Beta Forecasting with Realized Beta Estimators and Machine Learning Algorithms

Posted: 14 Jan 2021

See all articles by Bao Huy Doan

Bao Huy Doan

University of New South Wales

Dulani Jayasuriya

University of Auckland, Business School

John B. Lee

University of Auckland

Jonathan J. Reeves

UNSW Business School, University of New South Wales; Financial Research Network (FIRN)

Date Written: October 1, 2020

Abstract

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting, gradient boosting, random forests and neural networks. We also evaluate forecasts from these algorithms against the forecasts generated from simple constant realized beta estimators. We find that the machine learning algorithms can generate lower beta forecast error, relative to current benchmarks in beta forecasting.

Presented at the 40th International Symposium on Forecasting, October 26-28, 2020.

Keywords: CAPM, Systematic Risk

JEL Classification: C52, C58, G17

Suggested Citation

Doan, Bao Huy and Jayasuriya, Dulani and Lee, John B. and Reeves, Jonathan J., Beta Forecasting with Realized Beta Estimators and Machine Learning Algorithms (October 1, 2020). Available at SSRN: https://ssrn.com/abstract=3727306

Bao Huy Doan

University of New South Wales ( email )

Sydney, NSW 2052
Australia

Dulani Jayasuriya

University of Auckland, Business School ( email )

Auckland, 1010
New Zealand

John B. Lee

University of Auckland ( email )

Private Bag 92019
Auckland, 1001
New Zealand
649 373 7599 ext. 85171 (Phone)
649 373 7406 (Fax)

Jonathan J. Reeves (Contact Author)

UNSW Business School, University of New South Wales ( email )

Sydney, NSW 2052
Australia

Financial Research Network (FIRN) ( email )

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

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