QUANTIFUND MODEL FOR COHESIVE MUTUAL FUND PORTFOLIO OPTIMIZATION

Abstract

This article introduces Quantifund, an intelligent mutual fund portfolio optimization model developed using Microsoft Excel and Visual Basic for Applications (VBA). The tool is designed to empower mutual fund distributors and financial advisors with an affordable, user-friendly solution for generating data-driven, cohesive investment recommendations. With the exponential growth of the Indian mutual fund industry and the accompanying complexity in selecting among thousands of schemes, there is a critical need for decision-support systems that blend logic-driven automation with personalized advisory capabilities (Gupta & Singh, 2021).

Existing robo-advisory platforms, while algorithmically efficient, often fail to penetrate lower-tier advisory ecosystems due to high costs, complexity, and lack of contextual customization (Bhattacharyya, 2024). Quantifund addresses this gap by collecting investor-specific inputs—such as age, investment horizon, risk appetite, expected return, and target goals—and processing them through a VBA-coded selection engine. The model incorporates historical fund performance data from the top 15 Asset Management Companies (AMCs), applies weighted return calculations with greater emphasis on long-term performance, enforces an AMC exposure cap to ensure diversification, and filters fund options in alignment with the investor’s profile (Sultana & Pardhasaradhi, 2012).

Unlike fully automated robo-advisors, this semi-automated model facilitates human oversight, allowing advisors to review and adjust recommendations, thereby preserving a personalized touch. The output interface displays the recommended funds along with their categories, Net Asset Values (NAVs), and projected future values, enhancing decision clarity and transparency (Al-Abdullatif, 2023).

Beyond its technical utility, Quantifund promotes financial literacy, reduces advisor reliance on third-party platforms, and builds client trust through consistent and explainable logic. This article situates the development of Quantifund at the intersection of behavioral finance, modern portfolio theory, and technology acceptance, thereby contributing a scalable fintech framework characterized by simplicity, efficiency, and relevance to underrepresented advisory segments (Chitra & Thenmozhi, 2006).