Do Twitter Sentiments Really Effective on Energy Stocks? Evidence from Intercompany Dependency

Yılmaz, Emrah Sıtkı and Ozpolat, Aslı and Destek, Mehmet Akif (2022): Do Twitter Sentiments Really Effective on Energy Stocks? Evidence from Intercompany Dependency. Published in: Environmental Science and Pollution Research (14 June 2022)

Abstract

The study aims to examine the effects of social media activities on stock prices of the energy sector. In this respect, the sample covers the monthly period from 2015m6 to 2020m5 has been observed. Energy stocks as S&P 500 index (SP), stock market volatility index (VIX), trade-weighted USD index (USD) and Brent oil prices (OIL) have been used as independent variables. Accordingly, three different models have been created to analyze the link between returns, volatility and trading volume and Twitter sentiments by using Augment mean Group. As a result, we found that Twitter sentiment values have no significant impact on the returns and volatility of the companies. Tweets, on the other hand, appear to have a favorable impact on company trading volume values.

• Item Type: MPRA Paper
• Language: English
• Keywords: Social media; Twitter; Energy Sector; Stock Prices
• Subjects: G - Financial Economics > G0 - General; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy

References

Afkhami, M., Cormack, L. and Ghoddusi, H. (2017). Google search keywords that best predict energy price volatility, Energy Economics 67 (2017) 17–27.

Aman, H. (2013). An analysis of the impact of media coverage on stock price crashes and jumps: Evidence from Japan. Pacific-Basin Finance Journal 24 (2013) 22–38.

Bakar, A.B., Siganos, A., Vagenas-Nanos, E., (2014). Does mood explain the Monday effect? J. Forecast. 33, 409–418.

Bartov, E., et al., (2018). Can twitter help predict firm-level earnings and stock returns? Account. Rev. 93, 25–57.

Bollen, J., Mao, H., Zeng, X., (2011). Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8. Bond, S., Eberhardt,M., (2013). Accounting for Unobserved Heterogeneity in Panel Time Series Models. Nuffield College, University of Oxford, Mimeo

Breusch, T.S., Pagan, A.R., (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud. 47, 239–253.

Chan, W.S., (2003). Stock price reaction to news and no-news: drift and reversal after headlines. Journal of Financial Economics 70, 223–260.

Da Z, Engelberg J, Gao P. (2015). The Sum of All FEARS Investor Sentiment and Asset Prices. Review of Financial Studies. 2015;28(1):1-32.

Diebold, F.X., Yilmaz, K., (2014). On the network topology of variance decompositions: measuring the connectedness of financial firms. J. Econ. 182, 119–134.

Eberhardt, M., Bond, S., (2009). Cross-Section Dependence in Nonstationary Panel Models: a Novel Estimator.

Fang, L. and Peress, J., (2009). Media coverage and the cross-section of stock returns. Journal of Finance 64, 2023–2052.

Guo, J.-F., Ji, Q., (2013). How does market concern derived from the internet affect oil prices? Appl. Energy 112, 1536–1543.

Hamraoui, I. ve Boubaker, A. (2022). Impact of Twitter sentiment on stock price returns. Social Network Analysis and Mining, 2022 12:28.

Han, L., Lv, Q., Yin, L., (2017). Can investor attention predict oil prices? Energy Econ. 66, 547–558. Hirshleifer D, Shumway T (2003) Good day sunshine: stock returns and the weather. J Financ 58(3):1009–1032.

Hossain, M. M., Mammadov, B., Vakilzadeh, H. (2021). Wisdom of the crowd and stock price crash risk: evidence from social media. Review of Quantitative Finance and Accounting.

Huberman, G. and Regev, T., (2001). Contagious speculation and a cure for cancer: a nonevent that made stock prices soar. Journal of Finance 56, 387–396.

Jin L, Myers SC (2006) R2 around the world: New theory and new tests. J Financ Econ 79(2):257–292.

Jung, M.J., Naughton, J.P., Tahoun, A., Wang, C., (2017). Do firms strategically disseminate? Evidence from corporate use of social media. Account. Rev. 1–57 In-Press.

Kaplanski G, Levy H. (2010). Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics. 95(2):174-201.

Karabulut, Y. (2013). Can facebook predict stock market activity. Working paper.

Kim S-H, Kim D. Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior & Organization. 2014;107, Part B(0):708-29.

Lazzini, A. , Lazzini, S., Balluchi, F. and Mazza, M. (2021). Emotions, moods and hyperreality: social media and the stock market during the first phase of COVID-19 pandemic. Accounting, Auditing &Accountability Journal, 35(1): 199-215.

Li, X., et al., (2017). Daily happiness and stock returns: the case of Chinese company listed in the United States. Econ. Model. 64, 496–501.

López-Cabarcos MÁ, Piñeiro-Chousa J, Pérez-Pico AM. The impact technical and non-technical investors have on the stock market: Evidence from the sentiment extracted from social networks. Journal of Behavioral and Experimental Finance. 2017;15:15-20.

Majumdar, A. and Bose, I. (2019). Do tweets create value? A multi-period analysis of Twitter use and content of tweets for manufacturing firms. International Journal of Production Economics 216 (2019) 1–11.

Mao, Y., Wei, W., Wang, B., Liu, B., (2012). Correlating S&P 500 stocks with Twitter data. Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks, 69–72.

