Evaluating the GPT Chat Financial Functions on The Investors’ Mental Engineering

Articles in Press

Document Type : Research Paper

Authors

1 Department of Accounting, Sha.C., Islamic Azad University, Shahrood, Iran.

2 Department of Accounting, Ma.C., Islamic Azad University, Mashhad, Iran.

10.22103/jdc.2025.24071.1500

Abstract

Objective: One of the most important technological achievements in the past one or two years is the emergence of GPT Chat, which as an artificial intelligence chatbot can be effective in various areas of decision-making in the financial markets through computational and informational algorithms. The speed of development of these decision-making aids has been such that in this short period of time, many new theories in the field of behavioral finance have acknowledged its influence on the mentality and sentiment of investors. The purpose of this research is evaluating the GPT Chat financial functions on the investors’ mental/ sentiment engineering.
 
Method: From the point of view of methodology, on the one hand, this study is considered developmental in terms of results, and on the other hand, in terms of the purpose of conducting the study, it is categorized as descriptive-applied research. The data collection process is also mixed, so that due to the theoretical dispersion and the lack of reliable measurement tools, first, in the qualitative part, through a systematic content screening, the financial functions of GPTchat and the mental/emotional engineering of investors are identified that after performing the fuzzy Delphi analysis, through the gray VIKOR matrix, the identified dimensions of the two variables are evaluated against each other.
 
Results: The results of the study in the qualitative part indicate the identification of 5 criteria with a critical review of similar studies. In the quantitative part of the study, the findings showed that, by promoting the ability of reinforcement learning in decision-making as a priority of the financial function of GPT Chat, the most likely impact dimension of the identified fields of investors’ mental/ sentiment engineering is the reduction of mental anchors.
 
Conclusion: Based on the results obtained, it was expected that the financial function caused by GPT chat through large language models in the context of artificial intelligence platforms and neural networks, will give investors the opportunity to review their mental and emotional challenges more coherently and by engineering psychological criteria. and internal in making a decision, reduce the possibility of bias caused by mental anchor.

Keywords

Main Subjects

References

اصولیان، محمد؛ صادقی‌شریف، سیدجلال و شریفیانا، وحید (1400). تأثیر احساسات سرمایه‌گذار بر روند شکل‌گیری حباب در بازار سهام. چشم‌انداز مدیریت مالی، 11(35)، 91-118. https://jfmp.sbu.ac.ir/article_102127.html.
آقاسی، احسان؛ توحیدی، محمد و امیرشاهی، علی (1403). ارائه شاخصی جهت سنجش گرایش احساسی سرمایه‌گذاران در بازار سرمایه ایران. راهبرد مدیریت مالی، 12(1)، 87-112. https://jfm.alzahra.ac.ir/article_7652.html.
پازوکی، پریسا؛ صراف، فاطمه؛ جعفری، محبوبه و باغانی، علی (1400). کاربرد هوش مصنوعی در شناسایی عوامل عملکردی موثر بر سلامت مالی. نشریه مهندسی مالی و مدیریت اوراق بهادار، 12(48)، 371-390. https://journals.iau.ir/article_685992.html.
پناهی، بلال؛ فتحی، محمدرضا؛ مهدیه‌نجف‌آبادی، علی و رضی‌محب‌سراج، سمیه (1401). بررسی الگوی ارتباطی بین رفتارهای گذشته و سواد مالی با قصد سرمایه‌گذاری در بازار سهام با نقش میانجی متغیرهای فردی. مجله توسعه و سرمایه، 7(2)، 173-189. https://jdc.uk.ac.ir/article_3320.html.
حاجی علی‌عسگری، فاطمه؛ خان‌احمدی، احمد و آتشی، علیرضا (1402). چت‌بات هوش مصنوعی در سازمان بیمه سلامت ایران: تحولی در ارائه خدمات. بیمه سلامت ایران، ۶(۲)، ۹۱-۱۰۲. http://journal.ihio.gov.ir/article-1-289-fa.html.
خالقی‌زاده دهکردی، مریم؛ صراف، فاطمه و نجفی‌مقدم، علی (1401). کاربرد الگوریتم هوش مصنوعی در پیش‌بینی کارایی سرمایه‌گذاری با تأکید بر نقش معیارهای مدیریت ریسک. فصلنامه دانش سرمایه‌گذاری، 11(42)، 413-434. http://www.jik-ifea.ir/article_19356.html.
دشتی‌نژاد، معصومه؛ ایمانی‌برندق، محمد؛ رستمی، وهاب و محمدی، علی (1402). ارزیابی پیشران‌های لنگر ذهنی سرمایه‌گذاران براساس محدودیت ناشی از ابزارهای مالی مشتقه. مجله دانش حسابداری، 14(2)، 25-51. https://jak.uk.ac.ir/article_3503.html.
عارفی‌اصل، سولماز؛ عباس‌زاده، محمدرضا و حصارزاده، رضا (1403). رفتار معاملاتی سرمایه‌گذاران و تأثیر احساسات و لحن گزارش فعالیت هیئت‌مدیره بر آن. مجله دانش حسابداری، 15(3)، 1-20. https://jak.uk.ac.ir/article_4087.html.
گنجی، حمیدرضا؛ مشایخ، شهناز و صدیقی، زکیه (1402). بررسی تأثیر احساسات سرمایه‌گذاران بر انتظارات آنها از سودهای آتی. مطالعات تجربی حسابداری مالی، 20(78)، 155-190. https://qjma.atu.ac.ir/article_16374.html.
مهرعلی‌تبارفیروزجاه، بهروز؛ بهارمقدم، مهدی و شمس‌الدینی، کاظم (1402). بررسی و تبین عوامل مؤثر بر تصمیم‌گیری‌های متفاوت سرمایه‌گذاران در ایران. توسعه و سرمایه، 8(2)، 73-90. https://jdc.uk.ac.ir/article_3504.html.
موسوی‌جهرمی، یگانه؛ متقی، سمیرا؛ طالعی، سمانه و حیدری‌نسب، محسن (1402). بررسی تأثیر فناوری اطلاعات بر بهبود کارایی شرکت‌ها در بورس اوراق بهادار تهران. مجله توسعه و سرمایه، 8(1)، 213-231. https://jdc.uk.ac.ir/article_3422.html.
هاشمی، سیدعباس؛ رهروی‌دستجردی، علیرضا و حیدریان دولت‌آبادی، محمد (1402). تأثیر الگوهای تصمیم‌گیری احساسی سرمایه‌گذاران بر عملکرد شرکت. تحقیقات مالی، 25(2)، 205-227. https://jfr.ut.ac.ir/article_93300.html.
ولیان، حسن؛ عبدلی، محمدرضا و اروئی، مهران (1397). تعدیل شکاف انتظارات حسابرسی بر اساس استراتژی‌های منابع انسانی از طریق رویکرد تئوری راف و تئوری خاکستری. حسابداری مدیریت، 11(39)، 115-137. https://sid.ir/paper/198816/fa.

