About the article
DOI: https://www.doi.org/10.15219/em103.1642
The article is in the printed version on pages 86-96.
How to cite
Jankowski, J. i Piotrowski, D. (2024). Media społecznościowe jako źródło wiedzy wykorzystywanej w inwestycjach na rynku finansowym. e-mentor, 1(103), 86-96. https://doi.org/10.15219/em103.1642
E-mentor number 1 (103) / 2024
Table of contents
About the authors
Social media as a source of knowledge used in financial market investments
Jakub Jankowski, Dariusz Piotrowski
Abstract
Social media are a source of an enormous amount of data that can support investment decisions, with the development of digital technology in the field of data processing making the analysis of the content published on sites such as Twitter, Facebook and YouTube an indispensable part of the investment process for many financial market participants. The aim of this study is to identify the applications of social media in financial market investing, as well as undertaking to determine the position of social media among the available sources for obtaining market information. The empirical data used in the analysis was obtained through a survey carried out using the CAWI method.
The results of the survey indicate that social media are an important source of information, especially for respondents with experience in financial market investments, although they are inferior to financial portals in this respect. The varied use of the social media platforms analysed was also recognised. The main advantage of using Twitter was identified as the ability to monitor current trends and follow the profiles of investment experts, for Facebook it was the ability to join investment-themed groups, while YouTube was valued for its access to educational content.
Keywords: financial education, financial investments, behavioural finance, market sentiment analysis, market trend analysis, social networking sites, Twitter, Facebook, YouTube
References
- Aichner, T., Grünfelder, M., Maurer, O. i Jegeni, D. (2021). Twenty-five years of social media: a review of social media applications and definitions from 1994 to 2019. Cyberpsychology, Behavior, and Social Networking, 24(4), 215-222. http://doi.org/10.1089/cyber.2020.0134
- Al-Bahrani, A. i Patel, D. (2015). Incorporating Twitter, Instagram, and Facebook in Economics Classrooms. The Journal of Economic Education, 46(1), 56-67, http://doi.org/10.1080/00220485.2014.978922
- Ante, L. (2023). How Elon Musk's twitter activity moves cryptocurrency markets. Technological Forecasting and Social Change, 186(211), 122112. https://doi.org/10.1016/j.techfore.2022.122112
- Azucar, D., Marengo, D. i Settanni, M. (2018). Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis. Personality and Individual Differences, 124, 150-159. https://doi.org/10.1016/j.paid.2017.12.018
- Boyd, D. M. i Ellison, N. B. (2010). Social network sites: definition, history, and scholarship. IEEE Engineering Management Review, 38(3), 16-31, https://doi.org/10.1109/EMR.2010.5559139
- Bukovina, J. (2016). Social media big data and capital markets - An overview. Journal of Behavioral and Experimental Finance, 11, 18-26. https://doi.org/10.1016/j.jbef.2016.06.002
- Checkley, M., Higón, D. i Alles, H. (2017). The hasty wisdom of the mob: how market sentiment predicts stock market behavior. Expert Systems with Applications, 77, 256-263. https://doi.org/10.1016/j.eswa.2017.01.029
- Chen, H., De, P., Hu, Y. i Hwang, B-H. (2014). Wisdom of crowds: the value of stock opinions transmitted through social media. The Review of Financial Studies, 27(5), 1367-1403. https://doi.org/10.1093/rfs/hhu001
- Chen, W., Yeo, C. K., Lau, C. T. i Lee, B. S. (2018). Leveraging social media news to predict stock index movement using RNN-boost. Data & Knowledge Engineering, 118, 14-24. https://doi.org/10.1016/j.datak.2018.08.003
- Cymanow, P., Cymanow-Sosin, K., Paluch, Ł. i Tenerowicz, K. (2023). Nowe Media. Edukacja, finanse, zarządzanie. Tyniec Wydawnictwo Benedyktynów.
