Social media and the stock markets: an emerging market perspective
Abstract
There are numerous studies that examine the impact of social media on the stock market performance but there is a paucity of such evidences from the emerging economies. Today many multinational banks and other financial conglomerates from the developed countries are expanding their operations to the emerging markets, known for their rapid growth. The businesses in developed countries prefer using social media to reach out to their stakeholders. This might be a challenge as emerging markets are very different from the developed markets in terms of infrastructure and stock market development. This study performs the sentiment analysis of the tweets about the Indian companies that are a part of Nifty50 or any sectorial index, for a period of 15 months. The results from the Granger-causalty tests indicate that the Twitter sentiments have a significant relationship with the indices related to the banking and financial sectors of the Indian stock markets. Results from the Impulse Response Function reveal that, on the index returns, the impact of the negative sentiments stays for a longer period of time than the positive sentiments. This study would help businesses use social media effectively for information sharing and dissemination in the new environment.
Keyword : emerging economies, sentiment analysis, social media, Twitter, Indian stock markets, market efficiency, impulse response
How to Cite
Agarwal, S., Kumar, S., & Goel, U. (2021). Social media and the stock markets: an emerging market perspective. Journal of Business Economics and Management, 22(6), 1614-1632. https://doi.org/10.3846/jbem.2021.15619
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Fan, R., Talavera, O., & Tran, V. (2020b). Social media, political uncertainty, and stock markets. Review of Quantitative Finance and Accounting, 55(3), 1137–1153. https://doi.org/10.1007/s11156-020-00870-4
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Hart, K. L., Perlis, R. H., & McCoy, T. H. (2020). What do patients learn about psychotropic medications on the web? A natural language processing study. Journal of Affective Disorders, 260, 366–371. https://doi.org/10.1016/j.jad.2019.09.043
He, W., Guo, L., Shen, J., & Akula, V. (2016). Social media-based forecasting: A case study of Tweets and stock prices in the financial services industry. Journal of Organizational and End User Computing, 28(2), 74–91. https://doi.org/10.4018/joeuc.2016040105
Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014 (pp. 216–225). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPaper/8109
Ilavarasan, V., Kar, A., & Gupta, M. P. (2018). Social media and business practices in emerging markets: still unexplored. Journal of Advances in Management Research, 15(2), 110–114. https://doi.org/10.1108/jamr-05-2018-111
Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2017). Advances in social media research: Past, present and future. Information Systems Frontiers 2017, 20(3), 531–558. https://doi.org/10.1007/S10796-017-9810-Y
Kaushik, B., Hemani, H., & Ilavarasan, P. V. (2017). Social media usage vs. stock prices: An analysis of Indian firms. Procedia Computer Science, 122, 323–330. https://doi.org/10.1016/j.procs.2017.11.376
Kim, J., & Choi, H. (2019). Value co-creation through social media: A case study of a start-up company. Journal of Business Economics and Management, 20(1), 1–19. https://doi.org/10.3846/jbem.2019.6262
Kim, S. H., & Kim, D. (2014). Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior and Organization, 107(B), 708–729. https://doi.org/10.1016/j.jebo.2014.04.015
Klepek, M., & Starzyczná, H. (2018). Marketing communication model for social networks. Journal of Business Economics and Management, 19(3), 500–520. https://doi.org/10.3846/jbem.2018.6582
Kumaresh, N., Bonta, V., & Janardhan, N. (2019). A comprehensive study on lexicon based approaches for sentiment analysis. Asian Journal of Computer Science and Technology, 8(S2), 1–6. https://doi.org/10.51983/ajcst-2019.8.S2.2037
Lahey, M. (2016). Everyday life as a text. SAGE Open, 6(1), 2158244016633738. https://doi.org/10.1177/2158244016633738
Leitch, D., & Sherif, M. (2017). Twitter mood, CEO succession announcements and stock returns. Journal of Computational Science, 21, 1–10. https://doi.org/10.1016/J.JOCS.2017.04.002
Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69(1), 14–23. https://doi.org/10.1016/j.knosys.2014.04.022
Liu, B., & McConnell, J. J. (2013). The role of the media in corporate governance: Do the media influence managers’ capital allocation decisions? Journal of Financial Economics, 110(1), 1–17. https://doi.org/10.1016/j.jfineco.2013.06.003
Liu, L., Wu, J., Li, P., & Li, Q. (2015). A social-media-based approach to predicting stock comovement. Expert Systems with Applications, 42(8), 3893–3901. https://doi.org/10.1016/j.eswa.2014.12.049
Loughran, T., & Mcdonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x
Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.1111/j.1540-6261.1970.tb00518.x
Mobarek, A., & Fiorante, A. (2014). The prospects of BRIC countries: Testing weak-form market efficiency. Research in International Business and Finance, 30(1), 217–232. https://doi.org/10.1016/j.ribaf.2013.06.004
Mohan, R., & Kar, A. K. (2017). Demonetization and its impact on the Indian economy – Insights from social media analytics. In Lecture Notes in Computer Science: Vol. 10595. Digital nations – Smart cities, innovation, and sustainability (pp. 363–374). Springer, Cham. https://doi.org/10.1007/978-3-319-68557-1_32
National Stock Exchange. (2019). National stock exchange of India Ltd. NSE. https://www.nseindia.com/global/content/about_us/about_us.htm
Nayak, A., Manohara Pai, M. M., & Pai, R. M. (2016). Prediction models for Indian stock market. Procedia – Procedia Computer Science, 89, 441–449. https://doi.org/10.1016/j.procs.2016.06.096
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