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Exuberance in Financial Markets: Evidence from Machine Learning Algorithms

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  • Jan Viebig

Abstract

Motivated by Campbell and Shiller (1998), we show that the probability that abnormally low returns over long-term investment horizons occur in the future is disproportionately high when equity markets trade at extremely high valuation levels. Support vector machines are able to learn “clustering patterns” from fundamental data with high precision rates. Decision boundaries calculated with machine learning algorithms can help investors to detect irrational exuberance in financial markets followed by abnormally low returns.

Suggested Citation

  • Jan Viebig, 2020. "Exuberance in Financial Markets: Evidence from Machine Learning Algorithms," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(2), pages 128-135, April.
    • Handle: RePEc:taf:hbhfxx:v:21:y:2020:i:2:p:128-135
    DOI: 10.1080/15427560.2019.1663849
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