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Application of Artificial Intelligence in Smart Share Trading: An Empirical Study of NIFTY 50 Using Machine Learning and Deep Learning
Subject area: Management and Commerce · Area of research: AI in Share Trading
Abstract
Artificial Intelligence (AI) is transforming financial-market analysis through automated data processing, pattern recognition, predictive modelling, sentiment analysis, portfolio optimisation and algorithmic trading. This study examines AI-based smart share trading with reference to the NIFTY 50 using a secondary empirical research design. The paper synthesises recent literature and documented NIFTY 50 empirical evidence while distinguishing reported results from results requiring fresh model estimation. Recent systematic reviews identify Support Vector Machines, Long Short-Term Memory networks and Artificial Neural Networks among frequently used approaches, while newer work increasingly examines attention-based architectures. The study distinguishes statistical prediction accuracy from practical trading performance and proposes evaluation using MAE, RMSE, MAPE, R², directional accuracy, ROC-AUC, Sharpe ratio and maximum drawdown, with transaction costs included in practical backtesting. A recent NIFTY 50 study reported substantially different errors across model architectures, illustrating the sensitivity of results to model design and evaluation. The paper proposes an integrated smart-trading framework combining market data, feature engineering, AI prediction, confidence filtering, risk management and human oversight. It concludes that AI can support market decision-making, but credible deployment requires data quality, leakage-resistant validation, realistic cost-adjusted testing, explainability and regulatory compliance.
Keywords
Artificial Intelligence; Smart Share Trading; NIFTY 50; Machine Learning; Deep Learning; LSTM; Algorithmic Trading; Stock Market Prediction; FinTech; Risk Management
References
[1] R. Chopra and G. D. Sharma, “Application of artificial intelligence in stock market forecasting: A critique, review, and research agenda,” Journal of Risk and Financial Management, vol. 14, no. 11, Art. no. 526, 2021. MDPI
[2] F. Dakalbab, M. Abu Talib, Q. Nasir, and T. Saroufil, “Artificial intelligence techniques in financial trading: A systematic literature review,” Journal of King Saud University – Computer and Information Sciences, vol. 36, no. 3, Art. no. 102015, 2024. ScienceDirect
[3] C. Y. Lin and J. A. L. Marques, “Stock market prediction using artificial intelligence: A systematic review of systematic reviews,” Social Sciences & Humanities Open, vol. 9, Art. no. 100864, 2024. ScienceDirect
[4] N. Rouf, M. B. Malik, T. Arif, and S. Sharma, “Stock market prediction using machine learning techniques: A decade survey on methodologies, recent developments, and future directions,” Electronics, vol. 10, no. 21, Art. no. 2717, 2021. MDPI
[5] G. Sonkavde, D. S. Dharrao, A. M. Bongale, S. T. Deokate, D. Doreswamy, and S. K. Bhat, “Forecasting stock market prices using machine learning and deep learning models: A systematic review, performance analysis and discussion of implications,” International Journal of Financial Studies, vol. 11, no. 3, Art. no. 94, 2023. MDPI
[6] A. Chauhan et al., “Capturing Short- and Long-Term Temporal Dependencies Using Bahdanau-Enhanced Fused Attention Model for Financial Data—An Explainable AI Approach,” FinTech, vol. 5, no. 1, Art. no. 4, 2026.
[7] Securities and Exchange Board of India, “Safer participation of retail investors in Algorithmic trading,” Circular No. SEBI/HO/MIRSD/MIRSD-PoD/P/CIR/2025/0000013, Feb. 4, 2025. SEBI
[8] Securities and Exchange Board of India, “Extension of timeline for implementation of SEBI Circular dated February 04, 2025 on ‘Safer participation of retail investors in Algorithmic trading’,” Circular No. SEBI/HO/MIRSD/MIRSD-PoD/P/CIR/2025/132, Sep. 30, 2025. SEBI
[9] NSE Indices, NIFTY 50 and historical index data. National Stock Exchange of India, n.d. NSE Indices
How to cite this paper
@article{1723436,
author = {Dr. A. Sukumar},
title = {Application of Artificial Intelligence in Smart Share Trading: An Empirical Study of NIFTY 50 Using Machine Learning and Deep Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {3143-3148},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723436.pdf},
abstract = {Artificial Intelligence (AI) is transforming financial-market analysis through automated data processing, pattern recognition, predictive modelling, sentiment analysis, portfolio optimisation and algorithmic trading. This study examines AI-based smart share trading with reference to the NIFTY 50 using a secondary empirical research design. The paper synthesises recent literature and documented NIFTY 50 empirical evidence while distinguishing reported results from results requiring fresh model estimation. Recent systematic reviews identify Support Vector Machines, Long Short-Term Memory networks and Artificial Neural Networks among frequently used approaches, while newer work increasingly examines attention-based architectures. The study distinguishes statistical prediction accuracy from practical trading performance and proposes evaluation using MAE, RMSE, MAPE, R², directional accuracy, ROC-AUC, Sharpe ratio and maximum drawdown, with transaction costs included in practical backtesting. A recent NIFTY 50 study reported substantially different errors across model architectures, illustrating the sensitivity of results to model design and evaluation. The paper proposes an integrated smart-trading framework combining market data, feature engineering, AI prediction, confidence filtering, risk management and human oversight. It concludes that AI can support market decision-making, but credible deployment requires data quality, leakage-resistant validation, realistic cost-adjusted testing, explainability and regulatory compliance.},
keywords = {Artificial Intelligence; Smart Share Trading; NIFTY 50; Machine Learning; Deep Learning; LSTM; Algorithmic Trading; Stock Market Prediction; FinTech; Risk Management},
month = {September},
}