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Performance metrics for algorithmic traders

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Author Info

  • Rosenthal, Dale W.R.

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-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.

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File URL: http://mpra.ub.uni-muenchen.de/36787/
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File URL: http://mpra.ub.uni-muenchen.de/36938/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 36787.

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Date of creation: 22 Jun 2009
Date of revision: 04 Jan 2012
Handle: RePEc:pra:mprapa:36787

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Related research

Keywords: trading skill; short term market timing; order scheduling; luck versus skill;

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  1. Hendershott, Terrence & Jones, Charles M. & Menkveld, Albert J., 2008. "Does algorithmic trading improve liquidity?," CFS Working Paper Series 2008/41, Center for Financial Studies (CFS).
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Cited by:
  1. Robert Azencott & Arjun Beri & Yutheeka Gadhyan & Nicolas Joseph & Charles-Albert Lehalle & Matthew Rowley, 2013. "Realtime market microstructure analysis: online Transaction Cost Analysis," Papers 1302.6363, arXiv.org, revised Mar 2013.

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