Hybrid Type-2 Fuzzy AHP-TOPSIS Framework for Mutual Fund Performance Evaluation in Indian Stock Market (BSE)*

Abstract

This study explores stock selection within a complex and nonlinear financial environment by integrating two major categories of performance indicators: Accounting Financial Measures (AFM) and Economic Value Measures (EFM). It argues that evaluating both sets together provides a more robust and comprehensive basis for investment decision-making than relying on either in isolation. The framework considers key financial criteria, including Return on Invested Capital (ROIC), Economic Value Added (EVA), Price/Earnings-to-Growth (PEG) ratio, and Free Cash Flow (FCF) yield, among others, to capture profitability, growth potential, and value creation. To address uncertainty and subjectivity inherent in financial analysis, the study employs the Interval Type-2 Fuzzy Analytic Hierarchy Process (FAHP), a sophisticated multi-criteria decision-making method. This approach allows for more flexible and realistic modeling of investor judgments under ambiguity. Financial data were collected from ten leading companies listed on the BSE Exchange, ensuring relevance and representation of high-performing firms. The findings reveal that the PEG ratio emerges as the most significant criterion in stock selection, emphasizing the importance of balancing earnings growth with valuation. Profitability-based indicators such as ROIC and EVA also rank highly, highlighting their critical role in assessing efficient capital use and long-term value generation. In contrast, liquidity measures show relatively lower importance in the decision-making hierarchy. Overall, the study underscores the value of prioritizing profitability and growth-oriented metrics, offering practical insights for investors seeking to build strategically sound and performance-driven portfolios.