Does Twitter Activity Proxy for Idiosyncratic Information ? Evidence from the Indian Stock Market
DOI:
https://doi.org/10.17010/ijf/2021/v15i8/165815Keywords:
Twitter Activity
, Information Supply, Asymmetric Impact, Indian Stocks, Returns.JEL Classification Codes
, G12, G14, G41.Paper Submission Date
, March 10, 2020, Paper Sent Back for Revision, December 23, Paper Acceptance Date, February 20, 2021, Paper Published Online, August 30, 2021.Abstract
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.Downloads
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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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting stock market indicators through Twitter “I hope it is not as bad as I fearâ€. Procedia-Social and Behavioral Sciences, 26, 55–62. https://doi.org/10.1016/j.sbspro.2011.10.562