Using social media mining technology to improve stock price forecast accuracy

Many stock investors make investment decisions based on stock-price-related chip indicators. However, in addition to quantified data, financial news often has a nonnegligible impact on stock price. Nowadays, as new reviews are posted daily on social media, there may be value in using web opinions to improve the performance of stock price prediction. To this end, we use logistic regression to screen the chip indicators and establish a basic stock price prediction model. Then, we employ text mining technology to quantify the unstructured data of social media opinions on stock-related news into sentiment scores, which are found to correlate significantly with the change extent of the stock price. Based on the findings that the higher the sentiment scores, the lower the prediction accuracy of the logistic regression model, we propose an improved prediction approach that integrates sentiment scores into the logistic regression model. Our results show that the proposed model can improve the prediction accuracy for stock prices, and can thus provide a new reference for investment strategies for stock investors.