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Is the Covid equity bubble rational? A machine learning answer

Author

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  • Jean Jacques Ohana
  • 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)

  • Beatrice Guez

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.

Suggested Citation

  • Jean Jacques Ohana & Eric Benhamou & David Saltiel & Beatrice Guez, 2021. "Is the Covid equity bubble rational? A machine learning answer," Working Papers hal-03189799, HAL.
  • Handle: RePEc:hal:wpaper:hal-03189799
    Note: View the original document on HAL open archive server: https://hal.science/hal-03189799
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    3. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    4. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
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