Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-x64cq Total loading time: 0.445 Render date: 2022-05-29T09:25:19.364Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

Algorithmic Trading and the Market for Liquidity

Published online by Cambridge University Press:  19 September 2013

Terrence Hendershott
Affiliation:
hender@haas.berkeley.edu, Haas School of Business, University of California at Berkeley, 545 Student Services Bldg #1900, Berkeley, CA 94720
Ryan Riordan
Affiliation:
ryan.riordan@uoit.ca, 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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Almgren, R., and Chriss, N.. “Optimal Execution of Portfolio Transactions.” Journal of Risk, 3 (2000), 540.CrossRefGoogle 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.CrossRefGoogle Scholar
Bertsimas, D., and Lo, A.. “Optimal Control of Execution Costs.” Journal of Financial Markets, 1 (1998), 150.CrossRefGoogle 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).
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.CrossRefGoogle Scholar
Biais, B.; Hombert, J.; and Weill, P.-O.. “Trading and Liquidity with Limited Cognition.” Working Paper, Toulouse University, IDEI (2010).
Biais, B., and Woolley, P.. “High Frequency Trading.” Working Paper, Toulouse University, IDEI (2011).
Brogaard, J.; Hendershott, T.; and Riordan, R.. “High Frequency Trading and Price Discovery.” Working Paper, University of California at Berkeley (2013).
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).
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.CrossRefGoogle Scholar
Domowitz, I., and Yegerman, H.. “The Cost of Algorithmic Trading: A First Look at Comparative Performance.” Journal of Trading, 1 (2006), 3342.CrossRefGoogle 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.CrossRefGoogle Scholar
Foucault, T., and Menkveld, A.. “Competition for Order Flow and Smart Order Routing Systems.” Journal of Finance, 63 (2008), 119158.CrossRefGoogle 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.CrossRefGoogle Scholar
Griffiths, M.; Smith, B.; Turnbull, D.; and White, R.. “The Costs and Determinants of Order Aggressiveness.” Journal of Financial Economics, 56 (2000), 6588.CrossRefGoogle Scholar
Harris, L. “Optimal Dynamic Order Submission Strategies in Some Stylized Trading Problems.”Financial Markets, Institutions, and Instruments, 7 (1998), 176.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
Jovanovic, B., and Menkveld, A.. “Middlemen in Limit-Order Markets.” Working Paper, VU University Amsterdam (2011).
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.CrossRefGoogle 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.CrossRefGoogle 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).
Lee, C., and Ready, M.. “Inferring Trade Direction from Intraday Data.” Journal of Finance, 46 (1991), 733746.CrossRefGoogle Scholar
Lo, A.; MacKinlay, A.; and Zhang, J.. “Econometric Models of Limit-Order Executions.” Journal of Financial Economics, 65 (2002), 3171.CrossRefGoogle Scholar
Menkveld, A. “High Frequency Trading and the New Market Makers.” Journal of Financial Markets, 16 (2013), 712740.CrossRefGoogle Scholar
Pagnotta, E., and Philippon, T.. “Competing on Speed.” Working Paper, New York University (2011).
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.CrossRefGoogle Scholar
Rosu, I. “A Dynamic Model of the Limit Order Book.” Review of Financial Studies, 22 (2009), 46014641.CrossRefGoogle Scholar
SEC. Regulations NMS, Release No. 34-51808 (2005).
Thompson, S. “Simple Formulas for Standard Errors That Cluster by Both Firm and Time.” Journal of Financial Economics, 99 (2011), 110.CrossRefGoogle 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
206
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Algorithmic Trading and the Market for Liquidity
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Algorithmic Trading and the Market for Liquidity
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Algorithmic Trading and the Market for Liquidity
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *