پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 126 |
| تعداد شمارهها | 7,106 |
| تعداد مقالات | 76,327 |
| تعداد مشاهده مقاله | 152,168,588 |
| تعداد دریافت فایل اصل مقاله | 114,066,154 |
The Impact of Tweet Risks on Global Financial Markets Using the Quantile VAR Model | ||
| Iranian Economic Review | ||
| مقاله 3، دوره 29، شماره 4، اسفند 2025، صفحه 1234-1255 اصل مقاله (1.07 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ier.2024.366270.1007822 | ||
| نویسندگان | ||
| Naeim Shokri* 1؛ Samad Gholami2 | ||
| 1Department of Health Economics, Faculty of Health, Baqiyatallah University of Medical Sciences, Tehran, Iran. | ||
| 2Department of Economics, Faculty of Management & Economics, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده | ||
| This study examines how tweets influence the emotions of investors and how these emotions impact global financial markets. The study applies the Quantile VAR model, a method that captures the dynamics of different quantiles of the conditional distribution, to analyze the data of weekly returns for five financial variables: the U.S. dollar, gold, oil, NASDAQ index, and S&P index, from 2019 to 2023. The study also constructs weighted indices of emotions based on tweets from the US and England, using a sentiment analysis tool. The results reveal a high correlation (95%) between the emotions and the average conditional distribution, indicating that emotions have a significant effect on financial markets. The study also finds that gold and the U.S. dollar are the most vulnerable to emotional shocks, especially during the peak of the COVID-19 pandemic, when fear and uncertainty were widespread. The study emphasizes the importance of addressing the direct impact of emotions on financial markets and urges policymakers and legislators to implement regulations that can protect investors and ensure market stability. | ||
| کلیدواژهها | ||
| Financial Markets؛ Investor Emotions؛ Quantile VAR؛ Risks؛ Tweets | ||
| مراجع | ||
|
Al-Nasseri, A., Ali, F. M., & Tucker, A. (2021). Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors. International Review of Financial Analysis, 78, 1-25. Retrieved from https://www.sciencedirect.com/science/article/pii/S1057521921002362.
Alomari, M., Al Rababa’a, A. R., El-Nader, G., Alkhataybeh, A., & Rehman, M. U. (2021). Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK. The Quarterly Review of Economics and Finance, 82, 280-297. Retrieved from https://www.sciencedirect.com/science/article/pii/S1062976921001599.
Ando, T., Greenwood-Nimmo, M., & Shin, Y. (2022). Quantile connectedness: modeling tail behavior in the topology of financial networks. Management Science, 68(4), 2401-2431. Retrieved from https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2021.3984.
Atkins, A., Niranjan, M., & Gerding, E. (2018). Financial news predicts stock market volatility better than the closing price. The Journal of Finance and Data Science, 4(2), 120-137. Retrieved from https://www.sciencedirect.com/science/article/pii/S240591881730048X.
Blankespoor, E., Miller, G. S., & White, H. D. (2014). The role of dissemination in market liquidity: Evidence from firms' use of Twitter™. The Accounting Review, 89(1), 79-112. Retrieved from https://publications.aaahq.org/accounting-review/article-abstract/89/1/79/3645.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. Retrieved from https://www.sciencedirect.com/science/article/pii/S187775031100007X.
Bouri, E., Saeed, T., Vo, X. V., & Roubaud, D. (2021). Quantile connectedness in the cryptocurrency market. Journal of International Financial Markets, Institutions and Money, 71, 42-65. Retrieved from https://www.sciencedirect.com/science/article/pii/S1042443121000214.
Checkley, M. S., Higón, D. A., & Alles, H. (2017). The hasty wisdom of the mob: How market sentiment predicts stock market behavior. Expert Systems with Applications, 77, 256-263. Retrieved from https://www.sciencedirect.com/science/article/pii/S0957417417300398
Corea, F. (2016). Can Twitter proxy the investors' sentiment? The case for the technology sector. Big Data Research, 4, 70-74. Retrieved from https://www.sciencedirect.com/science/article/pii/S2214579615300174.
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66. Retrieved from https://www.sciencedirect.com/science/article/pii/S016920701100032X.
Dorouzi, E., & Shokri, N. (2020). Effect of Bitcoin on the Discipline of International Law. The International Conference on Humanities and Law, Retrieved from https://civilica.com/doc/718340/.
Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74-81. Retrieved from https://dl.acm.org/doi/fullHtml/10.1145/2602574.
García, D. (2013). Sentiment during recessions. The Journal of Finance, 68(3), 1267-1300. Retrieved from https://onlinelibrary.wiley.com/share/JYBX79QQW6DE9YKJNJNI?target=10.1111/jofi.12027.
Garcia, D., & Schweitzer, F. (2015). Social signals and algorithmic trading of Bitcoin. Royal Society Open Science, 2(9), 20-46. Retrieved from https://royalsocietypublishing.org/doi/abs/10.1098/rsos.150288.
