Examining The Use of ChatGPT in Financial Markets with Swot Analysis

Year 2023, Volume: 8 Issue: 3, 296 - 305, 31.10.2023

Samet Gürsoy Mesut Doğan

https://doi.org/10.31454/troyacademy.1363366

Cited By: 1

Abstract

ChatGPT is a derivative of the GPT model and is an artificial intelligence technology used in the field of natural language processing. This model is used to generate appropriate responses to the user's text-based input by pre-training large amounts of text. The aim of this study is to examine the advantages, disadvantages, opportunities and threats of using ChatGPT in financial markets by performing a SWOT analysis. As a result of the analysis, it was determined that ChatGPT has potential advantages in financial analysis and decision-making processes. ChatGPT offers fast and direct communication, instant data analysis and personalized investment recommendations. These features can help investors track market movements and create personal investment strategies. At the same time, predicting future price movements by analyzing large amounts of data can ensure effective and efficient use in financial markets.

Keywords

ChatGPT, GPT, Financial Markets, SWOT Analysis

Finansal Piyasalarda ChatGPT Kullanımının Swot Analizi İle İncelenmesi

Abstract

ChatGPT, GPT modelinin bir türevi olup, doğal dil işleme alanında kullanılan bir yapay zeka teknolojisidir. Bu model, büyük miktardaki metni önceden eğiterek kullanıcının metin tabanlı girdilerine uygun yanıtlar oluşturmak için kullanılır. Bu çalışmanın amacı finansal piyasalarda ChatGPT kullanımının avantajları, dezavantajları, fırsatları ve tehditleri SWOT analizi yapılarak incelemektir. Analiz sonucunda ChatGPT'nin finansal analiz ve karar verme süreçlerinde potansiyel avantajlarının olduğu belirlenmiştir. ChatGPT hızlı ve doğrudan iletişim, anlık veri analizi ve kişiselleştirilmiş yatırım önerileri sunar. Bu özellikler yatırımcıların piyasa hareketlerini takip etmelerine ve kişisel yatırım stratejileri oluşturmalarına yardımcı olabilir. Aynı zamanda büyük miktarda verinin analiz edilerek gelecekteki fiyat hareketlerinin tahmin edilebilmesi, finansal piyasalarda etkin ve verimli kullanılmasını da sağlayabilmektedir.

Keywords

ChatGPT, GPT, Finansal Piyasalar, Swot Analizi

References

• Akkus, H. T., Gursoy, S., Dogan, M., & Demir, A. B. (2022). Metaverse and Metaverse Cryptocurrencies (meta coins): Bubbles or Future?. Journal of Economics Finance and Accounting, 9(1), 22-29.
• Ali, H., & Aysan, A. F. (2023). What will ChatGPT Revolutionize in Financial Industry?. Available at SSRN 4403372.
• Ante, L., & Demir, E. (2023). The ChatGPT Effect on AI-themed cryptocurrencies. Available at SSRN 4350557.
• Beerbaum, D. O. (2023). Generative Artificial Intelligence (GAI) with Chat GPT for Accounting–a business case. Available at SSRN 4385651.
• Blomkvist, M., Qiu, Y., & Zhao, Y. (2023). Automation and Stock Prices: The Case of ChatGPT. Available at SSRN.
• Bolukbasi, T., Chang, K. W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in neural information processing systems (pp. 4349-4357).
• Bouri, E., Lucey, B., & Roubaud, D. (2021). The drivers of Bitcoin volatility: A Bayesian-GARCH approach. Finance Research Letters, 38, 101757.
• Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. NeurIPS.
• Cao, Y., & Zhai, J. (2023). Bridging the gap–the impact of ChatGPT on financial research. Journal of Chinese Economic and Business Studies, 1-15.
• Chen, Z., Zheng, L. N., Lu, C., Yuan, J., & Zhu, D. (2023). ChatGPT Informed Graph Neural Network for Stock Movement Prediction. arXiv preprint arXiv:2306.03763.
• Choudhary, N., Bansal, V., Agarwal, V., & Varma, M. (2022). YAFGPT: Yet Another Financial GPT. arXiv preprint arXiv:2202.08212.
• Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
• Fischer, T., Krauss, C., & Treichel, A. (2021). Beyond Black-Box AI: Interpretable Deep Learning Approaches for Bankruptcy Prediction. Decision Support Systems, 146, 113528.
• Gao, Y., Chiang, M., Kim, J., He, X., & Chen, Y. (2020). Reinforcement learning for finance: A review. Quantitative Finance, 20(9), 1449-1470.
• George, A. S., & George, A. H. (2023). A review of ChatGPT AI's impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9-23.
• Holtzman, A., Buys, J., Du, J., Forbes, M., Choi, Y., Zhang, C., ... & Madaan, A. (2021). The curious case of neural text degeneration. arXiv preprint arXiv:2101.00561.
• Jaiswal, P., & Srivastava, S. (2021). An Appraisal of the Limitations of Artificial Intelligence in Predictive Analytics of the Financial Market. In Applications of Computational Intelligence in Smart Technologies (pp. 49-65). Springer, Singapore.
• Jang, J., Kim, J., & Kim, J. H. (2021). Understanding Artificial Intelligence in Financial Services: Opportunities and Threats for Customization. Information Systems Frontiers, 23(3), 605-618.
• Kim, A., Muhn, M., & Nikolaev, V. (2023). Bloated Disclosures: Can ChatGPT Help Investors Process Financial Information?. arXiv preprint arXiv:2306.10224.
• Kumar, V., & Liu, X. (2022). Machine Learning in Financial Services: Applications and Challenges. ACM Transactions on Management Information Systems (TMIS), 12(1), 1-21.
• Li, X., Zhu, X., Ma, Z., Liu, X., & Shah, S. (2023). Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks. arXiv preprint arXiv:2305.05862.
• Lopez-Lira, A., & Tang, Y. (2023). Can chatgpt forecast stock price movements? return predictability and large language models. arXiv preprint arXiv:2304.07619.
• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
• Pew Research Center (2021). AI in Context: The Labor Market and the Future of Work. Retrieved from https://www.pewresearch.org/internet/2021/01/13/ai-in-context-the-labor-market-and-the-future-of-work/ Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
• Raghupathi, W., & Raghupathi, V. (2020). An empirical study of artificial intelligence in finance: Artificial neural network models for predicting stock prices. International Journal of Business, 25(2), 330-357.
• Smith, R., Singh, A. P., VanderPlas, J., Eickenberg, M., Campbell, C., Ho, D., ... & Thite, A. (2021). An artificial intelligence model for automated diagnosis of patient cardiovascular risk. Nature medicine, 27(1), 34-41.
• World Economic Forum (2020). Global Risks Report 2020. Retrieved from https://www.weforum.org/reports/the-global-risks-report-2020
• Xie, Q., Han, W., Lai, Y., Peng, M., & Huang, J. (2023). The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges. arXiv preprint arXiv:2304.05351.
• Zaremba, A., & Demir, E. (2023). ChatGPT: Unlocking the future of NLP in finance. Available at SSRN 4323643.
• Zhang, R., & Zhang, H. (2021). An Empirical Study of the Impact of Artificial Intelligence on Firm Performance: The Mediation Role of Corporate Innovation. IEEE Transactions on Engineering Management.
• Zhang, W., Zhang, J., Su, Z., & Liu, J. (2021). Improving ChatGPT with SpeechGPT. arXiv preprint arXiv:2110.09880.