Machine Learning Forecasts of Asymmetric Betas Using Firm-Specific Information

TL;DR

This paper shows that machine learning can effectively model asymmetric risk in stocks, improving risk forecasts, valuation, and portfolio performance by capturing nonlinearities and firm-specific factors.

Contribution

It introduces a machine learning framework for modeling conditional asymmetric betas, enhancing risk prediction, valuation, and portfolio strategies over traditional methods.

Findings

Machine learning improves out-of-sample risk forecasts across various measures.

Firm characteristics like intangibles, momentum, and growth are key risk drivers.

Decomposing systematic risk into granular components yields better market beta estimates.

Abstract

We demonstrate that machine learning methods provide a powerful framework for modelling conditional asymmetric risk. Using a large cross-section of US stocks and a comprehensive set of firm characteristics, we show that allowing for nonlinearities significantly increases the out-of-sample performance across a wide range of asymmetric beta measures and forecasting horizons. Trading frictions, followed by characteristics related to intangibles, momentum and growth, emerge as the most important drivers of future risk dynamics. Reconstructing CAPM beta from forecasts of asymmetric beta components indicates that a more granular decomposition of systematic risk yields a more accurate representation of market beta. We also find that incorporating conditional beta forecasts into discounted cash flow models that account for the term structure of betas enhances equity valuation accuracy. Finally, we show that the statistical outperformance of conditional betas translates into economically significant benefits for market-neutral portfolio investors.