Maximizing Predictability in the Stock and Bond Markets

Andrew Lo (alo-admin@mit.edu) and A. Craig MacKinlay

No 5027, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: G12 (search for similar items in EconPapers)
Date: 1995-02
Note: AP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Published as Lo, Andrew W. & Mackinlay, A. Craig, 1997. "Maximizing Predictability In The Stock And Bond Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 1(01), pages 102-134, January.

Downloads: (external link)
http://www.nber.org/papers/w5027.pdf (application/pdf)

Related works:
Journal Article: MAXIMIZING PREDICTABILITY IN THE STOCK AND BOND MARKETS (1997)
Working Paper: Maximizing predictability in the stock and bond markets (1992)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:5027

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w5027

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by (wpc@nber.org).