The Anatomy of Machine Learning-Based Portfolio Performance

Part of FinEML online seminar series
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Christian Montes Schutte
Friday 7 Jun 2024, 16:00 - 17:00
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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.

FinEML seminar series

The FinEML seminar series is designed to create a collaborative platform for the exchange of insights and findings within the field. We aim to foster a friendly atmosphere that encourages constructive feedback, providing an opportunity for both junior and senior researchers to share their work.

Submit research paper

Submit original research papers in the following topics, but not limited to:

  • Asset Pricing
  • Big Data
  • Forecasting with Machine Learning
  • Macro Finance
  • Option Pricing

Submit your research paper at the FinEML page

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