Detecting Bubbles by Machine Learning Prediction

Koutaroh Minami

No G-1-30, Working Paper Series from Hitotsubashi University Center for Financial Research

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.

Keywords: Bubbles; Generalized Supremum Augmented Dickey-Fuller test (GSADF); Machine learning; Long Short Term Memory (LSTM) (search for similar items in EconPapers)
JEL-codes: G10 G17 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2025-06
New Economics Papers: this item is included in nep-ets, nep-fmk and nep-for
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https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/85951/070hcfrWP_1_030.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:hit:hcfrwp:g-1-30

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