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SCIENCE FICTION TO REAL LIFE: BING AI AS AN INVESTMENT ADVISOR

Year 2023, Volume: 7 Issue: 2, 240 - 260, 31.12.2023
https://doi.org/10.7596/jebm.31122023.003

Abstract

Nowadays, generative artificial intelligence models have extensive applications, including finance. Artificial intelligence models in finance synthesise data to assist analysts in generating financial reports, detecting risks, predicting market trends, and optimising portfolios for managers and investors. However, it is crucial to determine the effectiveness of these financial functions. Therefore, this study aims to explore the financial capabilities of artificial intelligence in the finance domain and evaluate its performance through a case study on investment analysis. Within the scope of the study, a text-based artificial intelligence engine, Bing AI, was utilised to explore the financial capabilities of artificial intelligence. Bing AI was tasked with creating portfolios for companies listed on the BIST100 index based on their financial statements from 2019-2022, according to modern and traditional portfolio theories. The success of artificial intelligence in the financial field was evaluated by calculating the risk and return of the portfolio recommended by Bing AI for the period January 2023–November 2023. The findings indicate that Bing AI has the potential to partially support individuals with basic financial knowledge, but there is a need for further development in the application of finance.

References

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  • Biswas, S. (2023b). Role of ChatGPT in Public Health. Annals of Biomedical Engineering, 51(5), 868-869.
  • Callanan, E., Mbakwe, A., Papadimitriou, A., Pei, Y., Sibue, M., Zhu, X., Ma, Z., Liu, X. & Shah, S. (2023). Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams, doi.org/10.48550/arXiv.2310.08678
  • Cascella, M., Montomoli, J., Bellini, V. & Bignami, E. (2023). Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. Journal of Medical Systems, 47(1), 33. doi.org/10.1007/s10916-023-01925-4
  • Chu, M. N. (2023). Assessing the Benefits of ChatGPT for Business: An Empirical Study on Organizational Performance. IEEE Access. doi.org/10.1109/ACCESS.2023.3297447
  • Derdiyok, T., Unal, S., & Doğru, Ç. (2023). ChatGPT's Ability to Determine Financial Status of Companies. Ufuk University Social Sciences Institute Journal, 12(23), 6-20. doi.org/10.58635/ufuksbedergi.1285729
  • Dowling, M., & Lucey, B. (2023). ChatGPT for (Finance) Research: The Bananarama Conjecture. Finance Research Letters, 53, 103662. doi.org/10.1016/j.frl.2023.103662
  • Forbes, (2023). Artificial Intelligence Applications in Investing, Available at: https://www.forbes.com/sites/qai/2023/02/24/artificial-intelligence-applications-in-investing/?sh=1908d86de216
  • George, A. S., George, A. H. & Martin, A. S. G. (2023). A Review of ChatGPT AI's Impact on Several Business Sectors. Partners Universal International Innovation Journal, 1(1), 9-23. doi.org/10.5281/zenodo.7644359
  • Göktaş, F., & Duran. A. (2019). A New Possibilistic Mean-Variance Model Based on the Principal Components Analysis: An Application on the Turkish Holding Stocks. Journal of Multiple-Valued Logic & Soft Computing, 32(5-6), 455-476.
  • Grassini, S. (2023). Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Education Sciences, 13(7), 692. doi.org/10.3390/educsci13070692
  • Guo, Y., Xu, Z., & Yang, Y. (2023). Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing. Avaliable at: arXiv preprint arXiv:2310.12664
  • Güçlü, F. (2022). Does Islamic Sensitivity Affect Portfolio Performance? A Different Perspective on Islamic Equity Investments. Journal of TESAM Academy, 9(1), 105–128. doi.org/10.30626/tesamakademi.1022807
  • Güçlü, F., & Şekkeli, F. E. (2020). Performance Analysis and Comparison of Islamic and Conventional Stock Mutual Funds in Turkey. Business & Management Studies: An International Journal, 8(5), 4463–4486. doi.org/10.15295/bmij.v8i5.1659
  • Hofert, M. (2023). Assessing ChatGPT’s Proficiency in Quantitative Risk Management. Risks, 11(9), 166. doi.org/10.3390/risks11090166
  • Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A Large Language Model for Extracting Information from Financial Text. Contemporary Accounting Research, 40(2), 806-841. doi.org/10.1111/1911-3846.12832
  • Jurafsky, D., & Martin, J. H. (2014). Speech and Language Processing (3. Edition draft). Available at: https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
  • Krause, D., (2023). Large Language Models and Generative AI in Finance: An Analysis of ChatGPT, Bard, and Bing AI. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4511540
  • Küçüker, M. (2023). Artificial Intelligence Applications in Accounting: ChatGPT's Accounting Exam. Fırat University Journal of Social Sciences, 33(2), 875-888. doi.org/10.18069/firatsbed.1289885
  • Li, J., Dada, A., Kleesiek, J., & Egger, J. (2023). ChatGPT in Healthcare: A Taxonomy and Systematic Review. medRxiv, 2023-03. doi.org/10.1101/2023.03.30.23287899
  • Liu, Z., Huang, D., Huang, K., Li, Z., & Zhao, J. (2021). FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining. Proceedings of the Twenty-Ninth İnternational Conference on International Joint Conferences on Artificial Intelligence, 4513-4519 Available at: https://www.ijcai.org/proceedings/2020/622
  • OpenAI (2019). https://openai.com/research/gpt-2-1-5b-release
  • OpenAI (2021). https://openai.com/blog/gpt-3-apps
  • OpenAI (2023). https://openai.com/gpt-4
  • Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in Education: A Literature Review and Bibliometric Analysis. Cogent Education, 10(2), 2243134. doi.org/10.1080/2331186X.2023.2243134.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-training. Available at: https://www.mikecaptain.com/resources/pdf/GPT-1.pdf
  • Rajpoot, P. K., & Parikh, A. (2023). GPT-FinRE: In-context Learning for Financial Relation Extraction Using Large Language Models. ArXiv Preprint, Available at: https://arxiv.org/pdf/2306.17519.pdf
  • Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample G. (2023). LLaMA: Open and Efficient Foundation Language Models. ArXiv Preprint, arXiv:2302.13971. doi.org/10.48550/arXiv.2302.13971
  • Wang, N., Yang, H., & Wang, C. D. (2023). FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets. ArXiv Preprint, arXiv:2310.04793. doi.org/10.48550/arXiv.2310.04793
  • Wang, Z., Li, Y., Wu, J., Soon, J. & Zhang, X. (2023). FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis. doi.org/10.48550/arXiv.2308.01430
  • Wenzlaff, K. & Spaeth, S. (2022). Smarter than Humans? Validating How OpenAI’s ChatGPT Model Explains Crowdfunding, Alternative Finance and Community Finance. Available at SSRN: https://ssrn.com/abstract=4302443, http://dx.doi.org/10.2139/ssrn.4302443
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  • Yang, H., Liu, X.Y. & Wang, CD (2023). FinGPT: Open-Source Financial Large Language Models. Cornell University. arXiv preprint, arXiv:2306.06031. doi.org/10.48550/arXiv.2306.06031
  • Yang, Y., Tang, Y. & Tam, K.Y. (2023). InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning. Available at: https://www.arxiv-vanity.com/papers/2309.13064/
  • Yang, H., Liu, X.Y. & Wang, C.D. (2023) FinGPT: Opensource Financial Large Language Models. Cornell University. arXiv preprint, arXiv:2306.06031. doi.org/10.48550/arXiv.2306.06031
  • Zhang, L., Cai, W., Liu, Z., Yang, Z., Dai, W., Liao, Y., Qin, Q., Li,Y., Liu, X., Liu, Z., Zhu, Z. Wu, A. Guo, X. & Chen, Y. (2023). FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models. Cornell University. ArXiv Preprint, arXiv:2308.09975. doi.org/10.48550/arXiv.2308.09975

