Is the Covid equity bubble rational? A machine learning answer

Jean Jacques Ohana, Eric Benhamou (eric.benhamou@dauphine.eu), David Saltiel (davi.saltiel@gmail.com) and Beatrice Guez
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Eric Benhamou: MILES - Machine Intelligence and Learning Systems - LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique, LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
David Saltiel: LISIC - Laboratoire d'Informatique Signal et Image de la Côte d'Opale - ULCO - Université du Littoral Côte d'Opale

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Abstract: Is the Covid Equity bubble rational? In 2020, stock prices ballooned with S&P 500 gaining 16%, and the tech-heavy Nasdaq soaring to 43%, while fundamentals deteriorated with decreasing GDP forecasts, shrinking sales and revenues estimates and higher government deficits. To answer this fundamental question, with little bias as possible, we explore a gradient boosting decision trees (GBDT) approach that enables us to crunch numerous variables and let the data speak. We define a crisis regime to identify specific downturns in stock markets and normal rising equity markets. We test our approach and report improved accuracy of GBDT over other ML methods. Thanks to Shapley values, we are able to identify most important features, making this current work innovative and a suitable answer to the justification of current equity level.

Date: 2021-04-05
New Economics Papers: this item is included in nep-big and nep-cmp
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