The Aggregate of Earnings and Announcement Returns with the Help of Twitter Using "Wisdom of Crowds" Theory and "Macro Accounting" Theory: Evidence from NYSE and Nasdaq

Document Type : Original Article

Author

Department of Accounting, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract

This study aims to predict the aggregate of earnings and announcement returns with the help of this purpose, by a selection of Twitter social media as a media approved by the US Stock Exchange and Securities Organization, data related to 345 companies selected from the list of top 500 companies in the United States, for the four years 2016-2019 (Market participant tweets about sample companies from October 2015 to March 2020), extracted and used Was analyzed from Stata software. The results showed that the volume of earnings news published by companies on Twitter could predict earnings surprises, and the content of earnings news published by companies on Twitter can predict earnings surprises. The results also showed that the volume of earning news published by companies on Twitter could predict announcement returns and the content of earning news published by companies on Twitter can predict announcement returns. The results can also be considered a strategy in content analysis of market-quality news based on a list of specialized financial words.

Keywords

Main Subjects


©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

1. Abarbanell, J. (1991). Do analysts’ earnings forecasts incorporate information in prior stock price changes? Journal of Accounting and Economics, 14(2), pp. 147–165. https://doi.org/10.1016/0165-4101(91)90003-7.
2. Abarbanell, J. S., and Bernard, V. L. (1992). Tests of analysts’ overreaction/underreaction to earnings information as an explanation for anomalous stock price behavior. Journal of Finance, 47(3), pp. 1181–1207. https://doi.org/10.1111/j.1540-6261.1992.tb04010.x.
3. Al Guindy, M. (2017). The role of social media in financial markets. Queen University. Kingston, Canada.
4. Antweiler, W., and Frank, M. Z. (2004). Is all that talk just noise? The information content of Internet stock message boards. Journal of Finance, 59(3), pp. 1259–1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x.
5. Azar, P. D., and Lo, A. W. (2016). The wisdom of Twitter crowds: Predicting stock market reactions to FOMC meetings via Twitter feeds. Journal of Portfolio Management, 42 (5), pp.123–134.
6. Bartov, E., Faurel, L., and Mohanram, P. S. (2018). Can Twitter Help Predict Firm-Level Earnings and Share Returns? Journal of Accounting Review, 93(3), pp. 25-57. http://dx.doi.org/10.2139/ssrn.2782236.
7. Bekhradi Nasab, V., Kamali, E., and Ebrahimi Kahrizsangi, K. (2020). Scheme of Recent Advances in the Field of Accounting and Economics: Application of Macro Accounting Theory in Economic Forecasting. Iranian Journal of Accounting, Auditing and Finance, 4(1), pp. 79-97. doi: 10.22067/ijaaf.2020.39260. (In Persian).
8. Bekhradi Nasab, V., Kamali, E., and Ebrahimi kahrizsangi, K. (2022). A VAR Model for the Macroeconomic Indicators Restatements Predicting : Introduction to Macroaccounting Theory. Advances in Mathematical Finance and Applications, 7(4), pp. 1099-1112. doi: 10.22034/amfa.2021.1921151.1550. (In Persian).
9. Berg, J., Forsythe, R., Nelson, F., and Rietz, T. (2008). Results from a dozen years of election futures markets research. Handbook of Experimental Economic Results, 1, pp. 742–751. https://doi.org/10.1016/S1574-0722(07)00080-7.
10. Blankespoor, E., Miller, G. S., and White, H. D. (2014). The role of dissemination in market liquidity: Evidence from firms’ use of Twitter™. The Accounting Review, 89(1), pp. 79–112. https://doi.org/10.2308/accr-50576
11. Bloomberg. (2013). Netflix CEO Hastings Faces SEC Action over Facebook Post. Available at: http://www. bloomberg.com/news/2012-12-06/netflix-CEO-hastings-faces-sec-action-over-Facebook-post.html
12. Bollen, J., Mao, H., and Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2 (1), pp. 1–8. https://doi. org/10.1016/j.jocs.2010.12.007.
13. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), pp.57–82. https://doi.org/10.2307/2329556.
