From curiosity to confidence: exploring investor behaviour in the age of generative AI
DOI:
https://doi.org/10.47989/ir31261271Keywords:
GenAI, Generative AI, Artificial Intelligence, Investor Behavior, Stock Market PredictionAbstract
Introduction. The advent of generative artificial intelligence has fundamentally changed how ordinary investors get, examine, and comprehend financial data to make predictions about the stock market.
Method. This study utilised a qualitative research method and snowball sampling. Semi-structured interviews with 25 investors from India, collected qualitative data which was analysed using thematic analysis in NVivo 12, accompanied by tool-assisted sentiment categorisation.
Analysis. Results of the study revealed that the speed, automation, and intuitive interfaces of generative AI solutions are the main factors driving investor adoption. In addition to stock price prediction, applications include sentiment analysis, scenario modelling, risk identification, and earnings call summary. Time savings, better decision-making, and a decrease in emotional bias are among the main advantages noted. Nevertheless, issues including hallucinations, lack of context in AI outputs, verification issues, and worries about bias, ethics, and data privacy still exist.
Results. Investors showed a degree of faith in generative AI technologies, frequently depending on technical indications and cross-verification from conventional financial sources.
Conclusion. This study contributes to a better understanding of investor trust, verification behaviour, and human-AI interaction in generative AI-supported investment decision-making. The long-term viability of the method will rely on user trust, regulatory alignment, and responsible deployment.
References
Adeoye‐Olatunde, O. A., & Olenik, N. L. (2021). Research and scholarly methods: Semi‐structured interviews. Journal of the American College of Clinical Pharmacy, 4(10), 1358-1367. https://doi.org/10.1002/jac5.1441
Almasarwah, A., Al-Wreikat, A., Marei, Y., and Alsharari, N. (2024). AI's influence on corporate transparency and financial performance: a new era. International Journal of Behavioural Accounting and Finance, 7(3), 233-253. https://doi.org/10.1504/IJBAF.2024.143833
Alobaidy, M. A. A., and Saeed, S. Z. (2023). A comparative study of multi-layer perceptron and Jordan recurrent neural networks for signals classification in a robotic system. Journal Européen des Systèmes Automatisés, 56(4), 547. https://doi.org/10.18280/jesa.560404
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R. and Horvitz, E. (2019, May). Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-13). https://doi.org/10.1145/3290605.3300233
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.https://doi.org/10.48550/arXiv.1606.06565.
Bandi, A., Adapa, P. V. S. R., and Kuchi, Y. E. V. P. K. (2023). The power of generative ai: A review of requirements, models, input–output formats, evaluation metrics, and challenges. Future Internet, 15(8), 260. https://doi.org/10.3390/fi15080260
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M.T., and Weld, D. (2021, May). Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-16). https://doi.org/10.1145/3411764.3445717
Bansal, I., Saxena, A., Kansal, I., Gupta, G., Kumar, A., & Sharma, K. (2025). A look at the recursive potential of generative artificial intelligence in the financial technology sector. In Navigating Data Science in the Age of AI: Exploring Possibilities of Generative Intelligence (pp. 83-103). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83608-432-720251005
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542-547. https://doi.org/10.2307/1925546
Bollerslev, T., & Mikkelsen, H. O. (1996). Modeling and pricing long memory in stock market volatility. SS Journal of Econometrics, 73(1), 151-184. https://doi.org/10.1016/0304-4076(95)01736-4
Bouasabah, M. (2024). A performance analysis of stochastic processes and machine learning algorithms in stock market prediction. Economies, 12(8), 194. https://doi.org/10.3390/economies12080194
Boute, R. N., Gijsbrechts, J., & Van Mieghem, J. A. (2021). Digital lean operations: smart automation and artificial intelligence in financial services. In Innovative Technology at the Interface of Finance and Operations: Volume I (pp. 175-188). Springer International Publishing. https://doi.org/10.1007/978-3-030-75729-8_6
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
Bruni Prenestino, F., Barbierato, E., & Gatti, A. (2025). Robust synthetic data generation for sequential financial models using hybrid variational autoencoder–Markov chain Monte Carlo architectures. Future Internet, 17(2), 95. https://doi.org/10.3390/fi17020095
Challa, K. (2024). Artificial intelligence and generative neural systems: creating smarter customer support models for digital financial services. Journal of Computational Analysis and Applications, 33(8).
Chandar, S. K. (2022). Convolutional neural network for stock trading using technical indicators. Automated Software Engineering, 29(1), 16. https://doi.org/10.1007/s10515-021-00303-z
Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, 100015. https://doi.org/10.1016/j.dajour.2021.100015
Choi, P. M. S., Huang, S. H., & Wang, Q. (2025). Large language models in finance: an overview. Finance and Large Language Models, 1-26. https://doi.org/10.1007/978-981-96-5833-6_1
Chopra, R., & Sharma, G. D. (2021). Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda. Journal of Risk and Financial Management, 14(11), 526. https://doi.org/10.3390/jrfm14110526
Dashtaki, S. M., Chagahi, M. H., Bahadori, A., Moshiri, B., Piran, M. J., & Montazeri, A. (2025). HSIF: a transformer-based cross-attention framework for cryptocurrency trend forecasting via multimodal sentiment–market fusion. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3605522
Desiraju, K., & Khan, A. (2023). Enhancing customer support services in banking using Generative AI. In International Working Conference on Transfer and Diffusion of IT (pp. 288-295). IFIP WG 8.6 International Working Conference on Transfer and Diffusion of IT, TDIT 2023, Nagpur, India, December 15-16, 2023, Proceedings, Part II (IFIP Advances in Information and Communication Technology, Vol. 698). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-50192-0_25
Dhakal, K. (2022). NVivo. Journal of the Medical Library Association: JMLA, 110(2), 270. https://doi.org/10.5195/jmla.2022.1271
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114. http://dx.doi.org/10.1037/xge0000033
Dubey, S. S., Astvansh, V., & Kopalle, P. K. (2025). Generative AI solutions to empower financial firms. Journal of Public Policy and Marketing, 44(3), 411-435. https://doi.org/10.1177/07439156241311300
Erdem, O., Hassett, K., and Egriboyun, F. (2025). Hallucination in AI-generated financial literature reviews: evaluating bibliographic accuracy. International Journal of Data Science and Analytics, 20, 4501-4510. https://doi.org/10.1007/s41060-025-00731-0
Fanfarillo, A., Roozitalab, B., Hu, W., & Cervone, G. (2021). Probabilistic forecasting using deep generative models. GeoInformatica, 25(1), 127-147. https://doi.org/10.1007/s10707-020-00425-8
Gangwani, P., & Panthi, V. (2025). Leveraging multimodal data and deep learning for enhanced stock market prediction. In M. Kumar, G. Srivastava, A. K. Singh, & K. Dubey (Eds.), AI‐Based Advanced Optimization Techniques for Edge Computing, 93-127. Wiley. https://doi.org/10.1002/9781394287062.ch4
Gierczyk, M., Gromkowska-Melosik, A., Scott, S., & Parker, C. (2024). The snowball sampling strategy in the field of social sciences. Contexts and considerations. Przegląd Badań Edukacyjnych (Educational Studies Review), 43(2), 87-104. https://doi.org/10.12775/PBE.2023.029
Gupta, R., Tiwari, S., & Chaudhary, P. (2025). Generative AI techniques and models. In Generative AI: techniques, models and applications (Lecture Notes on Data Engineering and Communications Technologies, 241). Springer. (pp. 45-64). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-82062-5_3
Hosseini, S. A., Hosseini, S. A., & Houshmand, M. (2024). Variational autoencoder-based dimension reduction of Ichimoku features for improved financial market analysis. Franklin Open, 8, 100135. https://doi.org/10.1016/j.fraope.2024.100135
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. https://doi.org/10.1111/1911-3846.12832
Husband, G. (2020). Ethical data collection and recognizing the impact of semi-structured interviews on research respondents. Education Sciences, 10(8), 206. https://doi.org/10.3390/educsci10080206
Illa, P. K., Parvathala, B., & Sharma, A. K. (2022). Stock price prediction methodology using random forest algorithm and support vector machine. Materials Today: Proceedings, 56(4), 1776-1782. https://doi.org/10.1016/j.matpr.2021.10.460
Iqbal, A., Ahmed, E., Rahman, A., & Ontor, M. R. H. (2024). Enhancing fraud detection and anomaly detection in retail banking using generative AI and machine learning models. The American Journal of Engineering and Technology, 6(11), 78-91. https://doi.org/10.37547/tajet/Volume06Issue11-09
Jain, R., & Vanzara, R. (2023). Emerging trends in AI-based stock market prediction: a comprehensive and systematic review. Engineering Proceedings, 56(1), 254. https://doi.org/10.3390/ASEC2023-15965
Joshi, R. (2025). Human-in-the-koop AI in financial services: data engineering that enables judgment at scale. Journal of Computer Science and Technology Studies, 7(7), 228-236. https://doi.org/10.32996/jcsts.2025.7.7.22
Joshi, S. (2025). Review of gen AI models for financial risk management. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 709-723. https://doi.org/10.32628/CSEIT2511114
Kallio, H., Pietilä, A. M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: developing a framework for a qualitative semi‐structured interview guide. Journal of Advanced Nursing, 72(12), 2954-2965. https://doi.org/10.1111/jan.13031
Kalu, F. A., & Bwalya, J. C. (2017). What makes qualitative research good research? An exploratory analysis of critical elements. International Journal of Social Science Research, 5(2), 43-56. https://doi.org/10.5296/ijssr.v5i2.10711
Kasztelnik, K., & Campbell, S. (2024). Revolutionizing financial health predictions: the integration of generative ai and advanced machine learning techniques. Journal of Applied Business and Economics, 26(6), https://doi.org/10.33423/jabe.v26i6.7434
Khalil, F., & Pipa, G. (2022). Is deep-learning and natural language processing transcending the financial forecasting? Investigation through lens of news analytic process. Computational Economics, 60(1), 147-171. https://doi.org/10.1007/s10614-021-10145-2
Kumar, T., Lalar, S., Garg, V., Sharma, P., & Mishra, R. D. (2025). Generative artificial intelligence (GAI) for accurate financial forecasting. In P. R. Chelliah, P. K. Dutta, A. Kumar, E. D. R. Santibanez Gonzalez, M. Mittal, & S. Gupta (Eds.), Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes, 57-76. Wiley. https://doi.org/10.1002/9781394271078.ch4
Lee, D. K. C., Guan, C., Yu, Y., and Ding, Q. (2024). A comprehensive review of generative AI in finance. FinTech, 3(3), 460-478. https://doi.org/10.3390/fintech3030025
Lee, J., Stevens, N., & Han, S. C. (2025). Large language models in finance (finllms). Neural Computing and Applications, 37, 24853–24867. https://doi.org/10.1007/s00521-024-10495-6
Lin, C. Y., & Marques, J. A. L. (2024). Stock market prediction using artificial intelligence: A systematic review of systematic reviews. Social Sciences and Humanities Open, 9, 100864. https://doi.org/10.1016/j.ssaho.2024.100864
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: people prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103. https://doi.org/10.1016/j.obhdp.2018.12.005
McClellan, M. (2025). AI and financial fragility: a framework for measuring systemic risk in deployment of generative AI for stock price predictions. Journal of Risk and Financial Management, 18(9), 475. https://doi.org/10.3390/jrfm18090475
