Up: 4:1-2

Multi-scale capability: A better approach to performance measurement for algorithmic trading

Ricky Cooper; Michael Ong; Ben Van Vliet

Algorithmic Finance (2015), 4:1-2, 53-68
DOI: 10.3233/AF-150043

Published: Abstract, PDF.
Archived: SSRN.


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.

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Philip Maymin

University of Bridgeport

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Jayaram Muthuswamy

Kent State University

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Kenneth J. Arrow

Stanford University

Herman Chernoff

Harvard University

David S. Johnson

AT&T Labs Research

Leonid Levin

Boston University

Myron Scholes

Stanford University

Michael Sipser

Massachusetts Institute of Technology

Richard Thaler

University of Chicago

Stephen Wolfram

Wolfram Research

Editorial Board

Associate Editors

Peter Bossaerts

California Institute of Technology

Emanuel Derman

Columbia University

Ming-Yang Kao

Northwestern University

Pete Kyle

University of Maryland

David Leinweber

Lawrence Berkeley National Laboratory

Richard J. Lipton

Georgia Tech

Avi Silberschatz

Yale University

Robert Webb

University of Virginia

Affiliate Editors

Giovanni Barone-Adesi

University of Lugano

Bruce Lehmann

University of California, San Diego

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