Maximizing Predictability in the Stock and Bond Markets
Andrew W. Lo
Massachusetts Institute of Technology (MIT) - Sloan School of Management; Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); National Bureau of Economic Research (NBER)
A. Craig Mackinlay
University of Pennsylvania - Finance Department; National Bureau of Economic Research (NBER)
NBER Working Paper No. w5027
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.
Number of Pages in PDF File: 60working papers series
Date posted: July 25, 2000
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