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Detecting Bubbles by Machine Learning Prediction

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  • MINAMI, Koutaroh

Abstract

This study explores the potential of machine learning, Long Short-Term Memory (LSTM), to detect asset price bubbles by analyzing prediction errors. Using monthly data of the Nikkei225 Index, I evaluate the performance of LSTM model in forecasting prices and compare with the GSADF test. I find that LSTM’s prediction accuracy significantly deteriorates during periods associated with asset bubbles, suggesting the presence of structural changes. In particular, the LSTM approach of this paper captures both the emergence and collapse of Japan’s late 1980s bubble separately. In addition, it can also capture structural changes related to policy changes in the 2010s Japan, which are not identified by the GSADF test. These findings suggest that machine learning can be used for not only identifying bubbles but also policy evaluations.

Suggested Citation

  • MINAMI, Koutaroh, 2025. "Detecting Bubbles by Machine Learning Prediction," Working Paper Series G-1-30, Hitotsubashi University Center for Financial Research.
    • Handle: RePEc:hit:hcfrwp:g-1-30
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    File URL: https://hit-u.repo.nii.ac.jp/record/2060981/files/070hcfrWP_1_030.pdf
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    References listed on IDEAS

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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