The Evolution of Asset Allocation: From Classical Models to Modern Innovations

Abstract

Asset allocation is a fundamental topic in investment management, serving as the foundation for constructing diversified portfolios and managing risks. This paper explores the evolution of asset allocation methodologies, from the classical Mean-Variance Optimization (MVO) framework introduced by Harry Markowitz to modern innovations that address its limitations. We first revisit the theoretical foundations of MVO and its extensions, highlighting their strengths and inherent challenges, such as sensitivity to estimation errors and lack of robustness in out-of-sample performance. Next, we examine alternative approaches, including minimum variance, maximum diversification, equal risk contribution, and Bayesian models like Black-Litterman, which incorporate subjective views into portfolio construction. Additionally, recent advancements such as the integration of higher-order moments, tail risk management, and machine learning techniques are discussed, showcasing their potential to redefine asset allocation practices. Through theoretical analysis and practical examples, this paper emphasizes the trade-offs between simplicity, robustness, and performance in portfolio construction, offering insights into the future of asset allocation research and practice.

Keywords: Asset Allocation, Mean-Variance Optimization, Black-Litterman, Bayesian Framework, Shrinkage, Kelly Formula, Portfolio Management

JEL Classification: G11