关键词: LSTM, 投资者情绪, 金融科技, 股票收盘价

Abstract: In the development of Fintech, the Internet public opinion may contribute to the forecast of the industry?s index data, but the relevant research is still insufficient. We use the Fintech stock transaction data in the Wind database as a microcosm of the financial technology industry, and use the sentiment classification model to mine the investor sentiment in the crawled more than 110000 Weibo texts. The study finds that the proportion of negative investor sentiment has a negative effect on the average closing price of the sample 84 Fintech stocks, and has a long-term stable equilibrium relationship. Furthermore, we construct a long short-term memory (LSTM) neural network model, which uses negative investor sentiment, weekday variables, and other quantitative index data of Fintech stocks as model inputs to predict the average closing price index data of Fintech stocks. The results show that after introducing the proportion of negative investor sentiment, the LSTM model of the experimental group has better results than the control group in forecast evaluation index, which illustrates the important role of internet public opinion in predicting the closing price of Fintech stocks. Then, the LSTM model in the experimental group also has better prediction results over different prediction periods than other control models (random forest, MLP neural network and support vector regression model), which further confirms its good prediction performance and model robustness. The article further enriches the research of natural language processing and deep learning technology in the field of Fintech, and provides new ideas for the prediction of relevant index data in the Fintech industry.

Key words: LSTM, Investor Sentiment, Fintech, Stock Closing Price