Building on Finance Theory to Forge the Future of Investment Practice
指出学术资产定价研究的假设和局限性使其难以直接应用于复杂真实市场，通过多个研究案例展示如何改进理论（如智能阿尔法、均值-方差-杠杆分析、增强型主动股票多空策略）以弥合理论与实践的差距，对量化投资从业者和金融学者有参考价值。
Academic asset pricing research has served as a foundational element in quantitative investing over the past several decades. However, its neoclassical assumptions and preference for parsimony have made academic research less useful when applied directly to complex, dynamic, and behavioral real-world markets. Successful investment practice requires a thorough grasp of the assumptions and limitations of each theory and involves adapting and enhancing research methodologies to better explain the real world. This article presents several examples of the authors’ research that highlight efforts to bridge the gap between theory and application. An active, dynamic, multifactor approach called smart alpha will help overcome the limitations of smart beta imposed by the standard factor models by accounting for a wider range of factors and changing market conditions. Mean–variance analysis will not yield optimal portfolios for leverage-averse investors but mean–variance–leverage analysis will by accounting for investor aversion to leverage risk. Enhanced active equity long–short strategies provide improved efficient frontiers by relaxing the long-only constraint while maintaining full benchmark index exposure. Hence, these strategies improve portfolio efficiency compared to long-only portfolios. Continuous-time finance models are not useful for explaining the behavior of financial markets; asynchronous, discrete-time, dynamic simulations are. Despite the many challenges, building on finance theory to forge the future of investment practice continues to be an exciting and rewarding endeavor.