Abstract: Financial bubbles have long been a focus of researchers, particularly due to the severe negative impacts following the bursting of financial bubbles. Therefore, the ability to effectively predict financial bubbles is of paramount importance. The aim of this study is to measure and predict the stock market price bubble in China from January 2015 to December 2023. To achieve this, we utilized the GSADF test, currently the most effective, to identify and measure the situation of the stock market price bubble in China. Subsequently, we selected inflation rate, consumer confidence index, stock yield, and price-earnings ratio as explanatory/predictive variables. Finally, four machine learning methods were employed to forecast the stock market price bubble in China. The results indicate that a price bubble occurred in the Chinese stock market during the first half of 2015, before the outbreak of the COVID-19 pandemic in China in January 2020. Furthermore, the comparison reveals that among the machine learning methods, logistic regression is the most suitable and effective for China, while other methods such as deep learning and decision trees also hold certain value.

Yunxi Wang and Tongjai Yampaka, “Predicting Stock Price Bubbles in China Using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151139

@article{Wang2024,
title = {Predicting Stock Price Bubbles in China Using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151139},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151139},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Yunxi Wang and Tongjai Yampaka}
}