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2021 | OriginalPaper | Buchkapitel

Explainable AI (XAI) Models Applied to the Multi-agent Environment of Financial Markets

verfasst von : Jean Jacques Ohana, Steve Ohana, Eric Benhamou, David Saltiel, Beatrice Guez

Erschienen in: Explainable and Transparent AI and Multi-Agent Systems

Verlag: Springer International Publishing

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Abstract

Financial markets are a real life multi-agent system that is well known to be hard to explain and interpret. 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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Metadaten
Titel
Explainable AI (XAI) Models Applied to the Multi-agent Environment of Financial Markets
verfasst von
Jean Jacques Ohana
Steve Ohana
Eric Benhamou
David Saltiel
Beatrice Guez
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-82017-6_12

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