1. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
2. Chen M., Tworek J., Jun H., Yuan Q., Pinto, H. P. D. O., Kaplan, J., Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
3. Wahde, M., & Virgolin, M. (2022). Conversational agents: Theory and applications // HANDBOOK ON COMPUTER LEARNING AND INTELLIGENCE: Volume 2: Deep Learning, Intelligent Control and Evolutionary Computation (pp. 497-544).
4. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I. & others (2019). Language models are unsupervised multitask learners. OpenAI blog, 1, 9.
5. Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Le, Q. V. (2021). Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652.
6. Muhammad Salar Khan, Hamza Umer. ChatGPT in finance: Applications, challenges, and solutions, Heliyon, Volume 1 0, Issue 2, 2024.
7. Zhang, Y., Sun, S., Galley, M., Chen, Y. C., Brockett, C., Gao, X., Dolan, B. (2019). Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536.
8. Kolte, G., Kini, V., Nair, H., & Babu, K. S. S. (2022). Stock Market Prediction using Deep Learning. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 10(4).
9. Sheeba, M., Kumar, D. R., Kirubakaran, S., Dixit, I., Leela, D., Ajay, R., Yadav, S. An investigation and development into the Use of AI-based analytical methods for forecasting the stock market. In 2023 IEEE International Conference on Open Source Systems and Technologies (ICOSEC), 2023, P. 1303-1307.
10. Kim, Alex G. and Muhn, Maximilian and Nikolaev, Valeri V. Bloated Disclosures: Can ChatGPT Help Investors Process Information? Chicago Booth Research Paper No. 23-07, University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2023-59, 2024.
11. Alessio, H.M., Malay, N., Maurer, K., Bailer, A.J., Rubin, B. (2018). Interaction of proctoring and student major on online test performance. International Review of Research in Open and Distributed Learning, 19(5).
12. Alshami, A., Elsayed, M., Ali, E., Eltoukhy, A.E., Zayed, T. Harnessing the power of chatgpt for automating systematic review process: Methodology, case study, limitations, and future directions. Systems 11, 2023, P. 351. EDN: ZQTLPF
13. Georgios Fatouros, John Soldatos, Kalliopi Kouroumali, Georgios Makridis, Dimosthenis Kyriazis, Transforming sentiment analysis in the financial domain with ChatGPT, Machine Learning with Applications, Volume 14, 2023.
14. Lopez-Lira, Alejandro and Tang, Yuehua, Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (April 6, 2023).
15. Куприянова И. А., Опря Д. С., Аблякимова К. Р. Применение нейронных сетей в современных реалиях на примере chatgpt // Формирование механизмов устойчивого развития экономики: сборник трудов III всероссийской научно-практической конференции, Севастополь, 01-02 июня 2023 года. Симферополь: Общество с ограниченной ответственностью “Издательство Типография “Ариал”, 2023. - 291 с. EDN: QQWQVF
16. Juuso Liesio, Peng Xu, Timo Kuosmanen. Portfolio diversification based on stochastic dominance under incomplete probability information, European Journal of Operational Research. 2020. Vol. 286. Iss. 2. P. 755-768. EDN: RZODAH
17. Gursoy, Samet & Dogan, Mesut. Examining The Use of ChatGPT in Financial Markets with Swot Analysis. TroyAcademy, Volume 8, 2023, P. 296-305.
