Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash

Rama K. Malladi ()
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Rama K. Malladi: California State University Dominguez Hills

Computational Economics, 2024, vol. 63, issue 3, No 4, 1045 pages

Abstract: Abstract Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20% drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S&P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes.

Keywords: Machine learning; Forecasting; Financial econometrics; Recession; Stock market crash (search for similar items in EconPapers)
JEL-codes: C01 C5 C58 C63 G11 G17 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-022-10333-8

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