Algorithmic Finance (2015), 4:1-2, 53-68
17 Pages Posted: 30 Aug 2014 Last revised: 28 Jul 2015
Date Written: May 1, 2015
This paper develops a new performance measurement methodology for algorithmic trading. By adapting capability from the quality control literature, we present new criteria for assessing control, expected tail loss and risk-adjusted performance in a single framework. The multi-scale capability measure we present is more descriptive and more appropriate for algorithmic trading than the traditional measure used in finance. It is robust to non-normality and the multiple time horizon decision processes inherent in algorithmic trading. We also argue that an algorithmic trading strategy, indeed any investment strategy, which satisfies the criteria to be multi-scale capable also satisfies any definition of prudence. It will be unlikely to harm the investor or external market participants in the event of its failure, while providing a high likelihood of satisfactory risk-adjusted performance.
Keywords: risk-adjusted performance measure, term structure of capability, algorithmic trading, prudence
Suggested Citation: Suggested Citation
Cooper, Ricky Alyn and Ong, Michael and Van Vliet, Ben, Multi-Scale Capability: A Better Approach to Performance Measurement for Algorithmic Trading (May 1, 2015). Algorithmic Finance (2015), 4:1-2, 53-68. Available at SSRN: https://ssrn.com/abstract=2488667