haas School of Business, University of California at Berkeley, 545 Student Services Bldg #1900, Berkeley, CA 94720
Ryan Riordan
Affiliation:
[email protected], Faculty of Business and Information Technology, University of Ontario Institute of Technology, 2000 Simcoe St N, Oshawa, ONT L1H 7K4, Canada

Abstract

We examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.

Type
Research Articles
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2013 

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References

Almgren, R., and Chriss, N.. “Optimal Execution of Portfolio Transactions.” Journal of Risk, 3 (2000), 540.Google Scholar
Barclay, M.; Hendershott, T.; and McCormick, D.. “Competition among Trading Venues: Information and Trading on Electronic Communications Networks.” Journal of Finance, 58 (2003), 26372666.Google Scholar
Bertsimas, D., and Lo, A.. “Optimal Control of Execution Costs.” Journal of Financial Markets, 1 (1998), 150.Google Scholar
Bessembinder, H. “Issues in Assessing Trade Execution Costs.” Journal of Financial Markets, 6 (2003), 233257.CrossRefGoogle Scholar
Biais, B.; Foucault, T.; and Moinas, S.. “Equilibrium Algorithmic Trading.” Working Paper, Toulouse University, IDEI (2011).Google Scholar
Biais, B.; Hillion, P.; and Spatt, C.. “An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse.” Journal of Finance, 50 (1995), 16551690.Google Scholar
Biais, B.; Hombert, J.; and Weill, P.-O.. “Trading and Liquidity with Limited Cognition.” Working Paper, Toulouse University, IDEI (2010).CrossRefGoogle Scholar
Biais, B., and Woolley, P.. “High Frequency Trading.” Working Paper, Toulouse University, IDEI (2011).Google Scholar
Brogaard, J.; Hendershott, T.; and Riordan, R.. “High Frequency Trading and Price Discovery.” Working Paper, University of California at Berkeley (2013).Google Scholar
Chaboud, A.; Chiquoine, B.; Hjalmarsson, E.; and Vega, C.. “Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market.” FRB International Finance Discussion Paper No. 980 (2009).Google Scholar
Cohen, K.; Maier, S.; Schwartz, R.; and Whitcomb, D.. “Transaction Costs, Order Placement Strategy and Existence of the Bid-Ask Spread.” Journal of Political Economy, 89 (1981), 287305.Google Scholar
Domowitz, I., and Yegerman, H.. “The Cost of Algorithmic Trading: A First Look at Comparative Performance.” Journal of Trading, 1 (2006), 3342.Google Scholar
Duffie, D. “Asset Price Dynamics with Slow-Moving Capital.” Journal of Finance, 65 (2010), 12381268.CrossRefGoogle Scholar
Engle, R.; Russell, J.; and Ferstenberg, R.. “Measuring and Modeling Execution Cost and Risk.” Journal of Portfolio Management, 38 (2012), 1428.Google Scholar
Foucault, T.; Kadan, O.; and Kandel, E.. “Liquidity Cycles and Make/ Take Fees in Electronic Markets.” Journal of Finance, 68 (2013), 299341.Google Scholar
Foucault, T., and Menkveld, A.. “Competition for Order Flow and Smart Order Routing Systems.” Journal of Finance, 63 (2008), 119158.Google Scholar
Foucault, T.; Roëll, A.; and Sandas, P.. “Market Making with Costly Monitoring: An Analysis of the SOES Controversy.” Review of Financial Studies, 16 (2003), 345384.CrossRefGoogle Scholar
Friedman, M. “The Case for Flexible Exchange Rates.” In Essays in Positive Economics, Friedman, M., ed. Chicago: University of Chicago Press (1953).Google Scholar
Goettler, R.; Parlour, C.; and Rajan, U.. “Informed Traders and Limit Order Markets.” Journal of Financial Economics, 93 (2009), 6787.Google Scholar