14. Chen, H., De, P., Hu, Y., and Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The review of financial studies, 27(5), pp. 1367-1403. https://doi.org/10.1093/rfs/hhu001.
15. Curtis, A., Richardson, V. J., and Schmardebeck, R. (2014). Investor attention and the pricing of earnings news. Handbook of Sentiment Analysis in Finance, Forthcoming. London, U.K, http://dx.doi.org/10.2139/ssrn.2467243.
16. Da, Z., J. Engelberg, and P. Gao. (2011). In search of attention, Journal of Finance, 66, pp.1461–1499.
17. Drake, M. S., Roulstone, D. T., & Thornock, J. R. (2012). Investor information demand: Evidence from Google searches around earnings announcements, Journal of Accounting Research, 50, pp. 1001– 1040.
18. Hirschey, M., V. Richardson, and S. Scholz. (2000). Stock price effects of Internet buy-sell recommendations: The Motley Fool case, Financial Review, 35, pp. 147–174.
19. Hong, H., J. Kubik, and A. Solomon. (2000). Security analysts’ career concerns and herding of earnings forecasts, Rand Journal of Economics, 31, pp. 121–144.
20. Hong, L., and S. Page. (2004). Groups of diverse problem solvers can outperform groups of highability problem solvers, Proceedings of the National Academy of Science, 101(46), pp. 16385– 16389.
21. Jame, R., Johnston, R., Markov, S., and M. Wolfe. (2016). The value of crowdsourced earnings forecasts, Journal of Accounting Research, 54 (4), pp. 1077-1110.
22. Jegadeesh, N., and Kim, W. (2010). Do analysts herd? An analysis of recommendations and market reactions. Review of Financial Studies, 23 (2), pp. 901–937. https://doi.org.10.1093.rfs.hhp093.
23. Jung, M. J., Naughton, J. P., Tahoun, A., and Wang, C. (2018). Do firms strategically disseminate? Evidence from corporate use of social media. The Accounting Review, 93(4), pp.225-252. https://doi.org/10.2308/accr-51906.
24. Lee, F., A. Hutton, and S. Shu. (2015). The role of social media in the capital market: Evidence from consumer product recalls, Journal of Accounting Research, 53 (2), pp. 367–404.
25. Loughran, T., and B. McDonald. (2011). When is a liability not a liability? Textual Analysis, Dictionaries, Journal of Finance, 66, pp. 35–65.
26. Mao, Y., Wei, W., Wang, B., and Liu, B. (2012). Correlating S&P 500 stocks with Twitter data. In Proceedings of the first ACM international workshop on hot topics on interdisciplinary social networks research, pp. 69-72. https://doi.org/10.1145/2392622.2392634.
27. Miller, G. (2006). The press as a watchdog for accounting fraud. Journal of Accounting Research, 44 (5), pp. 1001-1033. https://doi.org/10.1111/j.1475-679X.2006.00224.x.
28. Moldoveanu, M., and R. Martin. (2009). Diaminds: Decoding the mental habits of successful thinkers. University of Toronto Press.
29. Richins, G., A. Stapleton., T. C. Stratopoulos., and C. Wong. (2017). Big Data Analytics: Opportunity or Threat for the Accounting Profession?. Journal of Information Systems, 31(3),pp. 63-79. https://doi.org/10.2308/isys-51805.
30. Securities and Exchange Commission (SEC). ( 2013). Report of investigation pursuant to section 21(a) of the Securities Exchange Act of 1934: Netflix, Inc., and Reed Hastings. Available at: https://www.SEC.gov.litigation.investreport.34-69279.pdf
31. Stevens, D. E., and Williams, A. W. (2004). Inefficiency in earnings forecasts: Experimental evidence of reactions to positive vs. negative information. Experimental Economics, 7(1), 75-92. https://doi.org.10.1023.A:1026214106025.
32. Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. Anchor Books.
33. Tumarkin, R., and R. Whitelaw. (2001). News or noise? Internet postings and stock prices, Financial Analysts Journal, 57, pp. 41–51.
34. Warren, J. D., Moffitt, K. C., and Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), pp. 397-407. https://doi.org/10.2308/acch-51069
35. Watts, R. L., and Zimmerman, J. L. (1978). Towards a positive theory of the determination of accounting standards. The Accounting Review, 53(1), pp. 112–134.
36. Welch, I. (2000). Herding among security analysts, Journal of Financial Economics, 58, pp. 369–396.
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