References

Abaddi, S. (2023). GPT revolution and digital entrepreneurial intentions, Journal of Entrepreneurship in Emerging Economies, Vol. ahead-of-print, https://doi.org/10.1108/JEEE-07-2023-0260.
Aghasi, E., Tohidi, M., & Amirshahi, A. (2024). Providing an index to measure the sentiment investor in the Iranian capital market. Financial Management Strategy, 12(1), 87-112. DOI: 10.22051/jfm.2024.44173.2838 [In Persian].
Ahangar, R.G., & Fietko, A. (2023). Exploring the potential of chatGPT in financial decision making. Advancement in Business Analytics Tools for Higher Financial Performance, 2(4), 1-18. https://doi.org/10.4018/978-1-6684-8386-2.ch005.
Andleeb, R., & Hassan, A. (2023). Impact of investor sentiment on contemporaneous and future equity returns in emerging markets. Sage Open, 13(3), 19-34. https://doi.org/10.1177/21582440231193568.
Arefi Asl, S., Abbaszadeh, M.R., & Hesarzadeh, R. (2024). Investor trading behavior and investors sentiment and the tone of board activity report. Journal of Accounting Knowledge, 15(3), 1-20. DOI: 10.22103/jak.2024.22419.3976 [In Persian].
Awal, M.R., & Asaduzzaman, F. (2024). Curse or blessing? Students’ experience from ChatGPT with an application of Colaizzi’s phenomenological descriptive method of enquiry and content analysis, Higher Education, Skills and Work-Based Learning, Vol. ahead-of-print, https://doi.org/10.1108/HESWBL-09-2023-0249.
Baber, H., Nair, K., Gupta, R., & Gurjar, K. (2024). The beginning of ChatGPT: a systematic and bibliometric review of the literature, Information and Learning Sciences, 125(7/8), 587-614. https://doi.org/10.1108/ILS-04-2023-0035.
Bernd, A. (2023). Will ChatGPT revolutionize accounting? The benefits of Artificial Intelligence (AI) in accounting, Applied Economics and Business Sciences, 6(1), 87-111. https://ideas.repec.org/p/zbw.
Biswas, S., Joshi, N., & Mukhopadhyaya, J, N. (2023). ChatGPT in investment decision making: An introductory discussion, Afro-Egyptian Journal of Infectious and Endemic Diseases, 13(1), 75-79. DOI: 10.13140/RG.2.2.36417.43369.
Cardillo, A. (2024). Perplexity AI vs. ChatGPT: AI chatbot comparison. Exploding Topics, https://explodingtopics.com/-blog/-perplexity-ai-vs-chatgpt.
Dashtinezhad, M., Imani Barandagh, M., Rostami, V., & Mohammadi, A. (2023). Evaluation of investors' mental anchor drivers based on limitation of derivative financial instruments. Journal of Accounting Knowledge, 14(2), 25-51. DOI: 10.22103/jak.2022.20114.3761 [In Persian].
De Kok, T. (2023). Generative LLMs and textual analysis in accounting: (Chat)GPT as research assistant? SSRN Electronic Journal, 3(1), 1-24. http://dx.doi.org/10.2139/ssrn.4429658.
Dixit, A., Chauhan, R., & Shaw, R. (2024). Application of smart systems and emerging technologies for disaster risk reduction and management in Nepal, International Journal of Disaster Resilience in the Built Environment, Vol. ahead-of-print, https://doi.org/10.1108/IJDRBE-07-2023-0085.
Fatouros, G., Soldatos, J., Kouroumali, K., Makridis, G., & Kyriazis, D. (2023). Transforming sentiment analysis in the financial domain with ChatGPT, Machine Learning with Applications, 14(1), 76-91. https://doi.org/10.1016/j.mlwa.2023.100508.
Gandouet, O., Belbahri, M., Jezequel, A., & Bodjov, Y. (2024). Distilled ChatGPT topic & sentiment modeling with applications in finance. Advances in Neural Information Processing Systems, 33(1), 16857–16867. https://doi.org/10.48550/arXiv.2403.02185.
Ganji, H., Mashayekh, S., & Seddighi, Z. (2023). Examining the impact of investors' sentiment on their expectations of future earnings. Empirical Studies in Financial Accounting, 20(78), 155-190. DOI: 10.22054/qjma.2023.73514.2452 [In Persian].
