The relevance of asset return predictability is routinely assessed by the economic value that it produces in asset allocation exercises. Specifically, out-of-sample return forecasts are generated based on a set of predictors, increasingly via “black box” machine learning models.
Philippe Goulet Coulombe, David E. Rapach, Erik Christian Montes Schutte, Sander Schwenk-Nebbe
The results
The return forecasts then serve as inputs for constructing a portfolio, and portfolio performance metrics are computed over the forecast evaluation period. To shed light on the sources of the economic value generated by return predictability in fitted machine learning models, we develop a methodology based on Shapley values—the Shapley-based portfolio performance contribution (SPPC)—to directly estimate the contributions of individual or groups of predictors to portfolio performance. We illustrate the use of the SPPC in an empirical application measuring the economic value of cross-sectional stock return predictability based on a large number of firm characteristics and machine learning.
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- Asset Pricing
- Big Data
- Forecasting with Machine Learning
- Macro Finance
- Option Pricing
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