Decoding the Investor's Mind: Exploring Neurofinance in Decision-Making
Authors
ISME Bengaluru (Research Scholar -University of Mysore) (India)
ISME Bengaluru, Research Supervisor (India)
Article Information
DOI: 10.51244/IJRSI.2025.120800393
Subject Category: Education
Volume/Issue: 12/9 | Page No: 4331-4342
Publication Timeline
Submitted: 2025-10-06
Accepted: 2025-10-12
Published: 2025-10-18
Abstract
This paper explores neurofinance as a new domain integrating neuroscience, psychology, and behavioural finance to understand how investors make decisions. Risk-taking, reward expectation, and loss aversion—all core behavioural biases in investments—are driven by the activity of certain neural structures, the nucleus accumbens, amygdala, and insula. The author proposes the use of artificial intelligence and machine learning in neurofinance to improve the predictive capabilities of robo-advisers. While mentioning the primary obstacles of laboratory settings, ethics, and privacy, the author also alludes to the growing prospects of neurofinance in the development of future emotionally and rationally balanced investments. The anticipated outcomes are encouraging, but research in the neurofinance domain suffers from a number of limitations - lackluster laboratory conditions, low participant numbers, and a paradigm shift towards the need for knowledge in neuroscience. Through the lenses of neuroscience and behavioural finance, this research hopes to make a step toward understanding investor behaviour and the mental aspects associated with financial choices.
Keywords
Behavioural finance, Traditional finance, Neurofinance
Downloads
References
1. Ariely, D., & Berns, G. S. (2010). Neuromarketing: The hope and hype of neuroimaging in business. Nature Reviews Neuroscience, 11(4), 284-292. [Google Scholar] [Crossref]
2. Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. Handbook of the Economics of Finance, 1, 1053-1128. [Google Scholar] [Crossref]
3. Burrell, P., & Oh, B. (1997). The impact of neural networks in finance. Neural Computing & Applications. https://doi.org/10.1007/BF01501506 [Google Scholar] [Crossref]
4. Camerer, C. (2018). The promise and challenges of neuroeconomics. Journal of Economic Literature, 56(1), 29-57. [Google Scholar] [Crossref]
5. De Martino, B., Kumaran, D., Seymour, B., & Dolan, R. J. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313(5787), 684-687. [Google Scholar] [Crossref]
6. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417. [Google Scholar] [Crossref]
7. Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49(3), 283-306. [Google Scholar] [Crossref]
8. Farah, M. J. (2012). Neuroethics: The ethical, legal, and societal impact of neuroscience. Annual Review of Psychology, 63, 571-591. [Google Scholar] [Crossref]
9. Fliessbach, K., Weber, B., Trautner, P., Dohmen, T., Sunde, U., Elger, C. E., & Falk, A. (2007). Social comparison affects reward-related brain activity in the human ventral striatum. Science, 318, 1305-1308. [Google Scholar] [Crossref]
10. Frydman, C., & Camerer, C. (2016). The psychology and neuroscience of financial decision making. Trends in Cognitive Sciences, 20(9), 661-675. [Google Scholar] [Crossref]
11. Glimcher, P. W., & Fehr, E. (2013). Neuroeconomics: Decision making and the brain. Academic Press. [Google Scholar] [Crossref]
12. Hafner, M., Pollitt, A., Dufort, L., & Cattaneo, A. (2021). The use of EEG neurofeedback in financial trading: A systematic review. Frontiers in Neuroscience, 15, 1-12. [Google Scholar] [Crossref]
13. Hirshleifer, D. (2015). Behavioral finance. Annual Review of Financial Economics, 7, 133-159. [Google Scholar] [Crossref]
14. James, R. (2011). Applying neuroscience to financial planning practice: A framework and review. https://doi.org/10.2139/ssrn.1968995 [Google Scholar] [Crossref]
15. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. [Google Scholar] [Crossref]
16. Kandasamy, N., et al. (2014). Cortisol shifts financial risk preferences. Proceedings of the National Academy of Sciences, 111(9), 3608-3613. [Google Scholar] [Crossref]
17. Knutson, B., & Bossaerts, P. (2007). Neural antecedents of financial decisions. Journal of Neuroscience, 27(31), 8174-8177. [Google Scholar] [Crossref]
18. Kuhnen, C. M., & Chiao, J. Y. (2009). Genetic determinants of financial risk-taking. PLoS ONE, 4(2), e4362. [Google Scholar] [Crossref]
19. Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47(5), 763-770. https://doi.org/10.1016/j.neuron.2005.08.008 [Google Scholar] [Crossref]
20. Lo, A. W. (2012). Adaptive markets: Financial evolution at the speed of thought. Princeton University Press. [Google Scholar] [Crossref]
21. Lo, A. W., & Repin, D. V. (2002). The psychophysiology of real-time financial risk processing. Journal of Cognitive Neuroscience, 14(3), 323-339. [Google Scholar] [Crossref]
22. Loomes, G. (2010). Modeling the cognitive processes underlying risky choice. Journal of Economic Behavior & Organization, 75(2), 179-194. [Google Scholar] [Crossref]
23. Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x [Google Scholar] [Crossref]
24. McCarney, R., et al. (2007). The Hawthorne Effect: A randomised, controlled trial. BMC Medical Research Methodology, 7(30), 1-7. [Google Scholar] [Crossref]
25. Niklas, A. (2016). Gain- and loss-related brain activation in risky gambles: An fMRI and eye-tracking study. http://dx.doi.org/10.1523/ENEURO.0189-16.2016 [Google Scholar] [Crossref]
26. Peterson, R. L. (2010). Neuroeconomics and neurofinance. https://doi.org/10.1002/9781118258415.ch5 [Google Scholar] [Crossref]
27. Poldrack, R. A. (2012). Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding. Neuron, 72(5), 692-697. [Google Scholar] [Crossref]
28. Poldrack, R. A., & Farah, M. J. (2015). Progress and challenges in neuroimaging studies of human decision-making. Current Opinion in Behavioral Sciences, 5, 1-6. [Google Scholar] [Crossref]
29. Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347-356. https://doi.org/10.1111/1467-9280.00067 [Google Scholar] [Crossref]
30. Sahi, S. K. (2012). Neurofinance and investment behaviour. https://www.emerald.com/ insight/publication/issn/1086-7376. https://doi.org/10.1108/10867371211266900 [Google Scholar] [Crossref]
31. Sapra, S., & Zak, P. J. (2019). Neuroscience and financial decision-making. Review of Behavioral Finance, 11(3), 299-316. [Google Scholar] [Crossref]
32. Sapienza, P., Zingales, L., & Maestripieri, D. (2009). Gender differences in financial risk aversion and career choices. Proceedings of the National Academy of Sciences, 106(36), 15268-15273. [Google Scholar] [Crossref]
33. Shiller, R. J. (2000). Irrational exuberance. Princeton University Press. [Google Scholar] [Crossref]
34. Tversky, A., & Kahneman, D. (1992). Advances in prospect-theory—Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. http://doi.org/10.1007/Bf00122574 [Google Scholar] [Crossref]
35. Wang, X.-J. (2008). Decision making in recurrent neuronal circuits. Neuron, 215-234. [Google Scholar] [Crossref]
36. Williamson, S. J., & Kaufman, L. (1997). Study of human occipital alpha rhythm: The International Journal of Psychophysiology, 26(1-3), 63-76. [Google Scholar] [Crossref]
37. Zak, P. J. (2007). The neuroeconomics of trust. Scientific American, 296(3), 88-95. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
- Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
- “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
- Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
- Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study