Application of Artificial Intelligence in Smart Share Trading: An Empirical Study of NIFTY 50 Using Machine Learning and Deep Learning

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.