BİLİM KURGUDAN GERÇEK HAYATA: YATIRIM DANIŞMANI OLARAK BING AI

Year 2023, Volume: 7 Issue: 2, 240 - 260, 31.12.2023
https://doi.org/10.7596/jebm.31122023.003

Abstract

Günümüzde üretken yapay zekâ modelleri finansın da dâhil olduğu oldukça geniş bir kullanım alanına sahiptir. Finans alanında yapay zekâ modelleri verileri sentezleyerek analistlere; finansal rapor oluşturmada, risk tespit etmede, piyasa eğilimlerini tahmin etmede ve portföyleri optimize etmede yöneticilere ve yatırımcılara yardımcı olmaktadır. Ancak bütün bu finansal işlevleri ne derece etkin yerine getirdiğini belirlemek oldukça önemlidir. Bu nedenle çalışmada yapay zekânın finansal yeteneklerinin keşfedilmesi, yatırım analizi örnek olay incelemesi yoluyla performansının değerlendirilmesi amaçlanmıştır. Çalışma kapsamında, yapay zekânın finansal yeteneklerinin keşfedilmesi amacıyla, metin tabanlı bir yapay zekâ motoru olan Bing AI kullanılmıştır. Bing AI’dan BİST100 endeksindeki işletmelerin 2019-2022 dönemindeki finansal tablolarını dikkate alarak, modern ve geleneksel portföy teorilerine göre portföyler oluşturması istenmiştir. Bing AI'nın önerdiği portföyün Ocak 2023-Kasım 2023 dönemindeki risk ve getirisi hesaplanarak, yapay zekânın finansal alandaki başarısı değerlendirilmiştir. Elde edilen bulgular, Bing AI’nın finansal alanda temel bilgiye sahip kişilere kısmen de olsa destek olabilecek durumda olduğunu; ancak finansın uygulama alanında geliştirilmesine ihtiyaç duyduğunu göstermektedir.