- Da, Z., Engelberg, J. i Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x
- Filatotchev, I., Bell, R. G. i Rasheed, A. A. (2016). Globalization of capital markets: Implications for firm strategies. Journal of International Management, 22(3), 211-221. https://doi.org/10.1016/j.intman.2016.04.001
- Goyal, K. i Kumar, S. (2021). Financial literacy: a systematic review and bibliometric analysis. International Journal of Consumer Studies, 45(1), 80-105. https://doi.org/10.1111/ijcs.12605
- Hinton, S. i Hjorth, L. (2013). Understanding Social Media. SAGE Publications. https://doi.org/10.4135/9781446270189
- Hoffmann, A. i Otteby, K. (2018). Personal finance blogs: helpful tool for consumers with low financial literacy or preaching to the choir? International Journal of Consumer Studies, 42(2), 241-254. https://doi.org/10.1111/ijcs.12412
- Hu, H., Tang, L., Zhang, S. i Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188-195. https://doi.org/10.1016/j.neucom.2018.01.038
- Jena, P. R. i Majhi, R. (2023). Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach. Scientific African, 19, e01480. https://doi.org/10.1016/j.sciaf.2022.e01480
- Kaplan, A. i Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. https://doi.org/10.1016/j.bushor.2009.09.003
- Kisiołek, A. (2018). Analiza wpisów na portalu Twitter z wykorzystaniem narzędzi big data zawartych w pakiecie R. Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 362, 306-317.
- Kogan, S., Moskowitz, T. J. i Niessner, M. (2023). Social media and financial news manipulation. Review of Finance, 27(4), 1229-1268. https://doi.org/10.1093/rof/rfac058
- Kropiński, P. i Anholcer, M. (2022). How Google Trends can improve market predictions - the case of the Warsaw Stock Exchange. Economics and Business Review, 8(2), 7-28. https://doi.org/10.18559/ebr.2022.2.2
- Li, Q., Wang, T., Li, P., Liu, L., Gong, Q. i Chen, Y. (2014). The effect of news and public mood on stock movements. Information Sciences, 278, 826-840. https://doi.org/10.1016/j.ins.2014.03.096
- McGurk, Z., Nowak, A. i Hall, J. C. (2020). Stock returns and investor sentiment: textual analysis and social media. Journal of Economics and Finance, 44, 458-485. https://doi.org/10.1007/s12197-019-09494-4
- Mirtaheri, M., Abu-El-Haija, S., Morstatter, F., Ver Steeg, G. i Galstyan, A. (2021). Identifying and analyzing cryptocurrency manipulations in social media. IEEE Transactions on Computational Social Systems, 8(3), 607-617. https://doi.org/10.1109/TCSS.2021.3059286
- Nguyen, T., Shirai, K. i Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611. https://doi.org/10.1016/j.eswa.2015.07.052
- Paasonen, S. (2018). Affect, data, manipulation and price in social media. Distinktion: Journal of Social Theory, 19(2), 214-229, https://doi.org/10.1080/1600910X.2018.1475289
- Piotrowski, D. (2022). Demographic and socio-economic factors as barriers to robo-advisory acceptance in Poland. Annales Universitatis Mariae Curie-Skłodowska, section H - Oeconomia, 56(3), 109-126. http://dx.doi.org/10.17951/h.2022.56.3.109-126
- Rojszczak, M. (2020). Sztuczna inteligencja w innowacjach finansowych - aspekty prawne i regulacyjne. internetowy Kwartalnik Antymonopolowy i Regulacyjny (iKAR), 2(9), 61-77.
- Sprenger, T., Tumasjan, A., Sandner, P. i Welpe, I. (2014). Tweets and trades: the information content of stock microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12007.x
- Steinert, L. i Herff, C. (2018). Predicting altcoin returns using social media. PLoS ONE, 13(12), e0208119. https://doi.org/10.1371/journal.pone.0208119
- Sul, H. K., Dennis, A. R. i Yuan, L. (2017). Trading on Twitter: using social media sentiment to predict stock returns. Decision Sciences, 48(3), 454-488. https://doi.org/10.1111/deci.12229
- Tan, S. D. i Tas, O. (2021). Social media sentiment in international stock returns and trading activity. Journal of Behavioral Finance, 22(2), 221-234. https://doi.org/10.1080/15427560.2020.1772261
- Waszczeniuk, M. (2022). Wpływ informacji na rynek kapitałowy. W: E. Ignaciuk i M. Szmelter (red.), Społeczna odpowiedzialność nauki - świat po pandemii (s. 47-59). Fundacja Rozwoju Uniwersytetu Gdańskiego.
- Wołk, K. (2020). Advanced social media sentiment analysis for short-term cryptocurrency price prediction. Expert Systems, 37(2), e12493. https://doi.org/10.1111/exsy.12493
- Yan, X. i Zheng, L. (2017). Fundamental analysis and the cross-section of stock returns: a data-mining approach. Review of Financial Studies, 30(4), 1382-1423. https://doi.org/10.1093/rfs/hhx001