Hajialiasgari, F., Khanahmadi, A., Atashi, A. (2023). Artificial intelligence chatbot in Iran health insurance organization: A new era in service providing. Iranian Journal of Health Insurance, 6(2), 91-102. http://journal.ihio.gov.ir/article-1-289-fa.html [In Persian].
Hashemi, S.A., Rahrovi Dastjerdi, A., & Heydarian, M. (2023). The impact of investors' emotional decision patterns on firm performance. Financial Research Journal, 25(2), 205-227. DOI: 10.22059/frj.2022.344405.1007348 [In Persian].
Khaleghizadeh Dehkordi, M., Sarraf, F., & Najafi Moghadam, A. (2022). Application of artificial intelligence algorithm in predicting investment efficiency Emphasizing the role of risk management criteria. Journal of Investment Knowledge, 11(42), 413-434. http://www.jik-ifea.ir/article_19356.html?lang=en [In Persian].
Khan, M.S., Umer H. (2024). ChatGPT in finance: Applications, challenges, and solutions, Heliyon, 10(2), 62-81. https://doi.org/10.1016/j.heliyon.2024.e24890.
Kim, A.G., Muhn, M., & Nikolaev, V.V. (2024). Bloated Disclosures: Can ChatGPT help investors process information? Chicago Booth Research Paper, 2(2), 23-27. http://dx.doi.org/10.2139/ssrn.4425527.
Ko, H., Lee, J. (2023). Can Chatgpt Improve Investment Decision? From a Portfolio Management Perspective, Finance Research Letters, 64(2), 1-19.  http://dx.doi.org/10.2139/ssrn.4390529.
Lee, T.-K., & Koshoev, A. (2024). Investor sentiments revisited: negligence of stock-level sentiments may be a mistake, Review of Behavioral Finance, 16(3), 460-485. https://doi.org/10.1108/RBF-02-2023-0037.
Majid, S., Abbas, F., & Malik, M.N. (2023). Nexus between corporate innovation and investor sentiment: empirical evidence from the US, Kybernetes, 53(7), 2437-2457. https://doi.org/10.1108/K-12-2022-1688.
Mehralitabarfirouzjah, B., Baharmoghadam, M., & Shamsadini, K. (2023). Examining and explaining the influencing factors on the different decisions of investors in Iran. Journal of Development and Capital, 8(2), 73-90. DOI: 10.22103/jdc.2022.20398.1309 [In Persian].
Messaoud, D., & Ben Amar, A. (2024). Herding behaviour and sentiment: evidence from emerging markets, EuroMed Journal of Business, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EMJB-08-2023-0209.
Mousavi Jahromi, Y., Mottaghi, S., Talei, S., & Heydarinasab, M. (2023). Investigating the impact of information technology on improving the efficiency of companies in Tehran Stock Exchange. Journal of Development and Capital, 8(1), 213-231. DOI: 10.22103/jdc.2022.19403.1236 [In Persian].
Osoolian, M., SadeghiSharif, S.J., & Sharifiana, V. (2000). The Effect of Investor Sentiment on the Formation of Bubbles in the Stock Market. Financial Management Perspective, 11(35), 91-118. DOI: 10.52547/jfmp.11.35.91 [In Persian].
Panahi, B., Fathi, M.R., Mahdieh Najafabadi, A., & Razi moheb Seraj, S. (2022). Investigating the relationship between past behaviors and financial literacy with the intention to invest in the stock market with the mediating role of individual variables. Journal of Development and Capital, 7(2), 173-189. DOI: 10.22103/jdc.2022.18622.1180 [In Persian].
Panda, S., & Kaur, N. (2023). Exploring the viability of ChatGPT as an alternative to traditional chatbot systems in library and information centers, Library Hi Tech News, 40(3), 22-25. https://doi.org/10.1108/LHTN-02-2023-0032.
Parveen, Sh., Satti, Z.W., Subhan, Q.A., & Jamil, S. (2020). Exploring market overreaction, investors’ sentiments and investment decisions in an emerging stock market, Borsa Istanbul Review, 20(3), 105-128. https://doi.org/10.1016/j.bir.2020.02.002.
