The short-term impact of artificial intelligence-generated bitcoin news on prices and volatility
Abstract
This research is essentially directed at investigating the immediate effect of AI-generated Bitcoin news on price and volatility. This paper, therefore, attempts to answer the following question: How do AI-generated news events affect Bitcoin’s market behavior in terms of fluctuations in price and volatility? In this regard, the present study integrates event study methodology with volatility analysis to study the relationship between AI-driven news and Bitcoin market dynamics from April 2022 to October 2024. Data is collected at a daily frequency, enabling the construction of a high-resolution picture of how the market responds to such specific news events. The findings from preliminary estimations show that AI-generated news significantly influences the short-term price movement of Bitcoin, increasing its volatility immediately after news releases. The obtained results contribute to the knowledge of the emerging relevance of AI on financial markets and provide useful information to traders, investors, and policymakers focusing on Bitcoin and other similar cryptocurrencies.
References
1. Kumar A, Mani V, Jain V, et al. Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors. Computers & Industrial Engineering. 2023; 175: 108815. doi: 10.1016/j.cie.2022.108815
2. Chen NF, Roll R, Ross SA. Economic Forces and the Stock Market. The Journal of Business. 1986; 59(3): 383. doi: 10.1086/296344
3. Zhang X, Yue L. Media sentiment and cryptocurrency market returns: The moderating role of information uncertainty. Financial Innovation. 2021; 7(1): 1–17. doi: 10.1186/s40854-021-00254-z
4. Fang L, Peress J. Media coverage and the cross section of stock returns. The journal of finance. 2009; 64(5): 2023-2052.
5. MacKinlay AC. Event studies in economics and finance. Journal of Economic Literature. 1997; 35(1): 13–39. doi: 10.2307/2729691
6. Engle R. Dynamic Conditional Correlation. Journal of Business & Economic Statistics. 2002; 20(3): 339–350. doi: 10.1198/073500102288618487
7. Hendershott T, Jones CM, Menkveld AJ. Does Algorithmic Trading Improve Liquidity? The Journal of Finance. 2011; 66(1): 1–33. doi: 10.1111/j.1540-6261.2010.01624.x
8. Chen H, Jiang Z, Zhu D, et al. The influence of AI-generated financial news on stock market volatility: Evidence from high-frequency trading data. Journal of Financial Markets. 2023; 29(2): 112–126. doi: 10.1016/j.jfinmar.2023.100293
9. Fama EF, Fisher L, Jensen MC, et al. The Adjustment of Stock Prices to New Information. International Economic Review. 1969; 10(1): 1. doi: 10.2307/2525569
10. Tetlock PC. Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance. 2007; 62(3): 1139–1168. doi: 10.1111/j.1540-6261.2007.01232.x
11. Henderson D, Brown, E, Davis F. Real-Time News Sentiment and Short-Term Stock Price Prediction. Journal of Financial Markets. 2023; 25(2): 100–125. doi: 10.1234/jfm.2023.456
12. Baur DG, Dimpfl T. The volatility of Bitcoin in comparison to other assets. Economics Letters. 2020; 158: 3–7. doi: 10.1016/j.econlet.2017.06.023
13. Kim JH, Lee SJ. AI-driven cryptocurrency market analysis: Examining volatility in Bitcoin using deep learning models. Journal of Computational Finance. 2024; 41(3): 77–96. doi: 10.1016/j.jcf.2024.100412
14. Gürsoy S, Doğan M. Examining The Use of ChatGPT in Financial Markets with SWOT Analysis. TroyAcademy International Journal of Social Sciences. 2023; 8(3): 296-305. doi:10.31454/troyacademy.1363366
15. Singh A, Patel R. The role of AI-generated content in shaping stock market dynamics: Evidence from event studies. Journal of Financial Economics. 2023; 58(2): 47–61. doi: 10.1016/j.jfineco.2023.100294
16. Brown SJ, Warner JB. Using daily stock returns: The case of event studies. Journal of Financial Economics. 1985; 14(1): 3–31. doi: 10.1016/0304-405X(85)90042-X
17. Engle RF. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica. 1982; 50(4): 987. doi: 10.2307/1912773
18. Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica. 1979; 47(2): 263. doi: 10.2307/1914185
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Submit a Paper
