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]

Quantitative Models and Construction Methods Hidden Risk Model - **Model Name**: Hidden Risk Model - **Construction Idea**: Funds with high correlation in net asset value (NAV) trends are likely exposed to similar risks (explicit or hidden). By regressing fund factors weighted by correlation with similar funds, hidden risks can be stripped away [3][68][69] - **Construction Process**: 1. Identify funds with high NAV correlation over the past year (top N funds, N=20) [70] 2. Calculate weighted daily returns of similar funds based on correlation [70] 3. Perform time-series regression of the target fund's daily returns against the weighted returns of similar funds. The intercept term represents the adjusted alpha factor [70] - Formula: $$R_{p}=\alpha+\beta\cdot SimiRet+\varepsilon_{p}$$ [90] - **Evaluation**: Provides higher explanatory power for fund returns compared to traditional models like Fama-Five-Factor [3][86][90] Hidden Risk-Adjusted Comprehensive Selection Factor - **Factor Name**: Hidden Risk-Adjusted Comprehensive Selection Factor - **Construction Idea**: Combine factors improved by hidden risk adjustments (e.g., Sharpe ratio, hidden trading ability) with original factors that do not require adjustment (e.g., reverse investment ability) [115][118] - **Construction Process**: 1. Adjust factors like Sharpe ratio and hidden trading ability using hidden risk regression [94][101] 2. Combine adjusted factors with original factors using equal weighting [115] - **Evaluation**: Improves stability and predictive power of selection factors, with RankICIR increasing significantly [107][118] Sharpe Ratio Factor Adjustment - **Factor Name**: Hidden Risk-Adjusted Sharpe Ratio Factor - **Construction Idea**: Adjust the original Sharpe ratio factor by regressing it against the weighted Sharpe ratios of similar funds [94] - **Construction Process**: 1. Calculate weighted Sharpe ratios of similar funds based on correlation [94] 2. Perform cross-sectional regression of the original Sharpe ratio against the weighted Sharpe ratios [94] - Formula: $$Sharpe=\alpha+\beta\cdot SimiSharpe+\varepsilon$$ [94] - **Evaluation**: Stability significantly improved, with RankICIR increasing from 0.77 to 1.99 [96][98] Hidden Trading Ability Factor Adjustment - **Factor Name**: Hidden Risk-Adjusted Hidden Trading Ability Factor - **Construction Idea**: Adjust the original hidden trading ability factor by regressing it against the weighted hidden trading ability of similar funds [101] - **Construction Process**: 1. Calculate weighted hidden trading ability of similar funds based on correlation [101] 2. Perform cross-sectional regression of the original hidden trading ability against the weighted hidden trading ability [101] - **Evaluation**: Stability significantly improved, with RankICIR increasing from 1.68 to 2.23 [102][106] --- Model Backtesting Results Hidden Risk Model - **RankIC Mean**: 92.32% [86] - **RankICIR**: Not explicitly mentioned - **RankIC Win Rate**: Not explicitly mentioned Hidden Risk-Adjusted Comprehensive Selection Factor - **RankIC Mean**: 13.99% [118][121] - **RankICIR**: 3.18 [118][121] - **RankIC Win Rate**: 93.01% [118][121] - **Annualized Excess Information Ratio**: 2.4 [3] Sharpe Ratio Factor Adjustment - **RankIC Mean**: 7.70% [98] - **RankICIR**: 1.99 [98] - **RankIC Win Rate**: 87.41% [98] Hidden Trading Ability Factor Adjustment - **RankIC Mean**: 7.21% [102] - **RankICIR**: 2.23 [102] - **RankIC Win Rate**: 90.21% [102] --- Factor Backtesting Results Hidden Risk-Adjusted Comprehensive Selection Factor - **RankIC Mean**: 13.99% [118][121] - **RankICIR**: 3.18 [118][121] - **RankIC Win Rate**: 93.01% [118][121] - **Quarterly Excess Return (Top Decile)**: 1.46% [118][121] - **Quarterly Long-Short Return**: 2.74% [118][121] Sharpe Ratio Factor Adjustment - **RankIC Mean**: 7.70% [98] - **RankICIR**: 1.99 [98] - **RankIC Win Rate**: 87.41% [98] - **Quarterly Excess Return (Top Decile)**: 0.86% [98] - **Quarterly Long-Short Return**: Not explicitly mentioned Hidden Trading Ability Factor Adjustment - **RankIC Mean**: 7.21% [102] - **RankICIR**: 2.23 [102] - **RankIC Win Rate**: 90.21% [102] - **Quarterly Excess Return (Top Decile)**: 0.92% [102] - **Quarterly Long-Short Return**: Not explicitly mentioned --- FOF Portfolio Construction Results Hidden Risk-Adjusted Comprehensive Selection Factor-Based Portfolio - **Annualized Excess Return**: 8.86% [147] - **Annualized Tracking Error**: 3.52% [147] - **Excess Information Ratio**: 2.31 [147] - **Maximum Relative Drawdown**: 3.40% [147] - **Relative Return-to-Drawdown Ratio**: 2.61 [147] - **Monthly Win Rate**: 75.91% [147]