Share:


Social media and the stock markets: an emerging market perspective

    Shweta Agarwal   Affiliation
    ; Shailendra Kumar Affiliation
    ; Utkarsh Goel Affiliation

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
Published in Issue
Nov 18, 2021
Abstract Views
88
PDF Downloads
53
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Agarwal, S., Kumar, S., & Goel, U. (2019). Stock market response to information diffusion through internet sources: A literature review. International Journal of Information Management, 45, 118–131. https://doi.org/10.1016/j.ijinfomgt.2018.11.002

Ahmed, R. R., Vveinhardt, J., & Streimikiene, D. (2017). Interactive digital media and impact of customer attitude and technology on brand awareness: Evidence from the South Asian countries. Journal of Business Economics and Management, 18(6), 1115–1134. https://doi.org/10.3846/16111699.2017.1400460

Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of Internet stock message boards. Journal of Finance, 59(3), 1259–1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x

Bag, S., Tiwari, M. K., & Chan, F. T. S. (2019). Predicting the consumer’s purchase intention of durable goods: An attribute-level analysis. Journal of Business Research, 94, 408–419. https://doi.org/10.1016/j.jbusres.2017.11.031

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

Chang, C. H., & Lin, S. J. (2015). The effects of national culture and behavioral pitfalls on investors’ decision-making: Herding behavior in international stock markets. International Review of Economics and Finance, 37, 380–392. https://doi.org/10.1016/j.iref.2014.12.010

Chaturvedi, A. (2017). How India emerged as Twitter’s fastest growing market in terms of daily active users. In The Economic Times. https://economictimes.indiatimes.com/opinion/interviews/india-became-our-number-one-market-in-daily-users-twitters-new-india-director-taranjeet-singh/articleshow/58601906.cms

Chui, A. C. W., Titman, S., & Wei, K. C. J. (2010). Individualism and momentum around the world. Journal of Finance, 65(1), 361–392. https://doi.org/10.1111/j.1540-6261.2009.01532.x

Claessens, S., & Yurtoglu, B. B. (2013). Corporate governance in emerging markets: A survey. Emerging Markets Review, 15, 1–33. https://doi.org/10.1016/j.ememar.2012.03.002

Clement, J. (2019, November 21). Global social media ranking 2019 | Statista. Statista. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

Culnan, M. J., McHugh, P. J., & Zubillaga, J. I. (2010). How large U.S. companies can use Twitter and other social media to gain business value. MIS Quarterly Executive, 9(4), 243–259. https://pdfs.semanticscholar.org/a59f/46c2ee905fabc79885189c1c6781def6e45b.pdf

Deng, S., Huang, Z., Sinha, A. P., & Zhao, H. (2018). The interaction between microblog sentiment and stock returns: An empirical examination. MIS Quarterly: Management Information Systems, 42(3), 895–918. https://doi.org/10.25300/MISQ/2018/14268

Díaz-Mendoza, A. C., & Pardo, A. (2019). Holidays, weekends and range-based volatility. North American Journal of Economics and Finance, 52, 101124. https://doi.org/10.1016/j.najef.2019.101124

Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304. https://doi.org/10.2307/1392506

ETMarkets.com. (2020). SGX Nifty share price: SGX Nifty futures recover on Trump’s tweet, still down 100 points. The Economic Times. https://economictimes.indiatimes.com/markets/stocks/news/big-plunge-in-sgx-nifty-futures-signals-bloodbath-ahead-on-d-street/articleshow/73147846.cms

Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105. https://doi.org/10.1086/294743

Fan, R., Talavera, O., & Tran, V. (2020a). Social media bots and stock markets. European Financial Management, 26(3), 753–777. https://doi.org/10.1111/eufm.12245

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

Feldman, R., Govindaraj, S., Livnat, J., & Segal, B. (2010). Management’s tone change, post earnings announcement drift and accruals. Review of Accounting Studies, 15(4), 915–953. https://doi.org/10.1007/s11142-009-9111-x

Garman, M. B., & Klass, M. J. (2002). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67–78. https://doi.org/10.1086/296072

