Maximizing Predictability in the Stock and Bond Markets

60 Pages Posted: 25 Jul 2000 Last revised: 30 Sep 2010

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

A. Craig Mackinlay

University of Pennsylvania - Finance Department; National Bureau of Economic Research (NBER)

Date Written: February 1995

Abstract

We construct portfolios of stocks and of bonds that are maximally predictable with respect to a set of ex ante observable economic variables, and show that these levels of predictability are statistically significant, even after controlling for data-snooping biases. We disaggregate the sources for predictability by using several asset groups, including industry-sorted portfolios, and find that the sources of maximal predictability shift considerably across asset classes and sectors as the return-horizon changes. Using three out-of-sample measures of predictability, we show that the predictability of the maximally predictable portfolio is genuine and economically significant.

Suggested Citation

Lo, Andrew W. and MacKinlay, Archie Craig, Maximizing Predictability in the Stock and Bond Markets (February 1995). NBER Working Paper No. w5027. Available at SSRN: https://ssrn.com/abstract=225806

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

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National Bureau of Economic Research (NBER) ( email )

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Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

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Archie Craig MacKinlay

University of Pennsylvania - Finance Department ( email )

The Wharton School
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United States
215-898-5309 (Phone)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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