【国信金工】隐性风险视角下的选基因子统一改进框架

Group 1: Contract Benchmark and Implicit Benchmark - The performance comparison benchmark of public funds plays a crucial role in fund operations, serving as a standard for measuring investment performance and a basis for fund manager evaluation [1][5] - There exists a mismatch between the contract benchmark and the actual investment style of public funds, leading to the identification of an "implicit benchmark" that aligns more closely with the fund's net value trajectory [1][7] - A quantitative method is proposed to identify the implicit benchmark for each fund, revealing that active equity funds have lower tracking errors relative to implicit benchmarks compared to contract benchmarks [15][18] Group 2: Explicit Risk and Implicit Risk - Risks associated with funds can be categorized into explicit risks, which are known and documented, and implicit risks, which are unknown and emerge with changing market conditions [2][29] - Implicit risks can significantly impact asset returns, necessitating a refined approach to risk assessment in fund performance evaluation [2][29] Group 3: Improvement of Selection Factors from Implicit Risk Perspective - The implicit risk model demonstrates a higher explanatory power for fund returns compared to the Fama five-factor model, with an average R-squared of 92.32% since 2010, surpassing the 84.94% of the Fama model [3][63] - The development of a composite selection factor adjusted for implicit risk has shown significant improvements in performance metrics, including a RankIC mean of 13.99% and an annualized RankICIR of 3.18 [3][55] Group 4: FOF Selected Portfolio Construction - The increasing allocation of public funds to Hong Kong stocks necessitates their consideration in portfolio construction, with a FOF portfolio yielding an annualized excess return of 8.86% relative to the median of active equity funds [4][6] - The FOF portfolio maintains a low tracking error of 3.52% and a high information ratio of 2.31, indicating robust performance stability [4][6] Group 5: Performance Evaluation from Absolute and Relative Perspectives - Traditional performance evaluation methods based on absolute returns may not accurately reflect the performance of funds with different implicit benchmarks, highlighting the need for relative performance assessments [21][24] - The analysis of funds with the same contract benchmark but differing implicit benchmarks reveals that absolute returns can be misleading, necessitating a relative evaluation approach [21][24] Group 6: Challenges in Traditional Risk Separation - Traditional multi-factor models, such as the Fama five-factor model, may not fully capture the complexities of fund returns due to the presence of unobserved implicit risks [41][45] - The need for a more dynamic approach to risk separation is emphasized, as traditional models may lead to biased estimates of fund performance [41][45] Group 7: Improvement of Selection Factors Based on Implicit Risk Model - The implicit risk model can enhance the stability and predictive power of various selection factors, including the Sharpe ratio and hidden trading ability, by adjusting for implicit risks [70][81] - The adjusted selection factors demonstrate improved performance metrics, such as higher RankIC and win rates, indicating a more reliable assessment of fund performance [70][81]