George, P. (2020, January 9). Nifty, Sensex end higher as U.S.-Iran tensions ebb. Thomson Reuters. https://in.reuters.com/article/india-stocks/nifty-sensex-end-higher-as-u-s-iran-tensions-ebb-idINKBN1Z80FB

Gonzalez, R. (2018). Facebook’s new data restrictions will handcuff even honest researchers. WIRED. https://www.wired.com/story/fb-data-restrictions-hobble-researchers?mbid=nl_032318_daily_list3_p2&CNDID=48046051

Hansson, B., & Hördahl, P. (2005). Forecasting variance using stochastic volatility and GARCH. European Journal of Finance, 11(1), 33–57. https://doi.org/10.1080/1351847021000025803

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

Oliveira, N., Cortez, P., & Areal, N. (2016). Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 85, 62–73. https://doi.org/10.1016/j.dss.2016.02.013

Phua, J., Jin, S. V., & Kim, J. J. (2017). Gratifications of using Facebook, Twitter, Instagram, or Snapchat to follow brands: The moderating effect of social comparison, trust, tie strength, and network homophily on brand identification, brand engagement, brand commitment, and membership intention. Telematics and Informatics, 34(1), 412–424. https://doi.org/10.1016/j.tele.2016.06.004

Rimkuniene, D., & Zinkeviciute, V. (2014). Social media in communication of temporary organisations: Role, needs, strategic perspective. Journal of Business Economics and Management, 15(5), 899–914. https://doi.org/10.3846/16111699.2014.938360

Risius, M., Akolk, F., & Beck, R. (2015). Differential emotions and the stock market – The case of company-specific trading. In Twenty-Third European Conference on Information Systems (ECIS). Münster, Germany (pp. 1–17). https://core.ac.uk/download/pdf/50528565.pdf

Securities and Exchange Board of India. (2018, March 16). Order in the matter of Mr. Anirudh Sethi. SEBI. https://www.sebi.gov.in/enforcement/orders/mar-2018/order-in-the-matter-of-mr-anirudh-sethi_38263.html

Selyukh, A. (2013, April 23). Hackers send fake market-moving AP tweet on White House explosions. REUTERS. https://www.reuters.com/article/net-us-usa-whitehouse-ap/hackers-send-fake-market-moving-ap-tweet-on-white-house-explosions-idUSBRE93M12Y20130423

Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook’s daily sentiment and international stock markets. Journal of Economic Behavior and Organization, 107(B), 730–743. https://doi.org/10.1016/j.jebo.2014.06.004

Sprenger, T. O., Sandner, P. G., Tumasjan, A., & Welpe, I. M. (2014). 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

Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139–1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x

Trusov, M., Bucklin, R. E., & Pauwels, K. H. (2008). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. SSRN, 73(5), 90–102. https://doi.org/10.2139/ssrn.1129351

van Dieijen, M., Borah, A., Tellis, G. J., & Franses, P. H. (2020). Big data analysis of volatility spillovers of brands across social media and stock markets. Industrial Marketing Management, 88, 465–484. https://doi.org/10.1016/j.indmarman.2018.12.006

You, W., Guo, Y., & Peng, C. (2017). Twitter’s daily happiness sentiment and the predictability of stock returns. Finance Research Letters, 23, 58–64. https://doi.org/10.1016/j.frl.2017.07.018

Zhang, W., Li, X., Shen, D., & Teglio, A. (2016). Daily happiness and stock returns: Some international evidence. Physica A: Statistical Mechanics and Its Applications, 460, 201–209. https://doi.org/10.1016/j.physa.2016.05.026

Zhang, Z., Zhang, Y., Shen, D., & Zhang, W. (2018). The cross-correlations between online sentiment proxies: Evidence from Google Trends and Twitter. Physica A: Statistical Mechanics and Its Applications, 508, 67–75. https://doi.org/10.1016/j.physa.2018.05.051

Zheludev, I., Smith, R., & Aste, T. (2014). When can social media lead financial markets? Scientific Reports, 4(1), 1–12. https://doi.org/10.1038/srep04213