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International Journal of Science, Strategic Management and Technology

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ISSN: 3108-1762 (Online)
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IMPACT OF ARTIFICIAL INTELLIGENCE ON THE INDIAN STOCK MARKET

AUTHORS:
Tarun Bhatia
Mentor
Dr. Ritu Bharti
Affiliation
BBA (Third Year)Quantum University, Roorkee, Uttarakhand
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Artificial Intelligence (AI) has emerged as a transformative force across diverse sectors, with financial markets witnessing some of its most profound applications. This systematic review examines the multifaceted impact of AI on the Indian stock market over the decade spanning 2015 to 2025. Drawing upon peer-reviewed journal articles, conference proceedings, and reputed working papers, this paper synthesizes findings from over forty studies to provide a comprehensive understanding of how machine learning, deep learning, natural language processing (NLP), and algorithmic trading have reshaped market dynamics in India. The review identifies five primary thematic domains: stock price prediction, algorithmic and high-frequency trading, sentiment analysis, risk management, and regulatory challenges. Findings reveal that AI- driven models, particularly Long Short-Term Memory (LSTM) networks, transformer-based architectures, and ensemble methods, consistently outperform traditional statistical approaches in forecasting BSE Sensex and NSE Nifty 50 movements. Simultaneously, the proliferation of algorithmic trading has elevated market efficiency while raising concerns about flash crashes and systemic risk. The paper further highlights the growing role of AI in democratizing investment access through robo-advisors and fintech platforms. Regulatory frameworks by SEBI are found to be evolving but remain nascent relative to the pace of technological adoption. The review concludes with research gaps and future directions, underscoring the need for explainable AI and robust governance in Indian capital markets.

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Bhatia, T. (2026). Impact of Artificial Intelligence on the Indian Stock Market. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.449

Bhatia, Tarun. "Impact of Artificial Intelligence on the Indian Stock Market." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.449.

Bhatia, Tarun. "Impact of Artificial Intelligence on the Indian Stock Market." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.449.

References
1.acharya, R., Ghosh, S., & Nair, P. (2024). Detecting coordinated trading manipulation using graph neural networks: Evidence from Indian equity markets. Journal of Financial Crime, 31(2), 418–437.

2.Agarwal, N., & Mittal, M. (2020). BERT-based sentiment analysis for NSE Nifty 50 prediction: An NLP-augmented deep learning framework. Expert Systems with Applications, 158, 113–529.

3.Bhat, V., & Krishnaswamy, S. (2022). Algorithmic fairness in AI credit scoring and its market price implications for SME stocks on NSE. Finance Research Letters, 47, 102–617.

4.Bhatt, A., & Bhatt, R. (2015). Impact of algorithmic trading on market liquidity and volatility: Evidence from NSE India. IIMB Management Review, 27(4), 225–237.

5.Bhattacharya, D. (2023). SEBI enforcement patterns and AI-based market surveillance: A doctrinal and empirical analysis. SEBI Journal, 11(3), 5–29.

6.Bose, I., & Roy, S. (2021). Topic modeling of earnings call transcripts and post-announcement stock price drift: Evidence from NSE India. Pacific-Basin Finance Journal, 68, 101–607.

7.Chakraborty, S., Das, A., & Roy, P. (2019). Deep Q-network trading agents for Nifty 50: A reinforcement learning approach to autonomous portfolio management. Computational Economics, 54(3), 927–961.

8.Chopra, A., & Mehrotra, R. (2017). Genetic algorithm optimized neural networks for BSE 200 portfolio construction. International Journal of Financial Markets and Derivatives, 6(1), 44–68.

9.Desai, R., & Padhi, P. (2021). Robo-advisory in India: Technology adoption, democratization of investment, and regulatory challenges. Vikalpa: The Journal for Decision Makers, 46(2), 85– 105.

10.Garg, A., & Garg, V. (2016). Macroeconomic variable augmentation in SVM-based NSE Nifty 50 prediction. Applied Soft Computing, 49, 297–311.

 
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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