Abstract
News reports have become an imperative conduit of public information. Several recent studies have used news data from public media to investigate the impact of news on stock market returns. This study analyses the usefulness of news for predicting stock returns in the Taiwan stock market. We employ text mining of economic news, transform documents using a keyword matrix, and then convert the results into news variables. Subsequently, together with other quantitative variables, we construct a fixed effect model to investigate the behaviours of stock market returns in 20 subsectors from January 2008 to December 2014. Empirical analysis reveals that the news variables provide useful information for predicting Taiwan stock market returns, although the out-sample performance is only marginally improved. We also discover an asymmetric effect of economic news for predicting stock market returns. The prediction accuracy is higher when the stock market is booming than when it is glooming.
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