Explainable AI Models Applied to the Multi-Agent Environment of FInancial Markets (Slides EXTRAAMAS 2021 Seminar)
EXTRAAMAS 2021, 4 May 2021, online
17 Pages Posted: 6 May 2021 Last revised: 15 Nov 2021
Date Written: May 4, 2021
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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