Machine Learning Forecasts of Asymmetric Betas Using Firm-Specific Information

2026-04-24

AI Summary

This study addresses the challenge of accurately predicting conditional asymmetric betas of stocks under market downturns and upturns to enhance asset pricing and investment decisions. It pioneers the application of machine learning methods to high-dimensional firm characteristics to model the nonlinear dynamics of asymmetric betas, integrating these estimates into both the Capital Asset Pricing Model (CAPM) and discounted cash flow valuation frameworks. The research uncovers the pivotal roles of trading frictions, intangible assets, momentum, and growth factors in shaping asymmetric risk exposures. By doing so, it substantially improves out-of-sample predictive accuracy and equity valuation precision, yielding significant economic gains for market-neutral investment strategies.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• asymmetric beta
• conditional risk
• systematic risk
• equity valuation
• machine learning

Innovation

• machine learning
• asymmetric beta
• conditional risk
• equity valuation
• systematic risk decomposition