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Shapley values for LightGBM model applied to regime detection

Abstract : We consider a gradient boosting decision trees (GBDT) approach to predict large S&P 500 price drops from a set of 150 technical, fundamental and macroeconomic features. We report an improved accuracy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features provides improvements across all ML approaches. Shapley values have recently been introduced from game theory to the field of ML. They allow for a robust identification of the most important variables predicting stock market crises, and of a local explanation of the crisis probability at each date, through a consistent features attribution. We apply this methodology to analyse in detail the March 2020 financial meltdown, for which the model offered a timely out of sample prediction. This analysis unveils in particular the contrarian predictive role of the tech equity sector before and after the crash.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03320300
Contributor : Eric Benhamou Connect in order to contact the contributor
Submitted on : Sunday, August 15, 2021 - 11:41:36 AM
Last modification on : Tuesday, August 17, 2021 - 3:24:40 AM

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  • HAL Id : hal-03320300, version 1

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J Ohana, S Ohana, Eric Benhamou, D Saltiel, B Guez. Shapley values for LightGBM model applied to regime detection. 2021. ⟨hal-03320300⟩

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