Sentiments Extracted from News and Stock Market Reactions in Vietnam

Loan Thi Vu (), Dong Ngoc Pham, Hang Thu Kieu and Thuy Thi Thanh Pham
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Loan Thi Vu: Department of Banking and Finance, VNU University of Economics and Business, Vietnam National University, Hanoi 100000, Vietnam
Dong Ngoc Pham: Department of Information Technology, VNU University of Engineering and Technology, Vietnam National University, Hanoi 100000, Vietnam
Hang Thu Kieu: Department of Banking and Finance, VNU University of Economics and Business, Vietnam National University, Hanoi 100000, Vietnam
Thuy Thi Thanh Pham: Department of Banking and Finance, VNU University of Economics and Business, Vietnam National University, Hanoi 100000, Vietnam

IJFS, 2023, vol. 11, issue 3, 1-16

Abstract: News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover news sentiments and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors are concerned with the daily news by testing the movements of the market before and after the news is released. The results of the analysis show that there is an insignificant difference in the stock price as a response to the news. However, negative news sentiments can alter the variance of market returns.

Keywords: behavioral finance; Natural Language Processing; overreact; PhoBERT; textual analysis; underreact (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2023
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