Social networks have invaded our world and changed the way we communicate. Even the financial world has turned to social media, with Twitter leading the way. As a result, social media is becoming an undeniable tool that affects the stock market. In this study, we consulted the articles published on the facebook page ilboursa of Tunis. The published articles contain an announcement of the news of the movements of the shares of the listed companies by using negative and positive words. Our database is composed by a manual counting of these qualitative verbal information. We used the list of words of the psychological dictionary "Harvard-IV-4". Our research focuses on 26 Tunisian financial companies listed on the Tunis Stock Exchange, over a one-year horizon, from 01 January 2015 until 31 December 2015. We used the GMM (Generalized Method of Moment) on dynamic panel. The generalized method of moments analyzes two models in which five days of delay of the dependent variable appear as explanatory variable. The results of the study are twofold. First, the Tunis stock exchange seems to react positively to positive qualitative information. Second, it reacts negatively to negative qualitative information. Among other things, the impact of stock returns on information is quite important. It is always necessary to master the tool of social networks to disseminate good and relevant qualitative financial information on the financial market.
| Published in | Journal of Finance and Accounting (Volume 11, Issue 3) |
| DOI | 10.11648/j.jfa.20231103.11 |
| Page(s) | 61-66 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Verbal Qualitative Information, Social Media, Positive Information, Negative Information
| [1] | Blume, Marshall E. and Keim, Donald B (2012) Institutional Investors and Stock MarketLiquidity: Trends and Relationships (August 21, 2012). Jacobs Levy Equity Management Center for Quantitative Financial Research Paper. |
| [2] | David Ardia, Keven Bluteau, Kris Boudt (2022): Media abnormal tone, earnings announcements, and the stock market. Journal of Financial Markets. Volume 61, November 2022, 100683. |
| [3] | Du, Hanyu, Hao, Jing He, Feng, Xi, Wenze (2022): Media sentiment and cross-sectional stock returns in the Chinese stock market: Research in International Business and Finance. Volume 60 April 2022 Article number 101590. |
| [4] | G. Ranco et al (2015). The Effects of Twitter Sentiment on Stock Price Returns. DOI: 10.1371/journal.pone.0138441. |
| [5] | J. Bollen, H. Mao and X. Zeng (2011): Twitter mood predicts the stock market. Journal of Computational Science. Volume 2, Issue 1, March 2011, Pages 1-8. |
| [6] | Ji, X., Wang, J. and Yan, Z. (2021), "A stock price prediction method based on deep learning technology", International Journal of Crowd Science, Vol. 5 No. 1, pp. 55-72. https://doi.org/10.1108/IJCS-05-2020-0012. |
| [7] | K. Ahmad (2016): Media-expressed negative tone and firm-level stock returns. Journal of Corporate Finance. Volume 37, April 2016, Pages 152-172. |
| [8] | Karabulut (2013): Can Facebook Predict Stock Market Activity? Frankfurt School of Finance &Management. SSRN: https://ssrn.com/abstract=1919008. |
| [9] | L. Schaupp and F. Belanger (2014): The Value of Social Media for Small Businesses. Journal of Information Systems 28 (1): 187-207. |
| [10] | Lu Yan Yunyuan Wang Changshuai Li Guohao Tang (2022): Media Abnormal Tone and Cross-Section of Stock Returns: Evidence from China. Social Science Research Network. |
| [11] | Mahinda Kumbure, Christoph Lohrmann, Pasi Luukka, Jari Porras (2022): Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications. Volume 197, 1 July 2022, 116659. |
| [12] | Puri et al (2017): Analysis and Visualisation of Geo-Referenced Tweets for RealTime Information Diffusion. Procedia Computer Science 132 (2018) 1138-1146. |
| [13] | Ruochen Lu and Muchao Lu (2021): Stock Trend Prediction Algorithm Based on Deep Recurrent Neural Network. Wireless Communications and Mobile Computing. Volume 2021. https://doi.org/10.1155/2021/5694975. |
| [14] | Siganos et al (2014): Facebook's daily sentiment and international stock markets. Journal of Economic Behavior & Organization 107. DOI: 10.1016/j.jebo.2014.06.004. |
| [15] | Smailović et al (2013): Predictive Sentiment Analysis of Tweets: A Stock Market Application. DOI: 10.1007/978-3-642-39146-0_8. |
| [16] | Zexin Hu, Yiqi Zhao, Matloob Khushi: A Survey of Forex and Stock Price Prediction Using Deep Learning. Appl. Syst. Innov. 2021, 4 (1), 9; https://doi.org/10.3390/asi4010009. |
APA Style
