IMPACT OF THE LAUNCH OF CHATGPT ON AI TOKEN RETURNS AND INVESTOR PREFERENCES

Öz

This study examines the impact of the launch of ChatGPT on November 30, 2022, on returns from cryptocurrency tokens based on artificial intelligence (AI) and investor behavior dynamics. The study employs Granger causality analysis to examine Bitcoin (BTC), Ethereum (ETH), and four significant AI tokens (FET, GRT, INJ, and NEAR). The empirical analysis encompasses three distinct periods: the full sample, the pre-ChatGPT launch, and the post-ChatGPT launch. Results from the pre-launch period reveal limited causal linkages, predominantly characterized by BTC's unidirectional influence on FET and internal AI token interactions. Post-launch results demonstrate intensified BTC dominance over AI tokens, including bidirectional causality between BTC and GRT and unidirectional causality from BTC to FET and NEAR. Additionally, stronger interconnectedness emerged among AI tokens, particularly bidirectional causality between FET and GRT. The study concludes that the launch of ChatGPT significantly changed the structure of the cryptocurrency market, enhancing BTC’s impact on AI tokens and fostering a greater interdependence among them.

Anahtar Kelimeler

• Artificial intelligence
• ChatGPT
• Bitcoin
• Cryptocurrency
• Granger causality
• Time series analysis

CHATGPT'NİN YAPAY ZEKÂ TOKEN GETİRİLERİ VE YATIRIMCI TERCİHLERİ ÜZERİNDEKİ ETKİSİ

Öz

Bu çalışma, 30 Kasım 2022’de ChatGPT’nin piyasaya sürülmesinin, yapay zekâ (YZ) tabanlı kripto para tokenlerinin getirileri ve yatırımcı davranış dinamikleri üzerindeki etkisini incelemektedir. Çalışma, Bitcoin (BTC), Ethereum (ETH) ve dört önemli YZ tokenini (FET, GRT, INJ ve NEAR) incelemek için Granger nedensellik analizini kullanmaktadır. Analiz, üç farklı dönemi kapsamaktadır: tüm örneklem, ChatGPT lansmanı öncesi ve ChatGPT lansmanı sonrası. Lansman öncesi dönemden elde edilen sonuçlar, sınırlı nedensel bağlantılar ortaya koymaktadır. Bu bağlantılar, ağırlıklı olarak BTC'nin FET'i ve YZ tokenleri arasındaki etkileşimleri üzerindeki tek yönlü etkisiyle karakterize edilmektedir. Lansman sonrası sonuçlar, BTC ve GRT arasındaki çift yönlü nedensellik ve BTC’den FET ve NEAR’a tek yönlü nedensellik dahil olmak üzere, YZ tokenleri üzerinde BTC’nin hakimiyetinin yoğunlaştığını göstermektedir. Ek olarak, YZ tokenleri arasında, özellikle FET ve GRT arasında çift yönlü nedensellik olmak üzere daha güçlü bir karşılıklı bağlantı ortaya çıkmıştır. Çalışma, ChatGPT’nin lansmanının kripto para piyasasının yapısını önemli ölçüde değiştirdiği, BTC’nin YZ tokenleri üzerindeki etkisini artırdığı ve aralarında daha büyük bir karşılıklı bağımlılık oluşturduğu sonucuna varmaktadır.

Anahtar Kelimeler

• Yapay zeka
• ChatGPT
• Bitcoin
• Kripto para birimi
• Granger nedenselliği
• Zaman serisi analizi

