Performance metrics for algorithmic traders
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.
|Date of creation:||22 Jun 2009|
|Date of revision:||04 Jan 2012|
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Obizhaeva, Anna A. & Wang, Jiang, 2013.
"Optimal trading strategy and supply/demand dynamics,"
Journal of Financial Markets,
Elsevier, vol. 16(1), pages 1-32.
- Anna Obizhaeva & Jiang Wang, 2005. "Optimal Trading Strategy and Supply/Demand Dynamics," NBER Working Papers 11444, National Bureau of Economic Research, Inc.
- Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011.
"Does Algorithmic Trading Improve Liquidity?,"
Journal of Finance,
American Finance Association, vol. 66(1), pages 1-33, 02.
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