Mukherjee, S., Sadhukhan, B., Sarkar, N., Roy, D., & De, S. (2023). Stock market prediction using deep learning algorithms. CAAI Transactions on Intelligence Technology, 8(1), 82-94. https://doi.org/10.1049/cit2.12059
Oliffe, J. L., Kelly, M. T., Gonzalez Montaner, G., & Yu Ko, W. F. (2021). Zoom interviews: benefits and concessions. International Journal of Qualitative Methods, 20, 16094069211053522. https://doi.org/10.1177/16094069211053522
Ooi, K. B., Tan, G. W. H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y.K., Huang, T. L., Kar, A. K., Lee, V. H., Loh, X. M., Mic, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., Teng, C. I., Wamba, S. F. & Wong, L. W. (2025). The potential of generative artificial intelligence across disciplines: perspectives and future directions. Journal of Computer Information Systems, 65(1), 76-107. https://doi.org/10.1080/08874417.2023.2261010
Oruh, J., Viriri, S., & Adegun, A. (2022). Long short-term memory recurrent neural network for automatic speech recognition. IEEE Access, 10, 30069-30079. https://doi.org/10.1109/ACCESS.2022.3159339
Padmavathy, M. (2024). Behavioral finance and stock market anomalies: Exploring psychological factors influencing investment decisions. Shanlax International Journal of Management, 11(S1), 191-97. https://doi.org/10.34293/management.v11iiS1-Jan.7164
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human factors, 39(2), 230-253. https://doi.org/10.1518/001872097778543886
Popkova, E. G., & Parakhina, V. N. (2018, April). Managing the global financial system on the basis of artificial intelligence: possibilities and limitations. In E. Popkova (Ed.), “The future of the Global Financial System: Downfall of Harmony” ISC 2018, Bratislava, Slovakia (Lecture Notes in Networks and Systems, Vol. 57, pp. 100). Springer International Publishing. https://doi.org/10.1007/978-3-030-00102-5_100
Ramzan, F., Sartori, C., Consoli, S., & Reforgiato Recupero, D. (2024). Generative adversarial networks for synthetic data generation in finance: evaluating statistical similarities and quality assessment. AI, 5(2), 667-685. https://doi.org/10.3390/ai5020035
Romeo, G., & Conti, D. (2025). Exploring automation bias in human–AI collaboration: a review and implications for explainable AI. AI & SOCIETY, 1-20. https://doi.org/10.1007/s00146-025-02422-7
Sai, S., Arunakar, K., Chamola, V., Hussain, A., Bisht, P., & Kumar, S. (2025). Generative AI for finance: applications, case studies and challenges. Expert Systems, 42(3), e70018. https://doi.org/10.1111/exsy.70018
Sattar, M. A., Toseef, M., & Sattar, M. F. (2020). Behavioral finance biases in investment decision making. International Journal of Accounting, Finance and Risk Management, 5(2), 69. https://doi.org/10.11648/j.ijafrm.20200502.11
Sharaff, A., Chowdhury, T. R., & Bhandarkar, S. (2023). Lstm based sentiment analysis of financial news. SN Computer Science, 4(5), 584. https://doi.org/10.1007/s42979-023-02018-2
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
Shivalini, M. S., Gandra, P. K., Alamuri, S., & Gupta, S. S. (2024). Transforming financial market prediction through generative AI-powered data management. 2024 4th International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-6). IEEE. https://doi.org/10.1109/CISCT62494.2024.11134255
Sims, C. A. (1987). [Vector autoregressions and reality]: Comment. Journal of Business and Economic Statistics, 5(4), 443-449. https://doi.org/10.2307/1391993
Singh, R., Bansal, R., & Niranjanamurthy, M. (2023). Use and application of artificial intelligence in accounting and finance: benefits and challenges. es. In M. Niranjanamurthy, K. Sheoran, G. Dhand, & P. Kaur (Eds.), Data Wrangling: Concepts, Applications and Tools, 251-274. Wiley. https://doi.org/10.1002/9781119879862.ch12
