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