Optimization research on stock selection strategy based on NLP
This article focuses on the impact of stock reviews and news on stock prices.In order to select high-quality stocks to improve investment returns,natural language processing(NLP)technology was used to analyze stock review and news data.Based on a naive Bayesian model,a text sentiment tendency classification model was established,with a prediction accuracy of 84%and the generation of stock review factors.Based on the LDA topic model,topic modeling is performed on news texts to quickly obtain the topic of the news text,and confusion is introduced to search for the optimal number of topics in the document.News factors are generated,and stock evaluation factors and news factors are used as the basis for screening stocks.This article obtains the influenc-ing factors on the stock market from stock evaluation and news information,thereby optimizing stock selection strategies.For stock fundamental data,this article uses a decision tree model for factor importance analysis,selecting the top 5 most important factors.The model′s prediction accuracy reaches 88%.Through the decision tree model,it can more accurately determine which factors play a key role in influencing stock price changes.This improved method can improve the effectiveness and accuracy of stock selec-tion strategies.Finally,principal component analysis(PCA)is used to reduce the dimensionality of the data and select stocks based on the values of the principal components.
natural language processingtext sentiment tendency classification modelLDA theme modeldecision tree modelprincipal component analysis
NETL
NSTL
万方数据
