The Marginal Theory of Value and Its Implications for Modern Investment Decisions

The marginal theory of value, rooted in the principles of marginal utility and diminishing returns, has long been a cornerstone of economic thought. Yet its relevance to modern investment decisions-particularly in the context of portfolio allocation and resource efficiency-remains underexplored. Recent empirical research, however, underscores how these classical concepts continue to shape optimal investment strategies in an era increasingly dominated by artificial intelligence and algorithmic decision-making.

Marginal Utility and Portfolio Allocation: Beyond Traditional Frameworks

At its core, marginal utility theory posits that the value of an additional unit of an asset diminishes as its quantity increases. In portfolio management, this translates to the idea that the incremental benefit of adding a new asset or strategy to a portfolio declines as the portfolio grows. For instance, a 2025 study demonstrated that technical analysis strategies optimized through machine learning alone achieved cumulative returns of 1978% with monthly rebalancing, while fundamental analysis required semantic insights from large language models (LLMs) like ChatGPT-4o to reach peak performance. This divergence highlights how the marginal utility of different data types-algorithmic signals versus human-like semantic interpretation-varies depending on the investment horizon and methodology.

The mean-variance framework, introduced by Markowitz in 1952, remains a foundational tool for balancing risk and return. However, empirical evidence from 2020–2025 reveals its limitations in capturing the nuances of investor behavior. For example, portfolios optimized using CRRA utility functions often outperform mean-variance portfolios when risk aversion coefficients are misaligned with market realities. This suggests that investors must calibrate their utility functions to their specific risk preferences and market conditions to avoid suboptimal allocations.

The concept of diminishing returns manifests prominently in AI-driven portfolio optimization. A 2024 study found that over-reliance on short-term ML models-despite their initial predictive accuracy-leads to diminishing marginal returns as these models become overly reactive to market noise. Conversely, hybrid approaches that blend ML with LLM-derived semantic insights achieved a 701% return by balancing algorithmic precision with contextual interpretation. This underscores the importance of resource efficiency: investors must avoid over-allocating to high-cost, high-complexity models that yield diminishing incremental gains.

The integration of high-quality financial reporting into portfolio strategies has been shown to enhance resource efficiency. Research from 2023 indicates that firms with transparent reporting practices correlate with more efficient capital allocation, as investors can better assess risk and return trade-offs. This aligns with the principle of diminishing returns-where additional information beyond a certain threshold provides minimal marginal utility.

The Role of Behavioral and Computational Advances

Modern portfolio theory (MPT) has evolved beyond its traditional risk-return framework to incorporate behavioral insights. A 2022 study applied prospect theory to portfolio optimization, revealing that investor loss aversion and risk preferences significantly influence asset allocation. These behavioral factors, when integrated with computational tools like improved gray wolf optimization algorithms, enable more adaptive strategies that account for diminishing utility in volatile markets.

Simultaneously, the rise of LLMs has introduced new dimensions to portfolio management. For example, semantic intelligence derived from LLMs has proven particularly effective in fundamental analysis, where textual data from financial statements and economic narratives provide insights that purely quantitative models miss. This hybridization of tools reflects a strategic balance between maximizing marginal utility and mitigating diminishing returns.

Conclusion: Balancing Theory and Practice

The marginal theory of value remains a vital lens through which investors can evaluate portfolio allocation and resource efficiency. Empirical studies from 2020–2025 demonstrate that optimal strategies require a nuanced understanding of how marginal utility and diminishing returns interact with both traditional and AI-driven methodologies. Whether through hybrid models that combine ML and semantic analysis or behavioral frameworks that account for investor psychology, the key lies in aligning resource allocation with the diminishing marginal benefits of additional inputs.

As markets grow increasingly complex, investors must embrace a dual focus: leveraging advanced tools to capture marginal gains while remaining vigilant against the pitfalls of over-optimization. In doing so, they can navigate the delicate balance between innovation and efficiency, ensuring that their portfolios remain resilient in the face of evolving challenges.