Griffiths, M.; Smith, B.; Turnbull, D.; and White, R.. “The Costs and Determinants of Order Aggressiveness.” Journal of Financial Economics, 56 (2000), 6588.Google Scholar
Harris, L. “Optimal Dynamic Order Submission Strategies in Some Stylized Trading Problems.”Financial Markets, Institutions, and Instruments, 7 (1998), 176.Google Scholar
Hasbrouck, J., and Saar, G.. “Technology and Liquidity Provision: The Blurring of Traditional Definitions.” Journal of Financial Markets, 12 (2009), 143172.CrossRefGoogle Scholar
Hasbrouck, J., and Saar, G.. “Low-Latency Trading.” Journal of Financial Markets, 16 (2013), 646679.CrossRefGoogle Scholar
Hau, H. “Location Matters: An Examination of Trading Profits.” Journal of Finance, 56 (2001), 19591983.Google Scholar
Hendershott, T.; Jones, C. M.; and Menkveld, A. J.. “Does Algorithmic Trading Improve Liquidity?Journal of Finance, 66 (2011), 133.CrossRefGoogle Scholar
Jain, P. “Financial Market Design and the Equity Premium: Electronic versus Floor Trading.” Journal of Finance, 60 (2005), 29552985.Google Scholar
Jovanovic, B., and Menkveld, A.. “Middlemen in Limit-Order Markets.” Working Paper, VU University Amsterdam (2011).Google Scholar
Kawaller, I.; Koch, P.; and Koch, T.. “The Temporal Price Relationship between S&P 500 Futures and the S&P 500 Index.” Journal of Finance, 42 (1987), 13091329.Google Scholar
Keim, D., and Madhavan, A.. “Anatomy of the Trading Process: Empirical Evidence on the Behavior of Institutional Traders.” Journal of Financial Economics, 37 (1995), 371398.Google Scholar
Kirilenko, A.; Kyle, A. S.; Samadi, M.; and Tuzun, T.. “The Flash Crash: The Impact of High Frequency Trading on an Electronic Market.” Working Paper, Massachusetts Institute of Technology (2011).Google Scholar
Lee, C., and Ready, M.. “Inferring Trade Direction from Intraday Data.” Journal of Finance, 46 (1991), 733746.Google Scholar
Lo, A.; MacKinlay, A.; and Zhang, J.. “Econometric Models of Limit-Order Executions.” Journal of Financial Economics, 65 (2002), 3171.Google Scholar
Menkveld, A. “High Frequency Trading and the New Market Makers.” Journal of Financial Markets, 16 (2013), 712740.Google Scholar
Pagnotta, E., and Philippon, T.. “Competing on Speed.” Working Paper, New York University (2011).CrossRefGoogle Scholar
Parlour, C. “Price Dynamics in Limit Order Markets.” Review of Financial Studies, 11 (1998), 789816.CrossRefGoogle Scholar
Parlour, C., and Seppi, D.. “Limit Order Markets: A Survey.” Handbook of Financial Intermediation and Banking, Boot, A. W. A. and Thakor, A. V., eds. Amsterdam: Elsevier Science (2008).Google Scholar
Petersen, M. “Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches.”Review of Financial Studies, 22 (2009), 435480.CrossRefGoogle Scholar
Ranaldo, A. “Order Aggressiveness in Limit Order Book Markets.” Journal of Financial Markets, 7 (2004), 5374.Google Scholar
Rosu, I. “A Dynamic Model of the Limit Order Book.” Review of Financial Studies, 22 (2009), 46014641.Google Scholar
SEC. Regulations NMS, Release No. 34-51808 (2005).Google Scholar
Thompson, S. “Simple Formulas for Standard Errors That Cluster by Both Firm and Time.” Journal of Financial Economics, 99 (2011), 110.Google Scholar
Venkataraman, K. “Automated versus Floor Trading: An Analysis of Execution Costs on the Paris and New York Exchanges.” Journal of Finance, 56 (2001), 14451485.CrossRefGoogle Scholar