|Frequency:||Jun & Dec|
1 Institute of Management Studies, Banaras Hindu University, Varanasi, Uttar Pradesh, India
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.
The outbreak of COVID-19 was an extreme event that created panic among the people, disrupted normal functioning and adversely affected the economy. The stock market fell due to the uncertainty that ensued, and it continues to do so as new variants are being discovered. The study explores the impact of the outbreak of a new variant of COVID-19 on the stock market and analyses investor sentiment towards investing and global investing through sentiment analysis using QSR NVivo software. The results contribute to the extant literature that the investors’ sentiment is positive towards global investing despite the adverse conditions. Investors need to choose those stocks that are internationally diversified with sound fundamentals. The study reveals that markets bounce after a significant cooling period, and investment managers should encourage the investors to hold their portfolios. The study also identifies the important themes and social networking of investors from India with the world through the NodeXL. The study identified major themes such as alerts, timing, investors and investing, which shows that the people mainly focus on stocks, its alert and timing for making investment decisions.
Stock market crash, COVID-19 pandemic, sentiment analysis, social network analysis, thematic analysis
Ahmed, W., Bath, P. A., Sbaffi, L., & Demartini, G. (2019). Novel insights into views towards H1N1 during the 2009 Pandemic: A thematic analysis of Twitter data. Health Information and Libraries Journal, 36(1), 60–72. https://doi.org/10.1111/hir.12247
Ahmed, W., & Lugovic, S. (2019). Social media analytics: Analysis and visualisation of news diffusion using NodeXL. Online Information Review, 43(1), 149–160. https://doi.org/10.1108/OIR-03-2018-0093
Alam, M. N., Alam, M. S., & Chavali, K. (2020). Stock market response during COVID-19 lockdown period in India: An event study. Journal of Asian Finance, Economics and Business, 7(7), 131–137. https://doi.org/10.13106/jafeb.2020.vol7.no7.131
Almasarweh, M., & Wadi, S. A. L. (2018). ARIMA Model in predicting banking stock market data. Modern Applied Science, 12(11), 309. https://doi.org/10.5539/mas.v12n11p309
Anand, A., Basu, S., Pathak, J., & Thampy, A. (2021). The impact of sentiment on emerging stock markets. International Review of Economics and Finance, 75, 161–177. https://doi.org/10.1016/j.iref.2021.04.005
Bai, L., Wei, Y., Wei, G., Li, X., & Zhang, S. (2020). Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective. Finance Research Letters, 40, 101709. https://doi.org/10.1016/j.frl.2020.101709
Baig, A. S., Butt, H. A., Haroon, O., & Rizvi, S. A. R. (2021). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance Research Letters, 38, 101701. https://doi.org/10.1016/j.frl.2020.101701
Bozkurt, A. (2020). Book review: Analyzing social media networks with NodeXL - insights from a connected world. Contemporary Educational Technology, 8(2), 191–194. https://doi.org/10.30935/cedtech/6195
Gaire, H. N. (2017). Forecasting NEPSE Index: An ARIMA and GARCH approach. NRB Economic Review, 29(1), 53–66.
Goel, A., & Mittal, A. (2012). Stock prediction using Twitter sentiment analysis. Stanford University, CS229 (2011). http://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf media networks with NodeXL. Journal of Human Computer Interaction, 27(4), 255–263. https://doi.org/10.1016/C2009-0-64028-9
Hatefi Ghahfarrokhi, A., & Shamsfard, M. (2020). Tehran stock exchange prediction using sentiment analysis of online textual opinions. Intelligent Systems
Hansen, D. L., Shneiderman, B., & Smith, M. A. (2011). Analyzing social
in Accounting, Finance and Management, 27(1), 22–37. https://doi.org/10.1002/isaf.1465
Huang, J. Y., & Liu, J. H. (2020). Using social media mining technology to improve stock price forecast accuracy. Journal of Forecasting, 39(1), 104–116. https://doi.org/10.1002/for.2616
Huang, Z. Xiong, Tang, Q., & Huang, S. (2020). Foreign investors and stock price crash risk: Evidence from China. Economic Analysis and Policy, 68, 210–223. https://doi.org/10.1016/j.eap.2020.09.016
Jain, K. (2021). A study of investment opportunities and investors’ sentiments during COVID-19 pandemic. International Journal of Indian Culture and Business Management, 24(3), 283–302.
