拆解量化投资的超额收益计算与业绩归因

Core Viewpoint - The article emphasizes the importance of excess return (Alpha) in quantitative investment, highlighting the need for thorough analysis and attribution of performance to understand the sources of excess returns and evaluate the effectiveness of quantitative strategies [2][3]. Group 1: Excess Return and Its Calculation - Excess return (Alpha) is defined as the return of an investment portfolio relative to a benchmark, reflecting the ability to outperform passive benchmarks through active management [3]. - The calculation of excess return varies based on the chosen strategy and benchmark, with a core formula being: Excess Return = Portfolio Return - Benchmark Return [3]. - An example illustrates that if a quantitative strategy has a return of 25% while the benchmark (e.g., CSI 300) returns 10%, the simple excess return is 15% [3]. Group 2: Sources of Excess Return - Excess return can be categorized into three components: Pure Alpha, Smart Beta, and Beta, each with different characteristics and risk profiles [3]. - The performance of excess return is influenced by external market factors and the comprehensive investment capabilities of the institution, which are critical for assessing a fund's sustainability of returns [3]. Group 3: Brinson Attribution Model - The Brinson attribution model is a widely used method for performance attribution, breaking down excess return into allocation effect, selection effect, and interaction effect [4]. - The model requires detailed portfolio holding data to accurately assess the contributions of asset allocation and stock selection to excess returns [4]. Group 4: Performance Attribution Example - An example using the Brinson model shows a fund outperforming the CSI 300 by 4.2%, with contributions from asset allocation and stock selection analyzed to determine the sources of excess return [9]. - The analysis reveals that stock selection contributes significantly to excess return, indicating a strong capability in identifying high-performing stocks [9]. Group 5: Barra Risk Model - The Barra risk model is utilized for post-performance analysis, helping to identify risk exposures and optimize investment strategies [10][11]. - The model decomposes risk into various factors, allowing for a detailed understanding of how different risk factors contribute to overall portfolio volatility [13]. Group 6: Risk Management and Optimization - The article discusses the importance of managing risk while maintaining return potential, with specific strategies for adjusting factor exposures to enhance performance [15][16]. - It highlights the need for continuous strategy iteration and adaptation to market conditions to mitigate risks associated with excess returns [17].