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Machine Learning to Forecast Financial Bubbles in Stock Markets: Evidence from Vietnam

Author

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  • Kim Long Tran

    (Department of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam)

  • Hoang Anh Le

    (Department of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam)

  • Cap Phu Lieu

    (Department of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam)

  • Duc Trung Nguyen

    (Department of Banking, Ho Chi Minh University of Banking, No. 36 Ton That Dam Street, Nguyen Thai Binh Ward, District 1, Ho Chi Minh City 700000, Vietnam)

Abstract

Financial bubble prediction has been a significant area of interest in empirical finance, garnering substantial attention in the literature. This study aims to detect and forecast financial bubbles in the Vietnamese stock market from 2001 to 2021. The PSY procedure, which involves a right-tailed unit root test to identify the existence of financial bubbles, was employed to achieve this goal. Machine learning algorithms were then utilized to predict real-time financial bubble events. The results revealed the presence of financial bubbles in the Vietnamese stock market during 2006–2007 and 2017–2018. Additionally, the empirical evidence supported the superior performance of the random forest and artificial neural network algorithms over traditional statistical methods in predicting financial bubbles in the Vietnamese stock market.

Suggested Citation

  • Kim Long Tran & Hoang Anh Le & Cap Phu Lieu & Duc Trung Nguyen, 2023. "Machine Learning to Forecast Financial Bubbles in Stock Markets: Evidence from Vietnam," IJFS, MDPI, vol. 11(4), pages 1-18, November.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:4:p:133-:d:1276351
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    References listed on IDEAS

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    2. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    3. Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021. "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
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    Cited by:

    1. Christos I. Giannikos & Hany Guirguis & Andreas Kakolyris & Tin Shan (Michael) Suen, 2024. "When to Hedge Downside Risk?," Risks, MDPI, vol. 12(2), pages 1-20, February.

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