Research on Stock Price Forecast Based on News Sentiment Analysis—A Case Study of Alibaba

Abstract

Based on the media news of Alibaba and improvement of L&M dictionary, this study transforms unstructured text into structured news sentiment through dictionary matching. By employing data of Alibaba’s opening price, closing price, maximum price, minimum price and volume in Thomson Reuters database, we build a fifth-order VAR model with lags. The AR test indicates the stability of VAR model. In a further step, the results of Granger causality tests, impulse response function and variance decomposition show that VAR model is successful to forecast variables dopen, dmax and dmin. What’s more, news sentiment contributes to the prediction of all these three variables. At last, MAPE reveals dopen, dmax and dmin can be used in the out-sample forecast. We take dopen sequence for example, document how to predict the movement and rise of opening price by using the value and slope of dopen.