Persistence in Factor-Based Supervised Learning Models

Journal of Finance and Data Science

30 Pages Posted: 29 Jun 2020 Last revised: 2 Nov 2021

Multiple version iconThere are 2 versions of this paper

Date Written: November 1, 2021

Abstract

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they are trained on long samples, with long-term returns as dependent variables. In addition, we report that persistent features play a prominent role in these models. When applied to portfolio choice, we find that investors are always better off predicting annual returns, even when rebalancing at lower frequencies (monthly or quarterly). Our results remain robust to transaction costs and risk scaling, thus providing useful indications to quantitative asset managers.

Keywords: Factor investing, Machine learning, Asset Pricing, Autocorrelation

JEL Classification: C45, C53, G11, G12

Suggested Citation

Coqueret, Guillaume, Persistence in Factor-Based Supervised Learning Models (November 1, 2021). Journal of Finance and Data Science, Available at SSRN: https://ssrn.com/abstract=3588261 or http://dx.doi.org/10.2139/ssrn.3588261

Guillaume Coqueret (Contact Author)

EMLYON Business School ( email )

23 Avenue Guy de Collongue
Ecully, 69132
France

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