Ethical Statement

The research falls within the category of studies exempt from ethical committee approval, as it exclusively relies on publicly accessible information and does not involve the collection of data from human subjects.

References

  • Araci, D. T. (2019). FinBERT: Financial Sentiment Analysis with Pre-Trained Language Models. arXiv Preprint, Available at: arXiv:1908.10063.
  • Fatouros, G., Soldatos, J., Kouroumali, K., Makridis, G. & Kyriazi, D. (2023). Transforming Sentiment Analysis in the Financial Domain with ChatGPT, Machine Learning with Applications, 14,100508. doi.org/10.1016/j.mlwa.2023.100508
  • Adeshola, I., & Adepoju, A. P. (2023). The Opportunities and Challenges of ChatGPT in Education. Interactive Learning Environments, 1-14. doi.org/10.1080/10494820.2023.2253858
  • Ahangar, R. G., & Fietko, A. (2023). Exploring the Potential of ChatGPT in Financial Decision Making. In Advancement in Business Analytics Tools for Higher Financial Performance, IGI Global, 94-11.
  • Biswas, S. (2023a). Role of ChatGPT in Education. Available at SSRN: https://ssrn.com/abstract=4369981
  • Biswas, S. (2023b). Role of ChatGPT in Public Health. Annals of Biomedical Engineering, 51(5), 868-869.
  • Callanan, E., Mbakwe, A., Papadimitriou, A., Pei, Y., Sibue, M., Zhu, X., Ma, Z., Liu, X. & Shah, S. (2023). Can GPT Models Be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams, doi.org/10.48550/arXiv.2310.08678
  • Cascella, M., Montomoli, J., Bellini, V. & Bignami, E. (2023). Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. Journal of Medical Systems, 47(1), 33. doi.org/10.1007/s10916-023-01925-4
  • Chu, M. N. (2023). Assessing the Benefits of ChatGPT for Business: An Empirical Study on Organizational Performance. IEEE Access. doi.org/10.1109/ACCESS.2023.3297447
  • Derdiyok, T., Unal, S., & Doğru, Ç. (2023). ChatGPT's Ability to Determine Financial Status of Companies. Ufuk University Social Sciences Institute Journal, 12(23), 6-20. doi.org/10.58635/ufuksbedergi.1285729
  • Dowling, M., & Lucey, B. (2023). ChatGPT for (Finance) Research: The Bananarama Conjecture. Finance Research Letters, 53, 103662. doi.org/10.1016/j.frl.2023.103662
  • Forbes, (2023). Artificial Intelligence Applications in Investing, Available at: https://www.forbes.com/sites/qai/2023/02/24/artificial-intelligence-applications-in-investing/?sh=1908d86de216
  • George, A. S., George, A. H. & Martin, A. S. G. (2023). A Review of ChatGPT AI's Impact on Several Business Sectors. Partners Universal International Innovation Journal, 1(1), 9-23. doi.org/10.5281/zenodo.7644359
  • Göktaş, F., & Duran. A. (2019). A New Possibilistic Mean-Variance Model Based on the Principal Components Analysis: An Application on the Turkish Holding Stocks. Journal of Multiple-Valued Logic & Soft Computing, 32(5-6), 455-476.
  • Grassini, S. (2023). Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational Settings. Education Sciences, 13(7), 692. doi.org/10.3390/educsci13070692
  • Guo, Y., Xu, Z., & Yang, Y. (2023). Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing. Avaliable at: arXiv preprint arXiv:2310.12664
  • Güçlü, F. (2022). Does Islamic Sensitivity Affect Portfolio Performance? A Different Perspective on Islamic Equity Investments. Journal of TESAM Academy, 9(1), 105–128. doi.org/10.30626/tesamakademi.1022807
  • Güçlü, F., & Şekkeli, F. E. (2020). Performance Analysis and Comparison of Islamic and Conventional Stock Mutual Funds in Turkey. Business & Management Studies: An International Journal, 8(5), 4463–4486. doi.org/10.15295/bmij.v8i5.1659
  • Hofert, M. (2023). Assessing ChatGPT’s Proficiency in Quantitative Risk Management. Risks, 11(9), 166. doi.org/10.3390/risks11090166
  • Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A Large Language Model for Extracting Information from Financial Text. Contemporary Accounting Research, 40(2), 806-841. doi.org/10.1111/1911-3846.12832
  • Jurafsky, D., & Martin, J. H. (2014). Speech and Language Processing (3. Edition draft). Available at: https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
