# 15-090/III (2015-07-28)

David E. Allen, Centre for Applied Financial Studies, Adelaide, University of South Australia, and University of Sydney, Australia; Michael McAleer, National Tsing Hua University, Taiwan, Erasmus University Rotterdam, the Netherlands, and Complutense University of Madrid, Spain; Abhay K. Singh, Edith Cowan University, Perth, Australia
Sentiment Analysis, Financial News, Factor Models, Asset Pricing
JEL codes:
C31, G12, G140

In recent years there has been a tremendous growth in the influx of news related to traded assets in international financial markets. This financial news is now available via print media but also through real-time online sources such as internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially
significant impact on market price formation as these news items are swiftly transformed into investors sentiment which in turn drives prices. Various commercial agencies have started developing their own financial news data sets which are used by investors and traders to support their algorithmic trading strategies. Thomson Reuters News Analytics (TRNA)1 is one such data set. In this study we use the TRNA data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock prices of these companies using a multi-factor model. We use an augmented Fama French Three Factor Model to evaluate the additional effects of financial news sentiment on stock prices in the context of this model. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent company returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series.