Risk-Adjusted Performance Ranking and Clustering Analysis of NIFTY 50 Stocks on the Financial Metrics and Time-Series Forecasting

Stock market analytics is a vital part of systematic investment decision-making, portfolio management, and risk management.Conventional price-based evaluation procedures do not consider volatility, downside risk, and liquidity limitations, resulting in inappropriate stock selection.This paper suggests a combined risk-adjusted analytical framework that is applied to the constituents of the NIFTY 50 index based on historical data from 2015 to 2025.The dataset contains historical data for 52 unique companies that were constituents of the NIFTY 50 index during the study period (2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025).Although the index consists of 50 companies at any point in time, constituent changes over the study period resulted in a total of 52 unique companies being included in the analysis.This approach is a combination of the Compounded Annual Growth Rate (CAGR), annualized volatility, Sharpe ratio, Sortino ratio, and Maximum Drawdown to create a composite performance score.To ensure feasible tradability, liquidity screening is included.K-means clustering (K = 3) is used to carry out structural market segmentation based on growth and risk dimensions, and predictive power is determined through AutoRegressive Integrated Moving Average-based time-series forecasting.According to the empirical evidence, the top five risk-adjusted performers include TITAN, HINDUNILVR, BAJFINANCE, KOTAKBANK, and HDFC, with composite scores ranging from 0.833 to 1.829.The clustering results show three different performance regimes, with high-CAGR stocks (~26%) exhibiting better Sharpe ratios (~0.66) and lower volatility (~32%), while underperforming groups show negative growth and extreme drawdowns (-89%).The research adds a unified and quantitatively reliable stock assessment paradigm that balances growth, risk, and liquidity.In practice, the method aids robust stock ranking and provides a foundation for improving portfolio allocation strategies and predictive modeling.