Rosenthal, Dale W.R. (2012): Performance metrics for algorithmic traders.
This is the latest version of this item.
Download (433kB) | Preview
Portfolio traders may split large orders into smaller orders scheduled over time to reduce price impact. Since handling many orders is cumbersome, these smaller orders are often traded in an automated (“algorithmic”) manner. We propose metrics using these orders to help measure various trading-related skills with low noise. Managers may use these metrics to assess how separate parts of the trading process contribute execution, market timing, and order scheduling skills versus luck. These metrics could save 4 basis points in cost per trade yielding a 15% reduction in expenses and saving $7.3 billion annually for US-domiciled equity mutual funds alone. The metrics also allow recovery of parameters for a price impact model with lasting and ephemeral effects. Some metrics may help evaluate external intermediaries, test for possible front-running, and indicate sloppy or overly passive trading.
|Item Type:||MPRA Paper|
|Original Title:||Performance metrics for algorithmic traders|
|Keywords:||trading skill, short term market timing, order scheduling, luck versus skill|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading
G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates
G - Financial Economics > G2 - Financial Institutions and Services > G23 - Non-bank Financial Institutions ; Financial Instruments ; Institutional Investors
G - Financial Economics > G2 - Financial Institutions and Services > G24 - Investment Banking ; Venture Capital ; Brokerage ; Ratings and Ratings Agencies
|Depositing User:||Dale W.R. Rosenthal|
|Date Deposited:||26. Feb 2012 06:48|
|Last Modified:||14. Sep 2015 21:27|
Almgren, R. and N. Chriss. (2001)., “Optimal Execution of Portfolio Transactions.” Journal of Risk, 3, 5–39.
Berke, L. J. (2010)., “US Institutional Equity Brokerage 2010: Assets, Commission Management and Concentration.” Research report, TABB Group.
Bertsimas, D. and A. W. Lo. (1998)., “Optimal Control of Execution Costs.” Journal of Financial Markets, 1, 1–50.
Easley, D. and M. O’Hara. (2010)., “Microstructure and Ambiguity.” Journal of Finance, 65, 1817–1846.
Engle, R. F. and R. Ferstenberg. (2007)., “Execution Risk.” Journal of Portfolio Management, 33, 34–44.
Fama, E. F. and K. R. French. (2010)., “Luck versus Skill in the Cross-Section of Mutual Fund Returns.” Journal of Finance, 65, 1915–1947.
Hendershott, T., C. M. Jones, and A. J. Menkveld. (2011)., “Does Algorithmic Trading Improve Liquidity?” Journal of Finance, 66, 1–33.
Heston, S. L., R. A. Korajczyk, and R. Sadka. (2010)., “Intraday Patterns in the Cross-Section of Stock Returns.” Journal of Finance, 65, 1369–1407.
Honoré, A. (2009)., “Smart Order Router Supply and Demand: Everything to Everyone.” Research report, Aite Group.
Huberman, G. and W. Stanzl. (2004)., “Price Manipulation and Quasi-Arbitrage.” Econometrica, 74, 1247–1276.
Investment Company Institute (2010)., 2010 Investment Company Fact Book. Washington, DC.
Kissell, R. and R. Malamut (2005)., “Understanding the Profit and Loss Distribution of Trading Algorithms.” In “Algorithmic Trading: Precision, Control, Execution,” 41–49, New York: Institutional Investors Guides.
Kyle, A. S. (1985)., “Continuous Auctions and Insider Trading.” Econometrica, 53, 1315–1336.
Lehmann, B. N. (2003)., “What We Measure in Execution Cost Measurement.” Journal of Financial Markets, 6, 227–231.
Lucchetti, A. (2005)., “NYSE Probes Firms for Possible Improper Trading.” Wall Street Journal.
McPartland, K. (2010)., “Data Center Networking: Redefining the Total Area Network.” Research report, TABB Group.
Obizhaeva, A. and J. Wang (2006)., “Optimal Trading Strategy and Supply/Demand Dynamics.” Working paper, Massachusetts Institute of Technology.
Opiela, N. (2006)., “Hype and Algorithms: Is Algorithmic Trading the Way of the Future or Just ’Okay’?” CFA Magazine, 46–47.
Perold, A. F. (1988)., “The Implementation Shortfall: Paper Versus Reality.” Journal of Portfolio Management, 14, 4–9.
Puckett, A. and X. S. Yan. (2011)., “The Interim Trading Skills of Institutional Investors.” Journal of Finance, 66, 601–633.
Available Versions of this Item
Performance metrics for algorithmic traders. (deposited 20. Feb 2012 13:45)
- Performance metrics for algorithmic traders. (deposited 26. Feb 2012 06:48) [Currently Displayed]