A Hybrid Optimization Model for Fundamental Stock Picking Based on Value Investing Principles of Graham and Buffett
Abstract
This study proposes a hybrid optimization framework for fundamental stock selection grounded in the value investing philosophies of Benjamin Graham and Warren Buffett. The research integrates intrinsic value estimation, financial ratio-based screening, and multi-factor optimization to construct a structured decision-making model for long-term equity investment. While traditional value investing relies heavily on qualitative judgment and static financial ratios, the proposed model introduces a quantitative optimization layer that enhances selection efficiency, reduces behavioral bias, and improves portfolio robustness under market uncertainty.
The model synthesizes fundamental analysis with constrained optimization techniques to identify undervalued securities with strong financial health, sustainable earnings, and long-term growth potential. The theoretical foundation is supported by sustainable development perspectives in financial decision-making, emphasizing the broader role of education, ethical reasoning, and structured analytical thinking in economic systems (Ansary & Behera, 2017). The literature suggests that structured analytical frameworks improve decision quality in complex environments, particularly where multi-dimensional evaluation criteria are involved.
Findings indicate that hybridizing Graham-style defensive investing filters with Buffett-style qualitative economic moat assessment produces a more resilient and adaptive stock selection mechanism. The study contributes a replicable framework that bridges classical value investing with modern optimization logic, offering implications for institutional investors, portfolio managers, and algorithmic trading systems.
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