ARIMA-BPNN based Stock Price Prediction Model Based on Fusion News Sentiment Analysis

Abstract

In recent years, the prediction of stocks has mainly focused on improving and combining stock prediction algorithms, or analyzing news sentiment tendencies to simulate subjective investor consciousness. However, both methods have shortcomings in practicality and comprehensiveness. Therefore, based on the use of stock data, the sentiment propensity of vocabulary in the article was processed, and a new algorithm model was obtained by combining the differential integration moving average autoregressive model and backpropagation feedforward neural network model. Finally, sentiment propensity was integrated into the combination model to obtain an algorithm model that integrates sentiment analysis. After optimizing the sentiment vocabulary of news. The algorithm has improved its ability to recognize emotional tendency words, while traditional algorithms have been improved to improve the accuracy of stock prediction, further verifying the relationship curve between emotional tendency and stock prediction fluctuations. The experimental results show that the combined model of sentiment analysis is close to the true value in predicting stock results, with an error of less than 1.5%. The accuracy and stability of the model’s prediction results are significantly better than the uncombined model and traditional prediction models. The new combination model provides better judgment basis for investors through experimental prediction results, creating conditions for investors to avoid stock market risks and improve investment value.