Daily market news sentiment and stock prices

ABSTRACT

In recent years there has been a tremendous growth in readily available news related to traded assets in international financial markets. This financial news is now available 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 stock price movement as these news items are swiftly transformed into investors sentiment which in turn drives prices. In this study, we use the Thomson Reuters News Analytics (TRNA) data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index constituents. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock returns of these constituents using a multi-factor model. We augment the Fama–French three-factor model with the day’s sentiment score along with lagged scores to evaluate the additional effects of financial news sentiment on stock prices in the context of this model using Ordinary Least Square (OLS) and Quantile Regression (QR) to analyse the effect around the tail of the return distribution. We also conduct the analysis using the seven-day simple moving average (SMA) of the scores to account for news released on non-trading days. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent 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. The results also indicate that the SMA measure does not have a significant effect on the returns. The analysis using Quantile Regression provides evidence that the news has more impact on left tail compared to the right tail of the returns.

KEYWORDS:

• Sentiment analysis
• financial news
• factor models
• asset pricing

JEL CLASSIFICATION:

• C21
• G12
• G14

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ See the Handbook of News Analytics in Finance (Mitra and Mitra 2011) for further details on TRNA data set.

² mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

³ A further analysis can be conducted as part of the future work to test the scores as priced risk factors.

⁴ The tables do not report the p-values for brevity, complete tables with p-values can be obtained from the corresponding author.

⁵ The analysis is conducted for all the five moving 2-year periods, the rest of the results can be obtained from the authors on request.

⁶ The analysis is also repeated for various lags (1–5 days) and SMA (14,28,60,90 days) periods but all other specifications do not work as well as the one with daily sentiment scores.

Additional information

Funding

This work was supported by the QUANTVALLEY/FdR: ‘Quantitative Management Initiative’