Teo, T. W., Liew, S. Y., Chng, C. H., Ng, J. Y., & Khoo, Z. Z. (2024, September). GPT-4 Powered Virtual Analyst for Fundamental Stock Investment by Leveraging Qualitative Data. In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), Bangkok, Thailand,(pp. 304-309). https://doi.org/10.1109/AiDAS63860.2024.10730589
Thakkar, A., & Chaudhari, K. (2021). A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert Systems with Applications, 177, 114800. https://doi.org/10.1016/j.eswa.2021.114800
Thapa, S., Shiwakoti, S., Shah, S. B., Adhikari, S., Veeramani, H., Nasim, M., & Naseem, U. (2025). Large language models (llm) in computational social science: prospects, current state, and challenges. Social Network Analysis and Mining, 15(1), 1-30. https://doi.org/10.1007/s13278-025-01428-9
Vargas, M. R., De Lima, B. S., & Evsukoff, A. G. (2017,). Deep learning for stock market prediction from financial news articles. In 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA), Annecy, France, (pp. 60-65). https://doi.org/10.1109/CIVEMSA.2017.7995302
Venkatarathnam, N., Goranta, L. R., Kiran, P. C., Raju, B. P. G., Dilli, S., Basha, S. M., & Kethan, M. (2024). An empirical study on implementation of AI and ML in stock market prediction. Indian Journal of Information Sources and Services, 14(4), 165-174. https://doi.org/10.51983/ijiss-2024.14.4.26
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. https://doi.org/10.2307/30036540
Vuletić, M., Prenzel, F., & Cucuringu, M. (2024). Fin-gan: forecasting and classifying financial time series via generative adversarial networks. Quantitative Finance, 24(2), 175-199. https://doi.org/10.1080/14697688.2023.2299466
Vullam, N., Yakubreddy, K., Vellela, S. S., Sk, K. B., Reddy, V., & Priya, S. S. (2023) Prediction and analysis using a hybrid model for stock market. In 2023 3rd International Conference on Intelligent Technologies (CONIT) Hubli, India, (pp. 1-5). IEEE. https://doi.org/10.1109/CONIT59222.2023.10205638
Weidinger, L., Uesato, J., Rauh, M., Griffin, C., Huang, P. S., Mellor, J., Glaese, A., Cheng, M., Balle, B., Kasirzadeh, A., Biles, C., Brown, S., Kenton, Z., Hawkins, W., Stepleton, T., Birhane, A., Hendricks, L. A., Rimell, L., Isaac, W., Haas, J., Legassick, S., Irving, G. & Gabriel, I. (2022,). Taxonomy of risks posed by language models. In Proceedings of the 2022 ACM conference on fairness, accountability, and transparency (pp. 214-229). ACM. https://doi.org/10.1145/3531146.3533088
Zhang, D., Ma, M., & Xia, L. (2022). A comprehensive review on GANs for time-series signals. Neural Computing and Applications, 34(5), 3551-3571. https://doi.org/10.1007/s00521-022-06888-0
Zhou, Q., & Sheu, J. B. (2025). The use of generative artificial intelligence (Generative AI) in operations research: review and future research agenda. Journal of the Operational Research Society, 1-21. https://doi.org/10.1080/01605682.2025.2561762
Zhou, Q., & Sheu, J. B. (2025). The use of Generative Artificial Intelligence (Generative AI) in operations research: Review and future research agenda. Journal of the Operational Research Society, 1-21. https://doi.org/10.1080/01605682.2025.2561762
Zhou, Y., Li, Y., Wang, D., & Liu, Y. (2023). A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism. International Journal of Hydrogen Energy, 48(40), 15317-15330. https://doi.org/10.1016/j.ijhydene.2023.01.068
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