  • Krause, D., (2023). Large Language Models and Generative AI in Finance: An Analysis of ChatGPT, Bard, and Bing AI. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4511540
  • Küçüker, M. (2023). Artificial Intelligence Applications in Accounting: ChatGPT's Accounting Exam. Fırat University Journal of Social Sciences, 33(2), 875-888. doi.org/10.18069/firatsbed.1289885
  • Li, J., Dada, A., Kleesiek, J., & Egger, J. (2023). ChatGPT in Healthcare: A Taxonomy and Systematic Review. medRxiv, 2023-03. doi.org/10.1101/2023.03.30.23287899
  • Liu, Z., Huang, D., Huang, K., Li, Z., & Zhao, J. (2021). FinBERT: A Pre-trained Financial Language Representation Model for Financial Text Mining. Proceedings of the Twenty-Ninth İnternational Conference on International Joint Conferences on Artificial Intelligence, 4513-4519 Available at: https://www.ijcai.org/proceedings/2020/622
  • OpenAI (2019). https://openai.com/research/gpt-2-1-5b-release
  • OpenAI (2021). https://openai.com/blog/gpt-3-apps
  • OpenAI (2023). https://openai.com/gpt-4
  • Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in Education: A Literature Review and Bibliometric Analysis. Cogent Education, 10(2), 2243134. doi.org/10.1080/2331186X.2023.2243134.
  • Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-training. Available at: https://www.mikecaptain.com/resources/pdf/GPT-1.pdf
  • Rajpoot, P. K., & Parikh, A. (2023). GPT-FinRE: In-context Learning for Financial Relation Extraction Using Large Language Models. ArXiv Preprint, Available at: https://arxiv.org/pdf/2306.17519.pdf
  • Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample G. (2023). LLaMA: Open and Efficient Foundation Language Models. ArXiv Preprint, arXiv:2302.13971. doi.org/10.48550/arXiv.2302.13971
  • Wang, N., Yang, H., & Wang, C. D. (2023). FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets. ArXiv Preprint, arXiv:2310.04793. doi.org/10.48550/arXiv.2310.04793
  • Wang, Z., Li, Y., Wu, J., Soon, J. & Zhang, X. (2023). FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis. doi.org/10.48550/arXiv.2308.01430
  • Wenzlaff, K. & Spaeth, S. (2022). Smarter than Humans? Validating How OpenAI’s ChatGPT Model Explains Crowdfunding, Alternative Finance and Community Finance. Available at SSRN: https://ssrn.com/abstract=4302443, http://dx.doi.org/10.2139/ssrn.4302443
  • Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D. & Mann, G. (2023). BloombergGPT: A Large Language Model for Finance, arXiv preprint, arXiv:2303.1756, doi.org/10.48550/arXiv.2303.17564
  • Xie, Q., Han, W., Zhang, X., Lai, Y., Peng, M., Lopez-Lira, A. & Huang. J. (2023). PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance. Cornell University. arXiv preprint, arXiv:2306.05443. doi.org/10.48550/arXiv.2306.05443
  • Yang, H., Liu, X.Y. & Wang, CD (2023). FinGPT: Open-Source Financial Large Language Models. Cornell University. arXiv preprint, arXiv:2306.06031. doi.org/10.48550/arXiv.2306.06031
  • Yang, Y., Tang, Y. & Tam, K.Y. (2023). InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning. Available at: https://www.arxiv-vanity.com/papers/2309.13064/
  • Yang, H., Liu, X.Y. & Wang, C.D. (2023) FinGPT: Opensource Financial Large Language Models. Cornell University. arXiv preprint, arXiv:2306.06031. doi.org/10.48550/arXiv.2306.06031
  • Zhang, L., Cai, W., Liu, Z., Yang, Z., Dai, W., Liao, Y., Qin, Q., Li,Y., Liu, X., Liu, Z., Zhu, Z. Wu, A. Guo, X. & Chen, Y. (2023). FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models. Cornell University. ArXiv Preprint, arXiv:2308.09975. doi.org/10.48550/arXiv.2308.09975
There are 41 citations in total.

Details

Primary Language English
Subjects Finance and Investment (Other)
Journal Section Articles
Authors

İnci Merve Altan 0000-0002-6269-7726

Metin Kılıç 0000-0002-5025-6384

Early Pub Date December 31, 2023
Publication Date December 31, 2023
Submission Date December 7, 2023
Acceptance Date December 31, 2023
Published in Issue Year 2023 Volume: 7 Issue: 2

Cite

APA Altan, İ. M., & Kılıç, M. (2023). SCIENCE FICTION TO REAL LIFE: BING AI AS AN INVESTMENT ADVISOR. Ekonomi İşletme Ve Yönetim Dergisi, 7(2), 240-260. https://doi.org/10.7596/jebm.31122023.003