Analysis of Fundamental Multi-factor Stock Selection Strategy

Abstract

This paper focuses on the fundamental multi-factor stock selection strategy, systematically sorts out its core factor system (covering traditional financial factors and emerging non-financial factors), the evolution process of the model from linear stage to machine learning application, and deeply analyzes the challenges such as factor effectiveness fluctuations and implementation obstacles faced in practice, and discusses the future development directions in the application of alternative data, improvement of model interpretability and construction of dynamic adjustment mechanisms. The article deeply traces the model evolution path of this strategy, which has gradually transitioned from the early simple weighting models relying on linear regression to the complex model stage incorporating machine learning algorithms such as random forests and gradient boosting decision trees. It clearly demonstrates the role of technological advancement in driving the improvement of strategy accuracy. The research aims to provide theoretical and practical reference for investors to optimize their investment portfolios and improve returns. It emphasizes that building an effective strategy requires balancing predictive capabilities and robustness, and it is necessary to promote the development of strategies by strengthening non-financial factor research, developing model interpretation tools, and exploring cross-market adaptation mechanisms.