A New Equity Investment Strategy with Artificial Intelligence, Multi Factors, and Technical Indicators

Abstract

This study proposes a novel equity investment strategy that effectively integrates artificial intelligence (AI) techniques, multi factor models and financial technical indicators. To be practically useful as an investment fund, the strategy is designed to achieve high investment performance without losing interpretability, which is not always the case especially for complex models based on artificial intelligence. Specifically, as an equity long (buying) strategy, this paper extends a five factor model in Fama & French [1], a well-known finance model for its explainability to predict future returns by using a gradient boosting machine (GBM) 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. Combining individual equity long and index futures short models, the strategy invests in high expected return equities when the expected return of the portfolio is positive and also the market is expected to rise, otherwise it shorts (sells) index futures. To the best of our knowledge, the current study is the first attempt to develop an equity investment strategy based on a new predictable five factor model, which becomes successful with effective use of AI techniques and technical indicators. Finally, empirical analysis shows that the proposed strategy outperforms not only the baseline buy-and-hold strategy, but also typical mutual funds for the Japanese equities.