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

Kim Long Tran, Hoang Anh Le (), Cap Phu Lieu and Duc Trung Nguyen
Additional contact information
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

IJFS, 2023, vol. 11, issue 4, 1-18

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.

Keywords: financial bubbles; machine learning algorithms; Vietnamese stock market (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7072/11/4/133/pdf (application/pdf)
https://www.mdpi.com/2227-7072/11/4/133/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijfss:v:11:y:2023:i:4:p:133-:d:1276351

Access Statistics for this article

IJFS is currently edited by Ms. Hannah Lu

More articles in IJFS from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().