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Performance Metrics for Algorithmic Traders

30 Pages Posted: 28 Jul 2009 Last revised: 5 Jan 2012

Dale W. R. Rosenthal

University of Illinois at Chicago - Department of Finance

Date Written: June 10, 2009

Abstract

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-relatd 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.

Keywords: trading performance, luck versus skill, order scheduling, price impact

JEL Classification: G12, G14, G23, G24

Suggested Citation

Rosenthal, Dale W. R., Performance Metrics for Algorithmic Traders (June 10, 2009). UIC College of Business Administration Research Paper No. 09-14. Available at SSRN: https://ssrn.com/abstract=1439902 or http://dx.doi.org/10.2139/ssrn.1439902

Dale Rosenthal (Contact Author)

University of Illinois at Chicago - Department of Finance ( email )

2431 University Hall (UH)
601 S. Morgan Street
Chicago, IL 60607-7124
United States

HOME PAGE: http://tigger.uic.edu/~daler

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