Performance metrics for algorithmic traders
AbstractPortfolio 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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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 36787.
Date of creation: 22 Jun 2009
Date of revision: 04 Jan 2012
trading skill; short term market timing; order scheduling; luck versus skill;
Find related papers by JEL classification:
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
- G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
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