Pasca, M.G., & Arcese, G. (2024). ChatGPT between opportunities and challenges: an empirical study in Italy, The TQM Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TQM-08-2023-0268.
Pawlak, Z. (2005). Rough sets and flow graphs, rough sets, fuzzy sets. Data Mining and Granular Computing, 36(41), 1-11. https://link.springer.com/book/10.1007/11548669.
Pazuki, P., Sarraf, F., Jafari, M., & Baghani, A. (2017). Application of artificial intelligence in identifying performance factors affecting financial health. Journal of Financial Engineering and Securities Management, 12(48), 371-390. https://journals.iau.ir/article_685992.html [In Persian].
Pelster, M., & Val, J. (2024). Can ChatGPT assist in picking stocks? Finance Research Letters, 59(2), 100-121. https://doi.org/10.1016/j.frl.2023.104786.
Romanko, O., Narayan, A., & Kwon, R.H. (2023). ChatGPT-Based Investment Portfolio Selection, Operations Research Forum, 4(2), 91-109. https://doi.org/10.1007/s43069-023-00277-6.
Sheridan, E. (2023). ChatGPT outperforms traditional sentiment analysis to forecast stock prices, arXiv preprint arXiv:2006.08097, 2(1), 1-12. https://doi.org/10.48550/arXiv.2304.07619.
Shyng, J.Y., Tzeng G.H., & Wang F.K. (2007). Rough set theory in analyzing the attributes of combination values for insurance market, Expert System with Applications, 32(1), 56-64. DOI: 10.1016/j.eswa.2005.11.002.
Susnjak, T. )2022(. CHATGPT: The end of online exam integrity? arXiv, https://arxiv.org/abs/2212.09292.
Ullah, R., Ismail, H.B., Islam Khan, M.T., & Zeb, A. (2024). Nexus between Chat GPT usage dimensions and investment decisions making in Pakistan: Moderating role of financial literacy, Technology in Society, 76(1), 111-136. https://doi.org/10.1016/j.techsoc.2024.102454.
Valiyan, H., Abdoli, M.R., & Aroui, M. (2018). Adjusting the audit expectations gap based on human resource strategies through the rough theory and gray theory approach. Management Accounting, 11(39), 115-137. https://sid.ir/paper/198816/fa [In Persian].
Vu, L.T., Pham, D.N., Kieu, H.T., & Pham, Th.T. (2023). Sentiments Extracted from News and Stock Market Reactions in Vietnam. International Journal of Financial Studies, 11(3), 101-166. https://doi.org/10.3390/ijfs11030101.
Wahyono, B., Rapih, S., & Boungou, W. (2024). Unleashing the wordsmith: Analysing the stock market reactions to the launch of ChatGPT in the US Education sector. Finance Research Letters, 58(1), 36-52. https://doi.org/10.1016/j.frl.2023.104576.
Wang, W., Xu, Y., Wu, Y.J., & Goh, M. (2022). Linguistic information distortion on investment decision-making in the crowdfunding market, Management Decision, 60(3), 648-672. https://doi.org/10.1108/MD-09-2020-1203.
Wood, D., Achhpilia, M.P., Adams, M.T., & Aghazadeh, S. (2023). The ChatGPT artificial intelligence chatbot: How well does it answer accounting assessment questions? Issues in Accounting Education, 38(4), 1-28. https://www.researchgate.net/publication/370211135.
Wu, D., Song, J., Bian, Y., Zheng, X., & Zhang, Z. (2021). Risk perception and intelligent decision in complex social information network, Industrial Management & Data Systems, 121(1), 99-110. https://doi.org/10.1108/IMDS-10-2020-0566.
Zhang, Q., Xie, Q., & Wang, G. (2016). A survey on rough set theory and its applications. CAAI Transactions on Intelligence Technology, 1(4), 313-333. DOI: 10.1016/j.trit.2016.11.001.
Journal of Development and Capital

Articles in Press, Accepted Manuscript
Available Online from 22 December 2025

Files

History

  • Receive Date: 23 September 2024
  • Revise Date: 09 April 2025
  • Accept Date: 29 April 2025

Share

How to cite

Statistics