Abstract : This study aims to identify effective and consistent key performance metrics in the Indian financial exchange market using AI algorithms. By analysing the compound annual growth rate (CAGR), yearly fluctuation rate, Sharpe ratio, Sortino ratio, and Calmar ratio of different stocks particular banking sectors, this research determines their risks and returns by delving into fundamental analysis through machine learning approach. The AI-generated analysis reveals that various stocks in the Indian securities exchange exhibit distinct performance in terms of volatility and returns. Markedly Bank of Baroda and State Bank of India consistently anchored as the strongest contender, displaying the highest CAGR, Sharpe ratio, Sortino ratio, and Calmar ratio. These results indicate consistently exposition of significant yields and are a favorable investment choice. Furthermore, this study explores the potential of machine learning algorithms in optimizing key performance indicators within the Indian stock market.
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