Meinusch, A., Tillmann, P., (2015). Quantitative easing and tapering uncertainty: evidence from Twitter. Joint Discussion Paper Series in Economics by the Universities of Aachen, Gießen, Göttingen, Kassel, Marburg and Siegen. No. 09-2015.

Miller, G.S., (2006). The press as a watchdog for accounting fraud. Journal of Accounting Research 44, 1001–1033.

Mitchel RLC, Philipps LH (2007) The psychological, neurochemical and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions. Neuropsychol, 45:617–629.

Mittal, A. and Goel, A., (2012). Stock prediction using Twitter sentiment analysis. Working Paper. Stanford University.

Mogaji, E., & Erkan, I. (2019). Insight into consumer experience on UK train transportation services. Travel Behaviour and Society, 14, 21-33.

Mogaji, E., Balakrishnan, J., & Kieu, T. A. (2021). Examining consumer behaviour in the UK Energy sector through the sentimental and thematic analysis of tweets. Journal of Consumer Behaviour, 20(2), 218-230.

Nofer, M. and Hinz, O., (2015). Using Twitter to predict the stock market. Bus. Inf. Syst. Eng. 57 (4), 229–242.

Pesaran, M.H., (2004). General Diagnostic Tests for Cross Section Dependence in Panels.

Rao, T. and Srivastava, S., (2013). Modeling movements in oil, gold, forex and market indices using search volume index and twitter sentiments. Proceedings of the 5th Annual ACM Web Science Conference. ACM., pp. 336–345.

Rakowski D, Shirley S E, Stark J R (2020) Twitter activity, investor attention, and the difusion of information. Financial Management: 1–44.

Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., Mozetič, I., 2015. The returns of Twitter sentiment on stock price returns. PLoS One 10 (9), 1–21.

Reboredo, J. C. and Ugolini, A. (2018). The impact of Twitter sentiment on renewable energy stocks. Energy Economics 76 (2018) 153–169.

Saunders EM (1993) Stock prices and Wall Street weather. Am Econ Rev 83(5):1337–1345 Schaupp, L.C., Bélanger, F., (2014). The value of social media for small businesses. J. Inf. Syst. 28 (1), 187–207.

Siamagka, T.N., Christodoulides, G. Michaelidou, N. and Valvi, A. (2015). Determinants of social media adoption by B2B organizations. Industrial Marketing Management 51 (2015) 89–99.

Siganos, A., Vagenas-Nanos, E., Verwijmeren, P., (2017). Divergence of sentiment and stock market trading. J. Bank. Finance. 78, 130–141.

Sun, Y., Liu, X., Chen, G., Hao, Y., & Zhang, Z.J. (2020). How mood affects the stock market: Empirical evidence from microblogs. Inf. Manag., 57, 103181.

Siikanen, M., et al., (2018). Facebook drives behavior of passive households in stock markets. Finance Res. Lett. 27, 208–213.

Sprenger, O. T.i Tumasjan, A. Sandner, G.P. and Welpe M. I. (2014). Tweets and trades: the information content on stock microblogs. European Financial Management, 20(5): 926:957.

Tajvidi, R., Karami, A., (2017). The effect of social media on firm performance. Comput. Hum. Behav. 1–10.

Tetlock, P.C. (2007). Giving content to investor sentiment: the role of media in the stock market. Journal of Finance 62, 1139–1168.

Tetlock, P.C., Saar-Tsechansky, M. and Macskassy, S., (2008). More than words: quantifying language to measure firms’ fundamentals. Journal of Finance 63, 1437–1467.

Textblob, 2019. Tutorial: Quickstart. Retrieved December 1, 2019, from https://textblob.readthedocs.io/en/dev/quickstart.html

Tonghui, Z. Ying, Y. and Xi, Wu (2020). Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo. Finance Research Letters, 32 (2020) 101173.

Velichety , S. and Shrivastava, U. (2022). Quantifying the impacts of online fake news on the equity value of social media platforms – Evidence from Twitter. International Journal of Information Management, 64, 102474.

Vozlyublennaia, N. (2014). Investor attencion, index performance and return predictability. Journal of Banking and Finance, 41(2014): 17-25.

Wang, Z. and Kim, G.H. (2017). Can Social Media Marketing Improve Customer Relationship Capabilities and Firm Performance? Dynamic Capability Perspective. Journal of Interactive Marketing 39 (2017) 15–26.

Wann D, Dolan T, Mcgeorge K, Allison J (1994) Relationships between spectator identification and spectators’ perceptions of influence, spectators’ emotions, and competition outcome. J Sport Exerc Psychol 16(4):347–364.

Yahoo Finance (2020). Historical data. Retrieved from https://finance.yahoo.com/screener/predefined/ms_energy/. 10.12.2020.

You, W.H., et al., (2017). Twitter's daily happiness sentiment and the predictability of stock returns. Finance Res. Lett. 23, 58–64.

Zaman, S., Yaqub, U. and Saleem, T. (2022). Analysis of Bitcoin’s price spike in context of Elon Musk’s Twitter activity. Global Knowledge, Memory and Communication. Retrieved from https://www.emerald.com/insight/content/doi/10.1108/GKMC-09-2021-0154/full/html.

Zang, W, Wang, P., Xiao, L. and Shen, D. (2018). Twitter’s daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests. Journal of Behavioral and Experimental Finance, 18(2018), 50-53.

Zu, X., Diao, X. and Meng, Z. (2019). The impact of social media input intensity on firm performance: Evidence from Sina Weibo. Physica A 536 (2019) 122556.