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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)–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 measurement
Key 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.
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US and Overseas: +1 646-931-9045
UK: 0207 139 1600