The Anatomy of Machine Learning-Based Portfolio Performance

57 Pages Posted: 29 Nov 2023 Last revised: 7 Dec 2023

See all articles by Philippe Goulet Coulombe

Philippe Goulet Coulombe

Université du Québec à Montréal - Département des Sciences Économiques

David Rapach

Research Department, Federal Reserve Bank of Atlanta; Washington University in St. Louis

Erik Christian Montes Schütte

Aarhus University; Aarhus University - CREATES; DFI

Sander Schwenk-Nebbe

Aarhus University - Department of Economics and Business Economics

Date Written: December 5, 2023

Abstract

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. 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.

Keywords: Asset return predictability, Machine learning, Out-of-sample forecast, Portfolio construction, Economic value, Shapley value, XGBoost

JEL Classification: C53, C55, C58, G11, G17

Suggested Citation

Goulet Coulombe, Philippe and Rapach, David and Schütte, Erik Christian Montes and Schütte, Erik Christian Montes and Schwenk-Nebbe, Sander, The Anatomy of Machine Learning-Based Portfolio Performance (December 5, 2023). Available at SSRN: https://ssrn.com/abstract=4628462 or http://dx.doi.org/10.2139/ssrn.4628462

Philippe Goulet Coulombe

Université du Québec à Montréal - Département des Sciences Économiques ( email )

PB 8888 Station DownTown
Succursale Centre Ville
Montreal, Quebec H3C3P8
Canada

David Rapach (Contact Author)

Research Department, Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Erik Christian Montes Schütte

Aarhus University ( email )

Nordre Ringgade 1
DK-8000 Aarhus C, 8000
Denmark

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

HOME PAGE: http://sites.google.com/view/christian-montes-schutte/home

DFI ( email )

Sander Schwenk-Nebbe

Aarhus University - Department of Economics and Business Economics ( email )

Fuglesangs Allé 4
Aarhus V, 8210
Denmark

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