Time-Series Efficient Factors

71 Pages Posted: 1 Apr 2020

See all articles by Sina Ehsani

Sina Ehsani

Northern Illinois University

Juhani T. Linnainmaa

Dartmouth College - Tuck School of Business; National Bureau of Economic Research (NBER)

Date Written: March 16, 2020

Abstract

Factors in prominent asset pricing models are positively serially correlated. We derive the optimal allocation that transforms an auto-correlated factor to a "time-series efficient" factor. The key determinant of the value of factor timing is the ratio of a factor's auto-correlation to its Sharpe ratio. Time-series efficient factors earn significantly higher Sharpe ratios than the original factors and contain all the information found in the original factors. Momentum strategies profit by timing auto-correlated factors; they pick up factor "inefficiencies." We show that, rather than augmenting models with the momentum factor, each factor can instead be made time-series efficient. An asset pricing model with time-series efficient factors, such as an efficient Fama-French five-factor model, prices momentum. Time-series efficient factors also explain more of the co-variance structure of returns; they describe the cross section better than the standard factors and align more closely with the true SDF.

Keywords: Factors, Asset Pricing Models, Anomalies, Momentum

JEL Classification: G11, G12, G40

Suggested Citation

Ehsani, Sina and Linnainmaa, Juhani T., Time-Series Efficient Factors (March 16, 2020). Tuck School of Business Working Paper No. 3555473, Available at SSRN: https://ssrn.com/abstract=3555473 or http://dx.doi.org/10.2139/ssrn.3555473

Sina Ehsani (Contact Author)

Northern Illinois University ( email )

Chicago, IL 60115
United States

Juhani T. Linnainmaa

Dartmouth College - Tuck School of Business ( email )

Hanover, NH 03755
United States

HOME PAGE: http://www.tuck.dartmouth.edu/faculty/faculty-directory/juhani-linnainmaa

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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