The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Yelin Li, Hui Bu, Jiahong Li and JunJie Wu

International Journal of Forecasting, 2020, vol. 36, issue 4, 1541-1562

Abstract: Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.

Keywords: Textual data; Text mining; Naïve Bayes classification algorithm; Deep learning method; Stock price forecasting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:4:p:1541-1562

DOI: 10.1016/j.ijforecast.2020.05.001

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