Hana Marzouk, Wyème Ben Mrad Douagi. (2023). Verbal Qualitative Information from Social Networks and Stock Performance of Tunisian Financial Companies. Journal of Finance and Accounting, 11(3), 61-66. https://doi.org/10.11648/j.jfa.20231103.11
ACS Style
Hana Marzouk; Wyème Ben Mrad Douagi. Verbal Qualitative Information from Social Networks and Stock Performance of Tunisian Financial Companies. J. Finance Account. 2023, 11(3), 61-66. doi: 10.11648/j.jfa.20231103.11
AMA Style
Hana Marzouk, Wyème Ben Mrad Douagi. Verbal Qualitative Information from Social Networks and Stock Performance of Tunisian Financial Companies. J Finance Account. 2023;11(3):61-66. doi: 10.11648/j.jfa.20231103.11
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.jfa.20231103.11,
author = {Hana Marzouk and Wyème Ben Mrad Douagi},
title = {Verbal Qualitative Information from Social Networks and Stock Performance of Tunisian Financial Companies},
journal = {Journal of Finance and Accounting},
volume = {11},
number = {3},
pages = {61-66},
doi = {10.11648/j.jfa.20231103.11},
url = {https://doi.org/10.11648/j.jfa.20231103.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jfa.20231103.11},
abstract = {Social networks have invaded our world and changed the way we communicate. Even the financial world has turned to social media, with Twitter leading the way. As a result, social media is becoming an undeniable tool that affects the stock market. In this study, we consulted the articles published on the facebook page ilboursa of Tunis. The published articles contain an announcement of the news of the movements of the shares of the listed companies by using negative and positive words. Our database is composed by a manual counting of these qualitative verbal information. We used the list of words of the psychological dictionary "Harvard-IV-4". Our research focuses on 26 Tunisian financial companies listed on the Tunis Stock Exchange, over a one-year horizon, from 01 January 2015 until 31 December 2015. We used the GMM (Generalized Method of Moment) on dynamic panel. The generalized method of moments analyzes two models in which five days of delay of the dependent variable appear as explanatory variable. The results of the study are twofold. First, the Tunis stock exchange seems to react positively to positive qualitative information. Second, it reacts negatively to negative qualitative information. Among other things, the impact of stock returns on information is quite important. It is always necessary to master the tool of social networks to disseminate good and relevant qualitative financial information on the financial market.},
year = {2023}
}
TY - JOUR T1 - Verbal Qualitative Information from Social Networks and Stock Performance of Tunisian Financial Companies AU - Hana Marzouk AU - Wyème Ben Mrad Douagi Y1 - 2023/05/18 PY - 2023 N1 - https://doi.org/10.11648/j.jfa.20231103.11 DO - 10.11648/j.jfa.20231103.11 T2 - Journal of Finance and Accounting JF - Journal of Finance and Accounting JO - Journal of Finance and Accounting SP - 61 EP - 66 PB - Science Publishing Group SN - 2330-7323 UR - https://doi.org/10.11648/j.jfa.20231103.11 AB - Social networks have invaded our world and changed the way we communicate. Even the financial world has turned to social media, with Twitter leading the way. As a result, social media is becoming an undeniable tool that affects the stock market. In this study, we consulted the articles published on the facebook page ilboursa of Tunis. The published articles contain an announcement of the news of the movements of the shares of the listed companies by using negative and positive words. Our database is composed by a manual counting of these qualitative verbal information. We used the list of words of the psychological dictionary "Harvard-IV-4". Our research focuses on 26 Tunisian financial companies listed on the Tunis Stock Exchange, over a one-year horizon, from 01 January 2015 until 31 December 2015. We used the GMM (Generalized Method of Moment) on dynamic panel. The generalized method of moments analyzes two models in which five days of delay of the dependent variable appear as explanatory variable. The results of the study are twofold. First, the Tunis stock exchange seems to react positively to positive qualitative information. Second, it reacts negatively to negative qualitative information. Among other things, the impact of stock returns on information is quite important. It is always necessary to master the tool of social networks to disseminate good and relevant qualitative financial information on the financial market. VL - 11 IS - 3 ER -
Information
Email: