Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Model

Liao Zhu (), Robert Jarrow () and Martin T. Wells ()
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Liao Zhu: Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA
Martin T. Wells: Charles A. Alexander Professor of Statistical Sciences, Department of Statistics and Data Science, Cornell University, Ithaca, New York 14853, USA

Quarterly Journal of Finance (QJF), 2021, vol. 11, issue 04, 1-30

Abstract: This paper tests a multi-factor asset pricing model that does not assume that the return’s beta coefficients are constants. This is done by estimating the generalized arbitrage pricing theory (GAPT) using price differences. An implication of the GAPT is that when using price differences instead of returns, the beta coefficients are constant. We employ the adaptive multi-factor (AMF) model to test the GAPT utilizing a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded exchange-traded funds. We compare the performance of the AMF model with the Fama–French 5-factor (FF5) model. For nearly all time periods less than six years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as five years) is more consistent with realized asset returns than is the FF5 model.

Keywords: Asset pricing; adaptive multi-factor model; GIBS algorithm; high-dimensional statistics; machine learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S2010139221500191

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