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Investing Through Economic Cycles with Ensemble Machine Learning Algorithms

Author

Listed:
  • Thomas Raffinot

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres)

  • Sylvain Benoît

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres)

Abstract

Ensemble machine learning algorithms (random forest and boosting) are applied to quickly and accurately detect economic turning points in the United States and in the Eurozone over the past three decades. The two key features of those algorithms are their abilities (i) to entertain a large number of predictors and (ii) to perform both variable selection and estimation simultaneously. The real-time ability to nowcast economic turning points is gauged by using investment strategies based on economic regimes induced by our models. When comparing predictive accuracy and profit measures, the model confidence set procedure is applied to avoid data snooping. We show that such investment strategies achieve impressive risk-adjusted returns: timing the market is thus possible.

Suggested Citation

  • Thomas Raffinot & Sylvain Benoît, 2019. "Investing Through Economic Cycles with Ensemble Machine Learning Algorithms," Working Papers hal-02292317, HAL.
    • Handle: RePEc:hal:wpaper:hal-02292317
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    Cited by:

    1. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    2. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.

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