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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64971
Volume:
10 |
6 |
Jun 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Management
Galgotias University
Management
Galgotias University
Management
Galgotias University
This thesis investigates the role of Artificial Intelligence (AI)-powered sentiment analysis tools in mitigating behavioral biases that distort rational investment decision-making. Financial markets are significantly influenced by investor psychology, with well-documented biases such as loss aversion, overconfidence, and herd mentality leading to suboptimal portfolio outcomes. The study examines how Natural Language Processing (NLP)-based sentiment tools—capable of processing news feeds, social media data, and analyst reports in real time—can serve as an objective corrective mechanism against these cognitive distortions. The research employs a mixed-methods approach combining a structured survey of approximately 100 retail investors and financial professionals in the Delhi-NCR region with a review of existing empirical literature. Hypotheses were tested to determine whether AI sentiment tools significantly reduce susceptibility to behavioral biases and whether their adoption leads to improved investment outcomes. Findings reveal that investors who use AI-driven sentiment analytics demonstrate measurably lower bias-driven decision errors and report higher confidence in their investment process. The thesis concludes with strategic recommendations for broader integration of AI sentiment tools in investment practice and proposes directions for future research.
KEYWORDS
Behavioral biases are not merely academic curiosities; they impose real and measurable costs on investor wealth. As AI sentiment tools become more accessible and sophisticated, they represent a scalable, low-cost intervention capable of improving the quality of retail investment decisions. The evidence presented in this thesis suggests that these tools are more than marketing innovations—they are practical instruments of financial discipline. With thoughtful design, appropriate regulation, and investor education, AI sentiment analytics can meaningfully contribute to a more behaviorally intelligent investment culture in India and beyond.
Araci, D. (2019). FinBERT: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261–292. Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. Chen, Q., Estrada, F., & Jiang, W. (2019). Earnings call sentiment and post-announcement drift: Evidence from NLP analysis. Journal of Financial Economics, 134(2), 394–420. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. López de Prado, M. (2018). Advances in financial machine learning. Wiley.
Publication Date
6/14/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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