Predictive Systems: Living with Imperfect Predictors

59 Pages Posted: 7 Jul 2012

See all articles by Lubos Pastor

Lubos Pastor

University of Chicago - Booth School of Business; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER)

Robert F. Stambaugh

University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER)

Multiple version iconThere are 4 versions of this paper

Date Written: January 2007

Abstract

The standard regression approach to modeling return predictability seems too restrictive in one way but too lax in another. A predictive regression models expected returns as an exact linear function of a given set of predictors but does not exploit the likely economic property that innovations in expected returns are negatively correlated with unexpected returns. We develop an alternative framework - a predictive system - that accommodates imperfect predictors and beliefs about that negative correlation. In this framework, the predictive ability of imperfect predictors is supplemented by information in lagged returns as well as lags of the predictors. Compared to predictive regressions, predictive systems deliver different and substantially more precise estimates of expected returns as well as different assessments of a given predictor's usefulness.

Suggested Citation

Pastor, Lubos and Stambaugh, Robert F., Predictive Systems: Living with Imperfect Predictors (January 2007). NBER Working Paper No. w12814. Available at SSRN: https://ssrn.com/abstract=2101929

Lubos Pastor (Contact Author)

University of Chicago - Booth School of Business ( email )

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Centre for Economic Policy Research (CEPR)

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National Bureau of Economic Research (NBER)

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Robert F. Stambaugh

University of Pennsylvania - The Wharton School ( email )

The Wharton School, Finance Department
University of Pennsylvania
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United States
215-898-5734 (Phone)
215-898-6200 (Fax)

National Bureau of Economic Research (NBER)

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