Groß-Klußmann, A., König, S., & Ebner, M. (2019). Buzzwords build momentum: Global financial Twitter sentiment and the aggregate stock market. Expert Systems with Applications, 136, 171-186. Retrieved from https://www.sciencedirect.com/science/article/pii/S0957417419304270.
Guo, F., Lyu, B., Lyu, X., & Zheng, J. (2025). Social Media Networks and Stock Price Synchronicity: Evidence from a Chinese Stock Forum. Abacus, 61(2), 419-461. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1111/abac.12341.
Hajilo Moghadam, A., Shokri, N., & Zisti, S. (2023). Investigating the Spillover Effects of Selected Digital Currencies with an Emphasis on Their Impact on Gold in the Era Before and After the Covid-19 Pandemic and the Role of the Government (Applying the Wavelet Coherence Approach). Public Sector Economics Studies, 2(2), 115-134. Retrieved from https://pse.razi.ac.ir/article_2675.html?lang=en.
Karampatsas, N., Malekpour, S., Mason, A., & Mavis, C. P. (2023). Twitter investor sentiment and corporate earnings announcements. European Financial Management, 29(3), 953-986. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1111/eufm.12384.
Kranefuss, E., & Johnson, D. K. (2021). Does Twitter Strengthen Volatility Forecasts? Evidence from the S&P 500, DJIA, and Twitter Sentiment Analysis. Social Science Research Network (SSRN), Colorado College Working Paper, 2021-01, 1-30. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3786251.
Mao, Y., Wei, W., Wang, B., & 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, 69-72. Retrieved from https://dl.acm.org/doi/abs/10.1145/2392622.2392634.
Neri, F., Aliprandi, C., Capeci, F., & Cuadros, M. (2012). Sentiment analysis on social media. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (919-926). Retrieved from https://ieeexplore.ieee.org/abstract/document/6425642/.
Ranco, G., Aleksovski, D., Caldarelli, G., Grčar, M., & Mozetič, I. (2015). The effects of Twitter sentiment on stock price returns. PloS one, 10(9), 1-21. Retrieved from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0138441.
Reboredo, J. C., & Ugolini, A. (2018). The impact of Twitter sentiment on renewable energy stocks. Energy Economics, 76(C), 153-169. Retrieved from https://www.sciencedirect.com/science/article/pii/S014098831830416X.
Shokri, N., Sahab Khodamoradi, M., and Hajiloo Moghadam, A. H. (2021). Investigating the effects of financial volatility spillover between digital currencies (application of multivariate GARCH approach). Financial Management Perspective, 11(35), 143-172. Retrieved from https://jfmp.sbu.ac.ir/article_102259.html?lang=en.
Shokri, N., & Roshanfekr, A. (2023). Investigating the spillover effects of Bitcoin's financial fluctuations on other digital currencies. International Journal of Blockchains and Cryptocurrencies, 4(1), 65-79. Retrieved from https://www.inderscienceonline.com/doi/abs/10.1504/IJBC.2023.131643.
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957. Retrieved from https://onlinelibrary.wiley.com/share/UUGNC78G44MHXBFZQ3RI?target=10.1111/j.1468-036X.2013.12007.x.
Sul, H. K., Dennis, A. R., & Yuan, L. (2017). Trading on Twitter: Using social media sentiment to predict stock returns. Decision Sciences, 48(3), 454-488. Retrieved from https://onlinelibrary.wiley.com/share/SVAHWSE6PIGINNVGXAER?target=10.1111/deci.12229.
Tetlock, P. C., Saar‐Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms' fundamentals. The Journal of Finance, 63(3), 1437-1467. Retrieved from https://onlinelibrary.wiley.com/share/8YVNP3IEGRQ6KZTU7IPT?target=10.1111/j.1540-6261.2008.01362.x.
Timmermann, A. (2008). Elusive return predictability. International Journal of Forecasting, 24(1), 1-18. Retrieved from https://www.sciencedirect.com/science/article/pii/S0169207007000969.
William, P., Shrivastava, A., Chauhan, P. S., Raja, M., Ojha, S. B., & Kumar, K. (2023). Natural Language processing implementation for sentiment analysis on tweets. Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022 (317-327). Singapore: Springer Nature Singapore. Retrieved from https://link.springer.com/chapter/10.1007/978-981-19-7982-8_26.
Zeitun, R., Rehman, M. U., Ahmad, N., & Vo, X. V. (2023). The impact of Twitter-based sentiment on US sectoral returns. The North American Journal of Economics and Finance, 64, 1-16. Retrieved from https://www.sciencedirect.com/science/article/pii/S1062940822001826. | ||
|
آمار تعداد مشاهده مقاله: 532 تعداد دریافت فایل اصل مقاله: 151 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب