Research on the prediction of investor sentiment on stock market volatility

Konferenz: ICETIS 2022 - 7th International Conference on Electronic Technology and Information Science
21.01.2022 - 23.01.2022 in Harbin, China

Tagungsband: ICETIS 2022

Seiten: 4Sprache: EnglischTyp: PDF

Autoren:
Feng, WenFang; Jia, ShiLiang (Lanzhou University of Technology, School of Economics and Management, LanZhou, China)

Inhalt:
The application of deep learning model in the financial field has attracted the attention of many researchers in recent years. This paper uses Bi-directional Long Short-Term Memory Neural Network model to study the relationship between investor sentiment and Volatility Prediction of Shanghai Stock Exchange index. The results show that during the sample observation period, there is a significant negative correlation between investor sentiment and stock market volatility, and investor sentiment can effectively predict the stock market volatility trend. These findings show that investor sentiment is closely related to the performance of the stock market, and investor sentiment has a significant impact on the stock market.