@article {Wang9, author = {Haifeng Wang and Harshdeep Singh Ahluwalia and Roger A. Aliaga-D{\'\i}az and Joseph H. Davis}, title = {The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning{\textendash}Time Series Approach}, volume = {3}, number = {2}, pages = {9--20}, year = {2021}, doi = {10.3905/jfds.2021.3.2.009}, publisher = {Institutional Investor Journals Umbrella}, abstract = {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){\textendash}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.{\textquoteright}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.}, issn = {2640-3943}, URL = {https://jfds.pm-research.com/content/3/2/9}, eprint = {https://jfds.pm-research.com/content/3/2/9.full.pdf}, journal = {The Journal of Financial Data Science} }