This study analyzed the impact of stock-specific information, proxied by Twitter activity, on the stock returns of listed Indian firms. Twitter activities at different levels of information assimilation were considered through tweet-count, retweet-count, and favorite-count as proxies for information supply, information propagation, and information affirmation/validation, respectively. Day-to-day price movement and price movements in two sub-periods: the off-market-hours and the market-hours were considered separately for the analysis. It was also tested whether Twitter activities had an asymmetric impact on stock returns on days with positive and negative sentiments. The study was carried out with data of over 2.4 million tweets about 437 Indian firms listed on the Bombay Stock Exchange for 124 trading days. Panel data analysis with random and fixed effects was employed to test whether the Twitter activity is a significant price mover. The results showed that all three measures of Twitter activity significantly impacted stock returns on a day-to-day basis, especially during the overnight period. However, during market hours, only tweet-count had a significant impact on stock price movements. Further, the results revealed that Twitter activity had the most significant impact on the price of a stock when the market sentiment about the stock was negative. This study is the first large-scale study in the Indian context and opens up the possibilities of further research on these lines.

1. Aggarwal, R., Gopal, R., Gupta, A., & Singh, H. (2012). Putting money where the mouths are : The relation between venture financing and electronic word-of-mouth. Information Systems Research, 23(3–part–2), 976–992. https://doi.org/10.1287/isre.1110.0402
2. Berry, T. D., & Howe, K. M. (1994). Public information arrival. The Journal of Finance, 49(4), 1331–1346. https://doi.org/10.1111/j.1540-6261.1994.tb02456.x
3. Bhardwaj, A., Narayan, Y., Vanraj, Pawan, & Dutta, M. (2015). Sentiment analysis for Indian stock market prediction using Sensex and Nifty. Procedia Computer Science, 70, 85–91. https://doi.org/10.1016/j.procs.2015.10.043
4. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007
5. Carretta, A., Farina, V., Martelli, D., Fiordelisi, F., & Schwizer, P. (2011). The impact of corporate governance press news on stock market returns. European Financial Management, 17(1), 100–119. https://doi.org/10.1111/j.1468-036X.2010.00548.x
6. Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461–1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
7. Dangi, M., & Kohli, B. (2018). Role of behavioral biases in investment decisions : A factor analysis. Indian Journal of Finance, 12(3), 43–57. https://doi.org/10.17010/ijf/2018/v12i3/121997
8. Deng, S., Huang, Z. J., Sinha, A. P., & Zhao, H. (2018). The interaction between microblog sentiment and stock returns : An empirical examination. MIS Quarterly, 42(3), 895–918.
9. Malik, F. (2011). Estimating the impact of good news on stock market volatility. Applied Financial Economics, 21(8), 545–554. https://doi.org/10.1080/09603107.2010.534063
10. Mangala, D., & Sharma, M. (2014). A brief mapping of theory and evidence of investors’ behavioural biases. Indian Journal of Finance, 8(8), 44–56. https://doi.org/10.17010/ijf/2014/v8i8/71855
11. Nayak, A., Pai, M. M., & Pai, R. M. (2016). Prediction models for Indian stock market. Procedia Computer Science, 89, 441– 449. https://doi.org/10.1016/j.procs.2016.06.096
12. Ranjan, S., Singh, I., Dua, S., & Sood, S. (2018). Sentiment analysis of stock blog network communities for prediction of stock price trends. Indian Journal of Finance, 12(12), 7–21. https://doi.org/10.17010/ijf/2018/v12i12/139888
13. Raut, R. K., & Das, N. (2015). Behavioral prospects of individual investor decision making process : A review. Indian Journal of Finance, 9(4), 44–55. https://doi.org/10.17010/ijf/2015/v9i4/71457
14. Renault, T. (2017). Intraday online investor sentiment and return patterns in the US stock market. Journal of Banking & Finance, 84, 25–40. https://doi.org/10.1016/j.jbankfin.2017.07.002
15. Rubin, A., & Rubin, E. (2010). Informed investors and the internet. Journal of Business Finance & Accounting, 37(7–8), 841–865. https://doi.org/10.1111/j.1468-5957.2010.02187.x
16. Ryan, P., & Taffler, R. J. (2004). Are economically significant stock returns and trading volumes driven by firm specific news releases ? Journal of Business Finance & Accounting, 31(1–2), 49–82. https://doi.org/10.1111/j.0306-686X.2004.0002.x
17. Sprenger, T. O., Sandner, P. G., Tumasjan, A., & Welpe, I. M. (2014a.). News or noise ? Using Twitter to identify and understand company - specific news flow. Journal of Business Finance & Accounting,41(7–8), 791–830. https://doi.org/10.1111/jbfa.12086
18. Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014b.). Tweets and trades : The information content of stock microblogs. European Financial Management, 20(5), 926–957. https://doi.org/10.1111/j.1468036X.2013.12007.x
19. Tewari, R., & Pathak, T. (2015). A correlation between mass media communication and foreign investments in India. Prabandhan : Indian Journal of Management, 8(10), 32–42. https://doi.org/10.17010/pijom/2015/v8i10/79829
20. Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808–1821. https://doi.org/10.1016/j.jbankfin.2012.02.007
21. Yang, S. Y., Mo, S. Y., & Liu, A. (2015). Twitter financial community sentiment and its predictive relationship to stock market movement. Quantitative Finance,15(10), 1637–1656. https://doi.org/10.1080/14697688.2015.1071078
22. Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value : A sentiment analysis approach. Decision Support Systems, 55(4), 919–926. https://doi.org/10.1016/j.dss.2012.12.028
23. Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55–62. https://doi.org/10.1016/j.sbspro.2011.10.562