JACIII Vol.25 No.5 pp. 581-591
doi: 10.20965/jaciii.2021.p0581


Stock Prediction Based on News Text Analysis

Wentao Gu, Linghong Zhang, Houjiao Xi, and Suhao Zheng

Department of Statistics, School of Statistics and Mathematics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China

Corresponding author

July 5, 2020
April 13, 2021
September 20, 2021
sentiment index, sentiment lexicon, LSTM, stock returns

With the vigorous development of information technology, the textual data of financial news have grown massively, and this ever-rich online news information can influence investors’ decision-making behavior, which affects the stock market. Thus, online news is an important factor affecting market volatility. Quantifying the sentiment of news media and applying it to stock-market prediction has become a popular research topic. In this study, a financial news sentiment lexicon and an auxiliary lexicon applicable to the financial field are constructed, and a sentiment index (SI) is constructed by defining the weight of semantic rules. Then, a comprehensive sentiment index (CSI) is constructed via principal component analysis of the sentiment index and structured stock-market trading data. Finally, these two sentiment indices are added to the generalized autoregressive conditional heteroscedastic (GARCH) and the Long short-term memory (LSTM) models to predict stock returns. The results indicate that the prediction results of LSTM models are better than those of GARCH models. Compared with general-purpose lexicons, the financial lexicons constructed in this study are more stable, and the inclusion of a comprehensive investor sentiment index improves the accuracy of measuring sentiment information. Thus, the proposed lexicons allow more comprehensive measurement of the effects of external sentiment factors on stock-market returns and can improve the prediction effect of stock-return models.

Cite this article as:
W. Gu, L. Zhang, H. Xi, and S. Zheng, “Stock Prediction Based on News Text Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.5, pp. 581-591, 2021.
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