Quant mutual funds are based on predictive analytics and systematically programmed investment strategies utilizing Artificial Intelligence (AI), Big Data Analytics, Algorithmic Trading, etc.; these funds ignore market-specific factors due to flexible diversification strategies in portfolio allocation; as a result, these funds have a greater emphasis on unsystematic risk as opposed to systematic risk. This study aims to examine how Inception Return and Systematic Risk influence the reward-to-volatility ratio. The analysis is based on a selection of Quant Money Managers Limited's mutual fund schemes (AMC). Only seven of the equity-based quant mutual fund strategies have been selected. The selection of seven quantitative mutual fund schemes is based on the portfolio turnover ratio. Sharpe's ratio, a performance evaluation metric, served as the dependent variable in the study. In the analysis, the independent variables are initial return and systematic risk. This study uses the Quality and Growth Stocks at Reasonable Price (QGARP) model and market-specific criteria to assist Quant Mutual Fund Managers with portfolio balance and diversification. Using investing and predictive analytics, fund managers of equity-focused quantitative mutual fund schemes diversify portfolios and reduce total risk with a high risk-to-reward ratio (Sharpe Ratio).

Quant Mutual Fund, Predictive Analytics, Artificial Intelligence (AI ), Big Data, Machine Learning, Algorithm Trading, Sharpe Ratio, Systematic Risk, Inception Return , Total Risk, BETA.