Abstract
Investor sentiment has been widely used in the research of the stock market, and how to accurately measure investor sentiment is still being explored. With the rise of social media, investor sentiment is no longer only influenced by macroeconomic data and news media, but also guided by We-Media and fragmented information. We take the data of China A-shares from January 2020 to December 2020 as the research object and propose a stock price prediction method that combines investor sentiment with multisource information. Firstly, the sentiment of macroeconomic data, brokerage research reports, news, and We-Media is calculated, respectively, and then the investor sentiment vector combining multisource information is obtained by the multilayer perceptron. Finally, the LSTM model is used to represent the stock time series characteristics. The results show that (1) the proposed algorithm is superior to the benchmark algorithm in terms of accuracy and F1-score, (2) investor sentiment vector can effectively measure the investment sentiment of stocks, and (3) compared with vector concatenation, multilayer perceptron can better represent investor sentiment.
Highlights
Behavioral finance, which is derived from finance, psychology, communication, and behavioral science, believes that the stock price is determined by the intrinsic value of an enterprise but is largely influenced by the psychology and behavior of investors [1]. e idea in behavioral finance is that investors in markets are not completely rational people
Sentiment indicators obtained from text analysis of social media content have been widely used in stock market prediction, but there is no consistency in research conclusions [15]
The method we proposed has achieved the best results in both accuracy and F1-score, which shows that investor sentiment vectors combined with multisource information can effectively improve the performance of stock price prediction
Summary
Behavioral finance, which is derived from finance, psychology, communication, and behavioral science, believes that the stock price is determined by the intrinsic value of an enterprise but is largely influenced by the psychology and behavior of investors [1]. e idea in behavioral finance is that investors in markets are not completely rational people. Song et al [4] proposed a method for predicting stock excess returns that integrates research reports and investor sentiment, which can be verified in the Chinese A-share market to effectively improve the accuracy of the forecast. We put forward a kind of multisource information fusion method to predict the price of the shares of investor’s emotion; first, the sentiment of macroeconomic data, securities research reports, news, and the media is calculated, fusion of multisource information is obtained by concatenation operation ISV (Investor Sentiment Vector, ISV), and LSTM model is used to represent the stock time series characteristics. (1) An investor sentiment measurement method integrating multisource information is proposed (2) e positive role of investor sentiment in the stock prediction task is verified (3) A stock price prediction framework is proposed based on deep learning e rest of this paper is organized as follows.
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