TY - JOUR T1 - The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach JF - The Journal of Financial Data Science SP - 9 LP - 20 DO - 10.3905/jfds.2021.3.2.009 VL - 3 IS - 2 AU - Haifeng Wang AU - Harshdeep Singh Ahluwalia AU - Roger A. Aliaga-Díaz AU - Joseph H. Davis Y1 - 2021/04/30 UR - https://pm-research.com/content/3/2/9.abstract N2 - Predicting long-term equity market returns is of great importance for investors to strategically allocate their assets. The authors explore machine learning (ML) methods to forecast 10-year-ahead US stock returns and compare the results with the traditional Shiller regression-based forecasts more commonly used in the asset-management industry. The authors find that ML techniques can only modestly improve the forecast accuracy of a traditional Shiller cyclically adjusted price-to-earnings ratio model, and they actually result in worse performance than the vector autoregressive model (VAR)–based two-step approach. The authors then implement this approach with ML techniques and allow for unspecified nonlinear relationships (a hybrid ML-VAR approach). They find about 50% improvement in real-time forecast accuracy for 10-year annualized US stock returns.TOPICS: Security analysis and valuation, big data/machine learning, quantitative methods, statistical methods, performance measurementKey Findings▪ Applying machine learning (ML) techniques within a robust economic framework such as Davis et al.’s (2018) two-step approach is superior than applying such techniques in isolation (directly forecasting equity returns).▪ Using the two-step approach, integrating ML with the vector autoregressive model (ML-VAR) to dynamically forecast earning yields reduces dramatically out-of-sample forecast errors, yielding an improvement of about 50% in forecast accuracy for long-horizon U.S. stock market returns.▪ Among the ML algorithms tested, the ensemble method, which averages all other model forecasts, consistently provides improved predictive power. ER -