An interval constraint-based trading strategy with social sentiment for the stock market  

作　　者：Mingchen Li Kun Yang Wencan Lin Yunjie Wei Shouyang Wang 

机构地区：[1]Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Zhongguancun East Road,#55,Haidian District,Beijing,100190,People’s Republic of China [2]School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing,100190,People’s Republic of China [3]Center for Forecasting Science,Chinese Academy of Sciences,Beijing,100190,People’s Republic of China

出　　处：《Financial Innovation》2024年第1期2768-2798,共31页金融创新(英文)

基　　金：partly supported by the National Natural Science Foundation of China under Grants No.72171223,No.71801213,and No.71988101;the National Key R&D Program of China under Grants No.2021ZD0111204。

摘　　要：Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.

关 键 词：Stock price forecasting Deep learning Sentiment analysis Trading strategy COVID-19 era 

分 类 号：F42[经济管理—产业经济]