MULTI-CRITERIA DECISIONMAKING FRAMEWORK FOR EVALUATING THE PERFORMANCE OF EQUITY MUTUAL FUNDS USING INTEGRATED RISK, RETURN, AND QUALITATIVE MEASURES WITH TOPSIS AND PANEL REGRESSION

Abstract

The present invention relates to a multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds using integrated risk, return, and qualitative measures with topsis and panel regression. Equity mutual funds, especially diversified and wealth-creating funds, have the potential to generate higher long-term returns compared to debt funds. However, they generally come with significant risks due to the dynamic nature of their portfolios. Therefore, a robust framework is required that not only considers quantitative aspects but also incorporates qualitative factors for evaluating long-term performance. The present invention develops a multi-criteria decision-making framework to evaluate the performance of equity mutual funds by integrating various risk, return, and qualitative measures. This framework employs the TOPSIS method to assign rankings based on fund performance. Furthermore, panel regression is used to observe the relationship between different factors, providing a comprehensive mechanism for evaluating long-term performance. This integrated approach enhances the accuracy of fund performance evaluations, helping investors make data-driven, informed decisions.

Specification

Description:Field of the Invention
The present invention relates to the technical field of financial analysis and investment management, more particular to a multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds using integrated risk, return, and qualitative measures with topsis and panel regression.
Background of the Invention
After the liberalization of economic policies, the Indian capital market has shown remarkable growth. The Indian Government is striving continuously to strengthen the financial stability of the stock markets year by year. Investors are increasingly interested in equity instruments in the stock market, primarily equity stocks, and aim to earn higher returns by investing their surplus funds in the stocks of listed companies. However, it remains a challenging task, especially for small and household investors, to earn long-term, decent returns from direct equity investments due volatile nature of the stock markets (Tandon & Walia N, 2015; Sharma et al., 2016).
Nowadays, equity mutual funds are gaining immense popularity for providing capital appreciation, along with periodic income in the form of dividends, to maintain the liquidity of investors (Arathy et al., 2015). Equity funds are becoming an affordable and dynamic investment option, especially for investors with small savings and limited financial literacy (Babbar & Sehgal, 2018). Diversified and wealth-creating equity funds enable investors to earn higher long-term returns while benefiting from diversification to minimize investment risk. Diversified funds are further categorized into "Large Cap Funds, Mid Cap Funds, and Small Cap Funds". The major components of the portfolios of these funds mainly consist of equity stocks from large-, mid-, and small-capitalization listed companies. Wealth-creating funds are further classified as Thematic Funds, Sectoral Funds, and ELSS Funds. Thematic funds invest in a group of industries or sectors united by a common theme, while Sectoral funds parts money in equity stocks within a particular sector (Sahu, 2023). ELSS funds also invest in equity stocks across various sectors of the Indian economy and provide tax benefits to their investors.
While these categories of equity funds are capable of generating higher long-term returns for small investors, they also carry a certain amount of risk due to the inherent nature of equity investments. The level of risk is distinct depending on the specific objectives of the fund. To achieve the maximum returns with lower risk from equity funds, it is imperative to conduct an in-depth scientific and statistical evaluation of the performance of target equity funds, considering both return and risk variables, as well as qualitative factors.
Return and risk variables measure the expected returns alongside the potential for losses in the performance evaluation process. Financial and statistical ratios, along with factor models, will be used to assess these variables. Qualitative factors also play an important role in the evaluation process, as they reflect the fundamental strength of a particular equity fund.
It is essential to apply both parametric and non-parametric statistical techniques, along with regression analysis, to assess the impact of return, risk, and qualitative variables on the expected return and risk outcomes.
Currently, there are around 45 Asset Management Companies (AMCs) operating in the Indian Mutual Fund Industry (AMFI). These AMCs offer a variety of equity funds with different plans and options, making it difficult for investors to select the right equity fund that generates comparatively higher returns at lower risk. In this context, Multi-Criteria Decision-Making (MCDM) techniques play a crucial role in helping investors choose the most suitable equity fund. MCDM provides methods for assigning weights to return, risk, and qualitative variables, as well as techniques for ranking equity funds based on their performance in these areas (Babalos et al., 2011). Ranking equity funds based on return and risk parameters, along with qualitative aspects, helps investors make informed and wise investment decisions.
The CRITIC Method (Diakoulaki et al., 1995; Chang, 2010; Zizovic et al., 2020) is one of the most appropriate techniques for assigning weights to return, risk, and qualitative variables, especially when the data consists primarily of secondary sources. The TOPSIS technique within MCDM is also an effective and suitable method for ranking equity funds. This technique identifies the alternative that is near to the best solution and distant from the ideal difficult solution in a multi-criteria decision-making scenario (Velasquez & Hester, 2013).
"As per the recent report from AMFI, the AUM of the Indian mutual fund industry has grown from ?10.96 trillion as of October 31, 2014, to ?67.26 trillion as of October 31, 2024, marking a more than six-fold increase over a span of 10 years." This evidence the growth of MF industry in India highlighting the fascinating investment avenue for investors. This invention will help investors navigate the complexities of the growing MF industry, providing a reliable, data-driven, and customizable framework to select the most suitable funds for their needs. Various problems are going to be addressed through this invention:
There is an abundance of mutual funds available to investors across various categories, making the process of selecting the right fund increasingly complex. Investors often find themselves in a challenging situation when it comes to evaluating and comparing funds based on their performance. The money they invest is hard-earned, and it is crucial that these funds are chosen wisely and rationally. This uncertainty and overwhelming choice can create a sense of urgency or pressure to make the right investment decisions. This framework is designed to address these challenges, helping investors navigate the complexities of mutual fund selection and make more informed decisions.
"Ambiguity and variability in qualitative factors can lead to uncertainties, and using a panel helps address this issue by providing a more consistent and reliable assessment."
Traditional methods of evaluating mutual funds often focus primarily on quantitative metrics like returns and risk, overlooking important qualitative factors that could influence long-term performance. The framework addresses this gap by creating a comprehensive evaluation mechanism that includes both types of measures.
Investors generally struggles in making data driven investment decision due to excess of information availability and complexity in analysing and interpreting the data. The proposed framework would help investors in making organized and structured approach to evaluate the mutual fund. The assigned ranking would help investors in identifying the best performing fund.
Panel regression would help in combining the relationship between quantitative and qualitative factors and in exploring impact of each other. This will help in evaluating long term performance with more accuracy and reliability.
Object of the present invention
The main objective of the present invention is to develop a multi-criteria decision-making framework which integrates risk, return and qualitative measures.
Another objective of the present invention is to resolve the complexities of making informed investment decisions.
Another objective of the present invention is to integrate the TOPSIS method with panel regression for performance evaluation of equity mutual fund.
Another objective of the present invention is to develop the accurate and reliable mechanism by incorporating panel regression by exploring the relationship between risk, return and qualitative measures
The further objective of the present invention is to assign ranking to mutual funds based on comprehensive performance evaluation.
Summary
This invention highlights a multi-criteria decision-making framework for evaluating the performance of equity MFs, addressing the growing complexities in the Indian MF industry. Since 1963 to 2024, mutual fund industry has come a long way in continuously attracting investors of different categories by providing decent returns. The participation of investors is increasing day by day more particularly in last few years challenging investors in making informed investment choices as there are abundant funds available. This process would help investors in not overlooking qualitative factors as it integrates risk, return and subjective measures for evaluating the performance. This also uses a panel approach to address the uncertainties, while panel regression is employed to determine the relationships between all the factors, leading to more accurate and reliable analysis.
Further, TOPSIS method is used to rank the mutual funds based on performance of the fund which helps investor in identifying top and worst performers. In a developing country like India with a vison of 'Viksit Bharat @ 2047 this invention would provide a transparent data driven approach evaluating mutual fund aiding investors in resolving the complexities of today's investment world.
Drawings

