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Machine Learning for Stock Beta Estimation by liusongting07 is a document available to read on EtoBox.

What is Machine Learning for Stock Beta Estimation about?

This paper assesses the effectiveness of machine learning techniques in estimating time-varying betas for US stocks, finding that tree-based models and neural networks significantly outperform traditional estimators. Random forests yield the lowest forecast errors and enable the creation of market-neutral portfolios, while the complexity of models varies over time. The study highlights the importance of nonlinear interactions and a broad set of predictors in enhancing predictive performance compared to line

Author

liusongting07

Language

EN