Kaynakça

1. Almeida, J., Gaio, C., & Gonçalves, T. C. (2024). Sustainability or artificial intelligence? Returns and volatility connectedness in crypto assets. The Journal of Risk Finance, 26(2), 177-212. https://doi.org/10.1108/jrf-04-2024-0111
2. Almeida, J., & Gonçalves, T. C. (2024). The AI revolution: are crypto markets more efficient after ChatGPT 3?. Finance Research Letters, 66, 105608. https://doi.org/10.1016/j.frl.2024.105608
3. Ante, L., & Demir, E. (2024). The ChatGPT effect on AI-themed cryptocurrencies. Economics and Business Letters, 13(1), 29-38. https://doi.org/10.17811/ebl.13.1.2024.29-38
4. Arslanian, H., & Fischer, F. (2019). The future of finance: The impact of FinTech, AI, and crypto on financial services. Springer.
5. Asteriou, D., & Hall, S. G. (2021). Applied econometrics. Macmillan International Higher Education.
6. Azeema, N., Nawaz, H., Gill, M. A., Khan, M. A., Miraj, J., & Lodhi, K. (2023). Impact of artificial intelligence on financial markets: Possibilities & challenges. Journal of Computing & Biomedical Informatics, 6(01), 287-299. https://doi.org/10.56979/601/2023
7. Ballis, A., & Anastasiou, D. (2023). Testing for herding in artificial intelligence-themed cryptocurrencies following the launch of ChatGPT. The Journal of Financial Data Science, 5(4), 161-171. https://doi.org/10.3905/jfds.2023.1.134
8. Brooks, C. (2014). Introductory econometrics for finance. Cambridge University Press.

9. Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057-1072. https://doi.org/10.2307/1912517
10. Dong, M. M., Stratopoulos, T. C., & Wang, V. X. (2024). A scoping review of ChatGPT research in accounting and finance. International Journal of Accounting Information Systems, 55, 100715. https://doi.org/10.1016/j.accinf.2024.100715
11. Enders, W. (2015). Applied econometric time series. John Wiley & Sons.
12. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 37(3), 424-438. https://doi.org/10.2307/1912791
13. Gujarati, D. N. (2002). Basic econometrics. McGraw-Hill Professional.
14. Keating, J. W. (1990). Identifying VAR models under rational expectations. Journal of Monetary Economics, 25(3), 453-476. https://doi.org/10.1016/0304-3932(90)90063-A
15. Kumar, V., Leone, R. P., & Gaskins, J. N. (1995). Aggregate and disaggregate sector forecasting using consumer confidence measures. International Journal of Forecasting, 11(3), 361-377. https://doi.org/10.1016/0169-2070(95)00594-2
16. Lee, K.F., & Qiufan, C. (2021). AI 2041 Ten Visions for Our Future, Crown Publishing Group.
17. Mafrur, R. (2025). AI‐Based Crypto Tokens: The Illusion of Decentralized AI?. IET Blockchain, 5(1), e70015. https://doi.org/10.1049/blc2.70015
18. Nguyen, K. Q., Nguyen, T. H., & Do, B. L. (2023). Narrative attention and related cryptocurrency returns. Finance Research Letters, 56, 104174. https://doi.org/10.1016/j.frl.2023.104174
19. Pelster, M., & Val, J. (2024). Can ChatGPT assist in picking stocks?. Finance Research Letters, 59, 104786. https://doi.org/10.1016/j.frl.2023.104786
20. Phelan, B. (2025). Everyday AI: simplifying life with artificial intelligence. Independently Published.
21. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335
22. Saggu, A., & Ante, L. (2023). The influence of ChatGPT on artificial intelligence related crypto assets: Evidence from a synthetic control analysis. Finance Research Letters, 55, 103993. https://doi.org/10.1016/j.frl.2023.103993
23. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48(1), 1-48. https://doi.org/10.2307/1912017
24. Le Tran, V., & Leirvik, T. (2019). A simple but powerful measure of market efficiency. Finance Research Letters, 29, 141-151. https://doi.org/10.1016/j.frl.2019.03.004
25. Vidal-Tomás, D., & Bartolucci, S. (2023). Artificial intelligence and digital economy: divergent realities. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4589333
26. Wang, J. N., Liu, H. C., & Hsu, Y. T. (2025). Do AI incidents and hazards matter for AI-themed cryptocurrency returns?. Finance Research Letters, 74, 106777. https://doi.org/10.1016/j.frl.2025.106777