The relation between news events and stock price jump: An analysis based on neural network

Abstract

The efficient-market hypothesis states that price movements are extremely efficient in reflecting information flows. Some studies have shown that stock prices are related to news events such as earnings announcements, political events and corporate takeovers, while others have failed to find convincing evidence to relate price changes to news. The aim of this study is to explore the relation between news and abnormal financial market volatility. We first investigate the Granger causality between news and stock returns. Our results show that stock price change is the Granger cause of news volume and news sentiment; news volume is not the Granger cause of stock price change while news sentiment is the Granger cause of stock price change. Moreover, we utilize an artificial neural network model to predict stock market collapses by using different volatility parameters. The findings from this study will further our understanding of stock price movements and the reasons for stock market collapses.