Figure 1: Multi-Criteria Decision-making Framework for Evaluating the Performance of Equity Mutual Funds with Integrated Risk, Return, and Qualitative Measures
Detailed Description of the Invention
The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
In any embodiment described herein, the open-ended terms "comprising," "comprises," and the like (which are synonymous with "including," "having" and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like. As used herein, the singular forms "a", "an", and "the" designate both the singular and the plural, unless expressly stated to designate the singular only.
This description of the invention highlights the procedure to be followed which helps the investors to evaluate the return and risk performance of their target equity funds. Appropriate benchmark index/ market index would also be selected for each specific equity fund. First return and risk performances of equity funds are measured by applying return and risk measures. These return and risk measures consist of financial & statistical ratios and factor models. Ratios of return and risk measured are calculated with the help of mutual fund NAV's, beta (systematic risk) of equity funds RF rate, Std Dev of fund, return & Std Dev of market index etc.
Excess return of equity fund is calculated by applying single and multifactor model. Single factor model consist CAPM and multifactor factor model having "Market Factor (MKT), Size Factor (SMB), Value Factor (HML), Liquidity Factor (LIQ) and Momentum Factor (MOM)". Fund Manager Experience, Age of Fund and Socially Responsible Investing and Expense Ratio are taken as qualitative factors for target equity funds. Scaling would be done for each specific qualitative factor.
On the basis of results of Normality, appropriate Parametric and Non Parametric Statistical Test would be applied on the results of return and return performance of specific equity funds. Parametric and Non-Parametric Tests are applied to check the statistical significant differences in return and risk performance results among the equity funds. Further Panel Regression is applied to develop a model for Qualitative Factors to check their impact on return measure performance of equity funds.
After applying Parametric & Non-Parametric Tests and Panel Regression, process of assigning ranks to equity funds would be started through MCDM-TOPSIS Technique. For this purpose, "CRITIC (Criteria Importance Through Intercriteria Correlation) Method" would be first applied for assigning the weights to Return, Risk and Qualitative Factors. For implementation of TOPSIS Technique it is also necessary to bifurcate the Return, Risk and Qualitative Factors into Beneficial and Non-Beneficial Criteria's. Beneficial Criteria's are those whose higher value are desired and Non-Beneficial Criteria's are those whose lower value are desired.
With the application of TOPSIS Technique final Performance Score of each equity fund is calculated. Results of Performance Score of each equity fund are arranged in ascending order to get the final ranks of equity funds. These final ranks help the investors to take intellectual investment decisions for investment in equity mutual funds.
Following is a brief explanation of each Return, Risk and Qualitative Factor:
Return Measures:
Annualized Daily Log Return: Annualized log returns from daily NAV of equity funds.
Sharpe's Ratio: This ratio explains the relationship between excess return earned per unit of volatility, represented by standard deviation of fund.
Treynor's Ratio: This ratio measures the risk premium/ excess return earned, per unit of systematic risk of fund, measured in terms of beta of fund
Appraisal Ratio: This ratio compares the fund's alpha to its unsystematic risk
Information Ratio: This ratio measures the portfolio return over the return of its benchmark, compared tracking error.
Sortino Ratio: This ratio calculates excess return earned per unit of downside risk.
Omega Ratio: This ratio quantifies the probability of target return.
Upside - Capture Ratio: Upside Market Capture ratio evaluates the fund manager performance as compared to the benchmark index at the time of bull period.
Capture Ratio: Ratio of Upside Capture Ratio to Downside Capture Ratio.
Modigilani - Modigilani Ratio(M2Ratio): It is an extended version of Sharpe Ratio which also includes standard deviation of market index and risk free rate.
Jensen's Alpha (From CAPM): Expected return earned by fund comprising market risk premium.
Jensen's Alpha From Five Factor Model:
r_f+ß_1 (R_m-R_f )+ß_2 (SMB)+ß_3 (HML)+ß_4 (MOM)+ß_5 (LIQ)
The given regression model comprises 5 factors, i.e. Market Factor (MKT), Size Factor (SMB), Value Factor (HML), Liquidity Factor (LIQ) and Momentum Factor (MOM).
Risk Measures:
Variance: This is a statistical ratio which measures the degree of variability in fund's average return.
Standard Deviation: (s)= vvariance
Downside Deviation: It is an estimate of the potential loss from investment in equity fund in a bearish time period.
Downside Capture Ratio: This ratio is used to evaluate the fund's manager performance in a bearish time period.
Qualitative Measures:
Age of Fund: Age of specific equity fund in number of years. This age is calculated from the date of inception of equity fund.
Fund Manager's Experience: Number of years from which fund manager of equity fund is maintaining the specific equity fund.
Socially Responsible Investing: Amount invested by respective AMC of equity fund in social responsible programs.
Expense Ratio: Annual maintenance cost of fund charge by respective AMC which includes management fees, operating cost etc.