Jain, K., & Singh, S. (2022). Ramifications of digitalization in higher education institutions concerning Indian educators: A thematic analysis. In Transforming Higher Education through Digitalization (pp. 91–111). Taylor & Francis. https://doi.org/10.1201/ 9781003132097
Kamyab, M., Tao, R., Mohammadi, M. H., & Rasool, A. (2018). Sentiment analysis on Twitter: A text mining approach to the Afghanistan status reviews. ACM International Conference Proceeding Series, 9(4), 14–19. https://doi.org/10.1145/3293663.3293687
Katoch, R., & Sidhu, A. (2021). An application of ARIMA Model to forecast the dynamics of COVID-19 epidemic in India. Global Business Review, 22(4), 1–14. https://doi.org/10.1177/0972150920988653
Mustapa, F. H., & Ismail, M. T. (2019). Modelling and forecasting S&P 500 stock prices using hybrid Arima-Garch Model. Journal of Physics: Conference Series, 1366(1). https://doi.org/10.1088/1742-6596/1366/1/012130
Nguyen, T. H., & Shirai, K. (2015). Topic modeling based sentiment analysis on social media for stock market prediction. ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference, 1, 1354–1364. https://doi.org/10.3115/v1/p15-1131
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. https://doi.org/10.1177/1609406917733847
Okorie, D. I., & Lin, B. (2021). Stock markets and the COVID-19 fractal contagion effects. Finance Research Letters, 38, 101640. https://doi.org/10.1016/j.frl.2020.101640
Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2017). Sentiment analysis of Twitter data for predicting stock market movements. International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings, 1345–1350. https://doi.org/10.1109/SCOPES.2016.7955659
Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC, 1320–1326. https://doi.org/10.17148/ijarcce.2016.51274
Pandey, V. S., & Bajpai, A. (2019). Predictive efficiency of ARIMA and ANN models: A case analysis of nifty fifty in Indian stock market. International Journal of Applied Engineering Research, 14(2), 973–4562.
Phan, D. H. B., & Narayan, P. K. (2020). Country responses and the reaction of the stock market to COVID-19—A preliminary exposition. Emerging Markets Finance and Trade, 56(10), 2138–2150. https://doi.org/10.1080/1540496X.2020.1784719
Salisu, A. A., Ebuh, G. U., & Usman, N. (2020). Revisiting oil-stock nexus during COVID-19 pandemic: Some preliminary results. International Review of Economics and Finance, 69(June), 280–294. https://doi.org/10.1016/j.iref.2020.06.023
Salisu, A. A., & Vo, X. V. (2020). Predicting stock returns in the presence of COVID-19 pandemic: The role of health news. International Review of Financial Analysis, 71(June), 101546. https://doi.org/10.1016/j.irfa.2020.101546
Schmidt, C. G., Wuttke, D. A., Ball, G. P., & Heese, H. S. (2020). Does social media elevate supply chain importance An empirical examination of supply chain glitches, Twitter reactions, and stock market returns. Journal of Operations Management, 66(6), 646–669. https://doi.org/10.1002/joom.1087
Selmi, R., Hammoudeh, S., Errami, Y., & Wohar, M. E. (2021). Is COVID-19 related anxiety an accelerator for responsible and sustainable investing A sentiment analysis. Applied Economics, 53(13), 1528–1539. https://doi.org/10.1080/00036846.2020.1834501
Shaikh, I. (2021). On the relation between pandemic disease outbreak news and crude oil, gold, gold mining, silver and energy markets. Resources Policy, 72(March), 102025. https://doi.org/10.1016/j.resourpol.2021.102025
Sharma, R., & Gupta, S. (2021). Bharat towards atmanirbharta: A Twitter based analysis using NVivo. Journal of Content, Community and Communication, 13(7), 58–65. https://doi.org/10.31620/JCCC.06.21/07
Smailovi, J., Grar, M., Lavra, N., & Žnidarši, M. (2013). Predictive sentiment analysis of tweets: A stock market application. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7947 LNCS, 77–88. https://doi.org/10.1007/978-3-642-39146-0_8
Srivastava, A., Singh, V., & Drall, G. S. (2019). Sentiment analysis of Twitter data: A hybrid approach. International Journal of Healthcare Information Systems and Informatics, 14(2), 1–16. https://doi.org/10.4018/IJHISI.2019040101
Sun, Y., Liu, X., Chen, G., Hao, Y., & Zhang, Z. (Justin). (2020). How mood affects the stock market: Empirical evidence from microblogs. Information and Management, 57(5), 103181. https://doi.org/10.1016/j.im.2019.103181
Sunarya, I. W. (2019). Modelling and forecasting stock market volatility of Nasdaq composite index. EAJ (Economics and Accounting Journal), 2(3), 181. https://doi.org/10.32493/eaj.v2i3.y2019.p181-189
Susruth, M. (2017). Financial forecasting: An empirical study on box –Jenkins methodology with reference to the Indian stock market. Pacific Business Review International, 10(2), 115–123.
Wagner, A. F. (2020). What the stock market tells us about the post-COVID-19 world. Nature Human Behaviour, 4(5), 440. https://doi.org/10.1038/s41562-020-0869-y
Zhang, W., Gong, X., Wang, C., & Ye, X. (2021). Predicting stock market volatility based on textual sentiment: A nonlinear analysis. Journal of Forecasting, 40(8), 1479–1500. https://doi.org/10.1002/for.2777