Evaluating Stocks with a Subjectively Rational Approach

This paper proposes a structured, multi-criteria framework for stock evaluation grounded in the principles of rationally subjective decision-making. Building on rational choice theory and bounded rationality, the methodology adapts the Rational Approach to financial analysis by integrating multiple dimensions - risk, profitability, valuation, growth, dividends, and quality - into a weighted scoring system. The proposed Rational Approach serves as an explicit mathematical compromise: it limits cognitive overload (resource-rational) while allowing managers to inject institutional priorities without relying on “black-box” proprietary algorithms. For illustration, the framework is applied to the Magnificent Seven technology companies, with each firm graded across categories using point-in-time financial metrics sourced from publicly available data. Scores are normalized to a 0-100 scale, ensuring comparability and transparency without reliance on analyst forecasts or proprietary algorithms. The study demonstrates how rational weighting schemes can enhance investor efficiency by balancing expected returns against uncertainty while remaining accessible to both institutional and individual investors. Beyond its immediate application, the framework offers a pedagogical bridge between decision theory, behavioral finance, and empirical stock analysis. Future research directions include methodological refinements through adaptive weighting, empirical validation across sectors and geographies, benchmarking against established rating systems, and integration of qualitative dimensions such as governance, environmental, social, and corporate responsibility factors. Collectively, the findings underscore the potential of subjectively rational, multi-criteria grading methodologies to advance academic discourse and provide practical tools for transparent, reproducible, and bias-resistant investment decision-making.