This post presents a detailed exploration of applying machine learning techniques to multi-factor models in asset pricing. It begins with an introduction to the limitations of traditional multi-factor models and the rationale for incorporating machine learning. The methodology section outlines the use of a dataset with 1269 features, emphasizing the importance of cross-sectional normalization and data scaling.
The core of the post is dedicated to explaining the model implementation, including the training and evaluation of various algorithms like Ridge, Lasso, ElasticNet, and XGBoost. The backtesting section discusses a long-short portfolio strategy, where stocks with high scores are taken long and those with low scores are shorted, detailing the process and the metrics used to assess performance.
The results are presented in a comprehensive table, providing insights into key metrics such as Sharpe ratio, volatility, alpha, beta, and CAGR, among others. The post concludes with an analysis of these results, offering a clear view of how the model performs under different investment strategies.
Post link: https://medium.com/p/1b945c4cf2de