Forecasting the Profitability of Factor-Based Investment Strategies through the Application of Machine Learning Algorithms

This study aims to analyze the results of traditional factor investing strategies and their modifications in the US market. Factor investing strategies have become increasingly popular among investors and financial market analysts due to their consistent ability to generate excess returns. This work proposes to conduct an empirical analysis of the effectiveness of traditional factor strategies and develop proprietary modifications to increase returns. The goal of the modified strategies is not only to achieve returns above the market average but also to surpass the returns of traditional factor investing strategies. The study will pay special attention to the possibility of applying machine learning algorithms to conduct deeper analysis of financial indicators and construct long-term investment portfolios based on factors. The research findings will allow for the evaluation of effectiveness and provide recommendations for the application of machine learning methods in constructing long-term investment strategies in the capital market.