AI-Driven Equity Valuation of Selected NIFTY 50 Companies Using Fundamental, Technical, and Machine Learning Techniques
DOI:
https://doi.org/10.36690/2674-5208-2026-2-36-45Keywords:
Artificial intelligence, equity valuation, NIFTY 50, fundamental analysis, technical analysis, machine learning, discounted cash flow, financial forecasting, stock market analysis, investment decision supportAbstract
Equity valuation remains a central task in financial markets because it allows investors to determine whether a stock is fairly priced and to identify attractive investment opportunities. Traditional valuation approaches, including financial statement analysis, accounting ratios, discounted cash flow valuation, and technical indicators, remain important, but they are increasingly insufficient in data-intensive and volatile market environments. Artificial intelligence and machine learning provide new opportunities for improving forecasting accuracy, identifying nonlinear market patterns, and supporting investment decision-making. The objective of this article is to develop and apply an AI-driven equity valuation framework for selected NIFTY 50 companies by integrating fundamental analysis, technical analysis, valuation models, risk indicators, and machine learning techniques. The study applies a multidimensional analytical and comparative methodology. It covers ten companies listed in the NIFTY 50 index, including TCS, Infosys, HDFC Bank, SBI, Reliance, ITC, HUL, Tata Motors, Maruti Suzuki, and Sun Pharma, over the period 2020-2025. The analysis includes fundamental indicators such as revenue growth, earnings per share, return on equity, return on assets, debt-equity ratio, and profit margin. It also uses technical indicators, discounted cash flow valuation, relative valuation ratios, risk and volatility indicators, and machine learning models, including linear regression, random forest, neural networks, and time-series forecasting. The results show that TCS, Infosys, ITC, and HUL demonstrate strong financial efficiency, while ITC, Tata Motors, HDFC Bank, and SBI show meaningful valuation upside based on intrinsic value estimates. Risk analysis identifies Tata Motors and SBI as higher-volatility stocks, while ITC, HUL, TCS, and Infosys demonstrate comparatively more stable profiles. Among the machine learning models, the neural network delivers the strongest predictive performance, with the highest accuracy and the lowest error values. The study concludes that artificial intelligence strengthens equity valuation when combined with traditional financial analysis rather than used as a standalone tool. The integrated framework improves the quality of stock assessment and supports better investment decisions in the Indian stock market.
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