The Memory of Beta

65 Pages Posted: 1 Jan 2020 Last revised: 14 Jan 2021

See all articles by Janis Becker

Janis Becker

Leibniz Universität Hannover

Fabian Hollstein

Saarland University

Marcel Prokopczuk

Leibniz Universität Hannover - Faculty of Economics and Management; University of Reading - ICMA Centre

Philipp Sibbertsen

University of Hannover

Date Written: November 25, 2019

Abstract

Researchers and practitioners employ a variety of time-series processes to forecast betas, either using short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: betas show consistent long-memory properties. For the vast majority of stocks, we reject both the short-memory and difference-stationary (random walk) alternatives. A pure long-memory model reliably provides superior beta forecasts compared to all alternatives. Accounting for long memory in beta also pays off economically for portfolio formation. We widely document the robustness of these results.

Keywords: Long memory, beta, persistence, forecasting, predictability

JEL Classification: G12, C58, G11

Suggested Citation

Becker, Janis and Hollstein, Fabian and Prokopczuk, Marcel and Sibbertsen, Philipp, The Memory of Beta (November 25, 2019). Journal of Banking and Finance (2021), Vol. 124, 106026 , Available at SSRN: https://ssrn.com/abstract=3492931 or http://dx.doi.org/10.2139/ssrn.3492931

Janis Becker

Leibniz Universität Hannover ( email )

Schneiderberg 50
Hannover, 30167
Germany

Fabian Hollstein (Contact Author)

Saarland University ( email )

Campus
Saarbrucken, Saarland D-66123
Germany

Marcel Prokopczuk

Leibniz Universität Hannover - Faculty of Economics and Management ( email )

Koenigsworther Platz 1
Hannover, 30167
Germany

University of Reading - ICMA Centre ( email )

Whiteknights Park
P.O. Box 242
Reading RG6 6BA
United Kingdom

Philipp Sibbertsen

University of Hannover ( email )

Welfengarten 1
D-30167 Hannover, 30167
Germany

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