Up: 2:1

The strategy approval decision: A Sharpe ratio indifference curve approach

David H. Bailey; Marcos López de Prado; Eva del Pozo

Algorithmic Finance (2013), 2:1, 99-109
DOI: 10.3233/AF-13018

Published: Abstract, PDF.
Archived: SSRN.


The problem of capital allocation to a set of strategies could be partially avoided or at least greatly simplified with an appropriate strategy approval decision process. This paper proposes such a procedure. We begin by splitting the capital allocation problem into two sequential stages: strategy approval and portfolio optimization. Then we argue that the goal of the second stage is to beat a naïve benchmark, and the goal of the first stage is to identify which strategies improve the performance of such a naïve benchmark. We believe that this is a sensible approach, as it does not leave all the work to the optimizer, thus adding robustness to the final outcome.

We introduce the concept of the Sharpe ratio indifference curve, which represents the space of pairs (candidate strategy's Sharpe ratio, candidate strategy's correlation to the approved set) for which the Sharpe ratio of the expanded approved set remains constant. We show that selecting strategies (or portfolio managers) solely based on past Sharpe ratio will lead to suboptimal outcomes, particularly when we ignore the impact that these decisions will have on the average correlation of the portfolio. Our strategy approval theorem proves that, under certain circumstances, it is entirely possible for firms to improve their overall Sharpe ratio by hiring portfolio managers with negative expected performance. Finally, we show that these results have important practical business implications with respect to the way investment firms hire, layoff and structure payouts.

JEL classifications: C02, G11, G14, D53.

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University of Bridgeport

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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

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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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