Using Statistical Process Control to Monitor Active Managers

35 Pages Posted: 19 Mar 2003

See all articles by Thomas K. Philips

Thomas K. Philips

NYU Tandon School of Engineering - Department of Finance and Risk Engineering

Emmanuel Yashchin

IBM Corporation - Thomas J. Watson Research Center

David M. Stein

Parametric Portfolio Associates

Date Written: January 17, 2003

Abstract

Investors who are invested in (or bear responsibility for) many active portfolios face a resource allocation problem: To which products should they direct their attention and scrutiny? Ideally they will focus their attention on portfolios that appear to be in trouble, but these are not easily identified using classical methods of performance evaluation.

In fact, it is often claimed that it takes forty years to determine whether an active portfolio outperforms its benchmark. The claim is fallacious. In this article, we show how a statistical process control scheme known as the CUSUM, which is closely related to Wald's [1947] Sequential Probability Ratio Test, can be used to reliably detect flat-to-the-benchmark performance in forty months, and underperformance faster still. By rapidly detecting underperformance, the CUSUM allows investors to focus their attention on potential problems before they have a serious impact on the performance of the overall portfolio.

The CUSUM procedure is provably optimal: For any given rate of false alarms, no other procedure can detect underperformance faster. It is robust to the distribution of excess returns, allowing its use in almost any asset class, including equities, fixed income, currencies and hedge funds without modification, and is currently being used to monitor over $500 billion in actively managed assets.

Keywords: CUSUM, performance measurement, alpha, excess return, active manager

JEL Classification: C22, C44, G12, G14, G23

Suggested Citation

Philips, Thomas K. and Yashchin, Emmanuel and Stein, David M., Using Statistical Process Control to Monitor Active Managers (January 17, 2003). Available at SSRN: https://ssrn.com/abstract=371121 or http://dx.doi.org/10.2139/ssrn.371121

Thomas K. Philips (Contact Author)

NYU Tandon School of Engineering - Department of Finance and Risk Engineering ( email )

Brooklyn, NY 11201
United States

Emmanuel Yashchin

IBM Corporation - Thomas J. Watson Research Center ( email )

P.O. Box 218
Yorktown Heights, NY 10598
United States
914-945-1828 (Phone)
914-945-3434 (Fax)

David M. Stein

Parametric Portfolio Associates ( email )

7310 Columbia Center
701 5th Avenue
Seattle, WA 98104
United States
206-386-5594 (Phone)

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