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

TL;DR

This paper tests the time-invariance of beta coefficients in the Adaptive Multi-Factor (AMF) model, showing it maintains stability over short periods unlike the Fama-French 5-factor model, thus better aligning with asset returns.

Contribution

It introduces a method to test beta stability in the AMF model and demonstrates its superiority over the FF5 model in capturing stable factor loadings.

Findings

AMF beta coefficients are time-invariant for periods less than 6 years.

AMF model outperforms FF5 in modeling realized asset returns.

AMF's stability supports its use with rolling windows for asset pricing.

Abstract

The purpose of this paper is to test the time-invariance of the beta coefficients estimated by the Adaptive Multi-Factor (AMF) model. The AMF model is implied by the generalized arbitrage pricing theory (GAPT), which implies constant beta coefficients. The AMF model utilizes a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded ETFs. We compare the AMF model with the Fama-French 5-factor (FF5) model. We show that for nearly all time periods with length less than 6 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 5 years) is more consistent with realized asset returns than is the FF5 model.