Enhancing a Multi-Factor Equity Strategy via Artificial Intelligence and Technical Indicators

This study proposes a novel equity investment strategy that effectively integrates artificial intelligence techniques, multi factor models and financial technical indicators. In investment-related finance research, there exists a serious problem that many actively managed funds fail to outperform simple index buy-and-hold strategies. Our objective is to address this issue by designing a strategy that not only delivers high investment performance but also maintains interpretability, an often-overlooked aspect in complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a Five Factor model, a well-known finance model for its theoretical grounding in financial economics and its high accountability, to predict future returns by using a gradient boosting machine and a state space model. In addition, an index futures short (selling) strategy for downside hedging is developed with IF-THEN rules and three technical indicators. To the best of our knowledge, this is the first study to develop an equity investment framework based on a new, more predictable Five Factor model, enhanced by artificial intelligence techniques and technical indicators. Finally, empirical analysis is conducted using the data on the Japanese stock market from 1/04/2010 to 12/29/2023 and shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities. Furthermore, statistical tests for Sharpe and Sortino ratios reveal that while the baseline strategy fails to reject the null hypothesis, particularly in the most recent period, our proposed strategy exhibits statistically significant performance across all subperiods, underscoring its robustness.