, Claims:1. A multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds, comprising of:
• a panel methodology will assist investors in the valuation process with greater precision by minimizing biases in investor behavior. It would expand the decision-making criteria and enhance confidence in the process.
• Combines multiple data sources and improves predictive power- enables investors in identifying patterns and provides a robust framework to evaluate the performance; and
• Combining the impact of quantitative and qualitative data by using TOPSIS method of ranking. By measuring the distance from the ideal performance using critique method, it provides a novel and suitable tool for investors.
2. The multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds as claiemd in the claim 1, wherein the presnet frameowrk guide investors by providing a comprehensive, data-driven evaluation that combines both measurable financial metrics (e.g., risk and return) and qualitative factors (e.g., age of firm, experience of fund manager etc.).
3. The multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds as claiemd in the claim 1, wherein the presnet frameowrk guide stakeholders to invest in rational way by understanding the dynamics of risk and return parameters along with the qualitative aspects.
4. The multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds as claiemd in the claim 1, wherein the presnet frameowrk enable government and policymakers in observing the lacking and multiplying the opportunities.
5. The multi-criteria decisionmaking framework for evaluating the performance of equity mutual funds as claiemd in the claim 1, wherein the presnet frameowrk guide investors in making personalized choices with unique investment goals and socially responsible values.

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