Research on GEM Stock Return Forecasting Based on Investor Sentiment
Taking the GEM stock market as the main research object and the prediction of GEM stock returns was analyzed based on text mining method.The lexicon method was used to classify the sentiment tendency of GEM stock comments crawled from the Oriental Fortune stock bar from April 1,2021 to April 1,2023 to build an investor sentiment index.A support vector machine(Particle Swarm Optimization Support Vector Machine,PSO-SVM)model optimized based on particle swarm algorithm was constructed to predict the return.In the empirical analysis stage,"Ningde Times",the stock with the largest outstanding market capitalization in GEM,was selected as a representative,and its return was predicted and analyzed by PSO-SVM model,and a series of control models were set up for comparative analysis at the same time.The results show that the model proposed in this paper is better than the other control models(multiple linear regression,random forest,support vector machine),and the model with the introduction of the sentiment index is better than the model without the introduction of the sentiment index.
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