Abstract
The main purpose of this study is to investigate the advantages and disadvantages of using ChatGPT and Google Bard in finance. As a method, academic studies and researches done so far were examined. In this direction, the advantages and disadvantages of both methods were tried to be presented in tables. As a result of the study, it was observed that special abilities can be attributed to both artificial intelligence systems, and when compared, it was found that the net advantage would be better understood over time. Because artificial intelligence systems are fed by the feedback of users. However, it has been observed that while ChatGPT works effectively in data analysis and rapid decision making, it may have limitations when it comes to freedom from emotion and bias. Google Bard, on the other hand, has been stated to be effective in risk management and portfolio optimization with its advanced forecasting and analysis capabilities. However, it is common opinion that the use of up-to-date and accurate data is important in both systems and that the factors will affect the performance of the methods.
Keywords
References
- 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.
- Bard (2023). Google Bard: Advancing Natural Language Processing. https://bard.google.com/faq
- 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.
- 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.
- D'Cruz, M., Valsan, R., & Soman, K. P. (2022). Finance Sentiment Analysis using Transformers. arXiv preprint arXiv:2201.07053.
- Dick, S. (2019). Artificial intelligence. Harvard Data Science Review, 1(1), 1-8. doi: 10.1162/99608f92.92fe150c
- Florıdı, L., Chırıattı, M. (2020), Gpt-3.5: Its Nature, Scope, Limits, And Consequences. Minds & Machines, 30, 681–694.
- Gomber, P., Arndt, B., Lutat, M., & Uhle, T. (2019). The Impact of Artificial Intelligence on the Finance Industry. Journal of Financial Perspectives, 2(2), 27-37.
- Gürsoy, S. ve Doğan, M. (2023). ChatGPT'nin Finansal Piyasalarda Kullanımının Swot Analizi ile İncelenmesi. TroyAcademy, 8 (3), 296-305 . DOI: 10.31454/troyacademy.1363366
- Hasanhodzic, J. (2022). Algorithmic Trading: Winning Strategies and Their Rationale. Wiley.
- Johnson, L. M., Smith, J. K., & Lee, H. (2023). Advancements in Natural Language Processing: The Development of Google Bard. Proceedings of the International Conference on Machine Learning, 112-120.
- McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174-1182. https://doi.org/10.1016/j.artint.2007.10.009
- Narang, R. (2019). Inside the Black Box: A Simple Guide to Quantitative and High-Frequency Trading. John Wiley & Sons.
- Pallathadka, H., Ramirez-Asis, E. H., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2023). Applications of artificial intelligence in business management, e-commerce and finance. Materials Today: Proceedings, 80, 2610-2613.
- Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
- Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.
- Smith, J. K., & Johnson, L. M. (2022). Google Bard: A Novel Language Model and Translation System. Journal of Artificial Intelligence Research, 35(2), 215-230.
- Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37.
- Zhao, W., Ye, Y., Liu, S., & Zhang, L. (2021). Applications of Natural Language Processing in Finance: A Comprehensive Survey. Finance Research Letters, 39, 101835.
Abstract
Bu çalışmanın temel amacı ChatGPT ve Google Bard finans alanında kullanılması üstünlükler ve yetersizliklerin neler olduğu araştırılmıştır. Yöntem olarak bugüne kadar yapılan akademik çalışmalar ve araştırmalar incelenmiştir. Bu doğrultuda her iki yöntemin üstünlükleri ve yetersizlikleri tablolar halinde sunulmaya çalışılmıştır. Çalışmanın sonucunda iki yapay zekâ sistemine de özel yetenekler atfedilebileceği gözlemlendi ve karşılaştırıldığında ise net üstünlüğün zamanla daha iyi anlaşılacağı yönünde bulgulara erişildi. Çünkü yapay zeka sistemleri kullanıcıların geri dönüşlerinden beslenmektedir. Bununla birlikte ChatGPT, veri analizi ve hızlı karar alma konusunda etkili bir şekilde çalışırken, duygu ve önyargılardan arınma konusunda sınırlamalara sahip olabileceği vurgulanmıştır. Google Bard ise gelişmiş tahmin ve analiz yetenekleri ile risk yönetimi ve portföy optimizasyonunda etkili olabileceği belirtilmiştir. Ancak, her iki sistemde de güncel ve doğru veri kullanımı önemli olduğu ve faktörlerin yöntemlerin performansı etki olacağı ortak görüşü hakimdir.
Keywords
References
- 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.
- Bard (2023). Google Bard: Advancing Natural Language Processing. https://bard.google.com/faq
- 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.
- 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.
- D'Cruz, M., Valsan, R., & Soman, K. P. (2022). Finance Sentiment Analysis using Transformers. arXiv preprint arXiv:2201.07053.
- Dick, S. (2019). Artificial intelligence. Harvard Data Science Review, 1(1), 1-8. doi: 10.1162/99608f92.92fe150c
- Florıdı, L., Chırıattı, M. (2020), Gpt-3.5: Its Nature, Scope, Limits, And Consequences. Minds & Machines, 30, 681–694.
- Gomber, P., Arndt, B., Lutat, M., & Uhle, T. (2019). The Impact of Artificial Intelligence on the Finance Industry. Journal of Financial Perspectives, 2(2), 27-37.
- Gürsoy, S. ve Doğan, M. (2023). ChatGPT'nin Finansal Piyasalarda Kullanımının Swot Analizi ile İncelenmesi. TroyAcademy, 8 (3), 296-305 . DOI: 10.31454/troyacademy.1363366
- Hasanhodzic, J. (2022). Algorithmic Trading: Winning Strategies and Their Rationale. Wiley.
- Johnson, L. M., Smith, J. K., & Lee, H. (2023). Advancements in Natural Language Processing: The Development of Google Bard. Proceedings of the International Conference on Machine Learning, 112-120.
- McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171(18), 1174-1182. https://doi.org/10.1016/j.artint.2007.10.009
- Narang, R. (2019). Inside the Black Box: A Simple Guide to Quantitative and High-Frequency Trading. John Wiley & Sons.
- Pallathadka, H., Ramirez-Asis, E. H., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2023). Applications of artificial intelligence in business management, e-commerce and finance. Materials Today: Proceedings, 80, 2610-2613.
- Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
- Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson.
- Smith, J. K., & Johnson, L. M. (2022). Google Bard: A Novel Language Model and Translation System. Journal of Artificial Intelligence Research, 35(2), 215-230.
- Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1-37.
- Zhao, W., Ye, Y., Liu, S., & Zhang, L. (2021). Applications of Natural Language Processing in Finance: A Comprehensive Survey. Finance Research Letters, 39, 101835.
Details
| Primary Language | English |
|---|---|
| Subjects | Finance |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | November 30, 2023 |
| Publication Date | November 30, 2023 |
| Submission Date | August 15, 2023 |
| Acceptance Date | November 3, 2023 |
| Published in Issue | Year 2023 Volume: 6 Issue: 2 |
The Journal of Social Sciences Academy
Sosyal Bilimler Akademi Dergisi
(SOBAD)