Group 1 - The article highlights the increasing frequency of "black swan" events in the A-share market, leading to significant drawdowns in excess returns for public index-enhanced products in 2024, marking the largest historical drawdown [1][4][6] - The "black swan index" has shown a higher proportion of extreme events occurring in 2024 compared to previous years, indicating a substantial increase in the probability of extreme tail risks [1][10][14] Group 2 - The evolution of risk models has transitioned from single-factor to multi-factor approaches, and from explicit to implicit risks, reflecting a deeper understanding of market risks [18][19][21] - Implicit risks are defined as those that change with market conditions and are not fully captured by traditional explicit risk models, making them crucial for comprehensive risk management [46][52] Group 3 - A comprehensive risk control process is proposed, consisting of three stages: preemptive measures, in-process control, and post-event handling, aimed at effectively managing both explicit and implicit risks [60][63] - The introduction of a full-process risk control model has shown to significantly reduce drawdowns and volatility without adversely affecting long-term returns [3][61] Group 4 - The traditional multi-factor index-enhanced model has demonstrated an annualized excess return of 18.77% with a maximum drawdown of 9.68%, while the model incorporating full-process risk control has achieved an annualized excess return of 16.51% with a maximum drawdown of only 4.90% [3][5] - The performance metrics indicate that the full-process risk control model enhances the stability of excess returns while minimizing drawdowns and volatility [3][5][61]

Quantitative Models and Construction Methods Model Name: Black Swan Index - **Construction Idea**: Measure the extremity of market transactions based on the deviation of style factor returns[24][25] - **Construction Process**: 1. Calculate the daily return deviation of style factors: $$ \sigma_{s,t}=\frac{\bar{r}_{s,t}-\bar{r}_{s}}{\sigma_{s}} $$ where $\bar{r}_{s,t}$ is the daily return of style factor $s$ on day $t$, $\bar{r}_{s}$ is the average daily return of style factor $s$ over the entire sample period, and $\sigma_{s}$ is the standard deviation of daily returns of style factor $s$ over the entire sample period[25] 2. Calculate the Black Swan Index: $$ BlackSwan_{t}=\frac{1}{N}\times\sum_{s\in S}\left|\sigma_{s,t}\right| $$ where $BlackSwan_{t}$ is the Black Swan Index on day $t$, $S$ is the set of all style factors, and $N$ is the number of style factors[25] - **Evaluation**: The Black Swan Index effectively captures the extremity of market transactions, indicating higher probabilities of extreme tail risks[24][25] Model Name: Heuristic Style Classification for Cognitive Risk Control - **Construction Idea**: Address the discrepancy between individual and collective cognition in style classification to control cognitive risk[80][81] - **Construction Process**: 1. Calculate the value and growth factors for each stock based on predefined metrics[85] 2. Construct value and growth portfolios by selecting the top 10% and bottom 10% stocks based on factor scores[82] 3. Perform time-series regression to classify stocks into value, growth, or balanced styles: $$ r_{t,t}\sim\beta_{\mathit{Value}}\cdot r_{\mathit{Value},t}+\beta_{\mathit{Growth}}\cdot r_{\mathit{Growth},t}+\varepsilon_{t} $$ subject to $0\leq\beta_{\mathit{Value}}\leq1$, $0\leq\beta_{\mathit{Growth}}\leq1$, and $\beta_{\mathit{Value}}+\beta_{\mathit{Growth}}=1$[97] 4. Use weighted least squares (WLS) to estimate regression coefficients based on the most differentiated trading days[98] - **Evaluation**: The heuristic style classification method captures market consensus more accurately than traditional factor scoring methods, reducing cognitive risk[80][81] Model Name: Louvain Community Detection for Hidden Risk Control - **Construction Idea**: Cluster stocks based on excess return correlations to identify hidden risks[116][117] - **Construction Process**: 1. Calculate weighted correlation of excess returns between stocks: $$ Corr_{w}(X,Y)=\frac{Cov_{w}(X,Y)}{\sigma_{w,X}\cdot\sigma_{w,Y}}=\frac{\sum_{i=1}^{n}w_{i}(x_{i}-\overline{X_{w}})(y_{i}-\overline{Y_{w}})}{\sqrt{\sum_{i=1}^{n}w_{i}(x_{i}-\overline{X_{w}})^{2}}\cdot\sqrt{\sum_{i=1}^{n}w_{i}(y_{i}-\overline{Y_{w}})^{2}}} $$ where $w_{i}$ is the weight for day $i$, reflecting market volatility[118] 2. Use Louvain algorithm to cluster stocks based on weighted correlation matrix[117] 3. Ensure clusters have at least 20 stocks and remove clusters with fewer stocks[121] - **Evaluation**: The Louvain community detection method effectively identifies hidden risks by clustering stocks with similar return patterns, which traditional risk models may overlook[116][117] Model Name: Dynamic Style Factor Control - **Construction Idea**: Control style factors dynamically based on their volatility clustering effect[128][129] - **Construction Process**: 1. Identify style factors with high volatility or significant volatility increase: $$ \text{High volatility: Rolling 3-month volatility in top 3} $$ $$ \text{Volatility increase: Rolling 3-month volatility > historical mean + 1 standard deviation} $$ 2. Set the exposure of these style factors to zero in the portfolio[136] - **Evaluation**: Dynamic style factor control captures major market risks without significantly affecting portfolio returns, leveraging the predictability of volatility clustering[128][129] Model Name: Adaptive Stock Deviation Control under Target Tracking Error - **Construction Idea**: Adjust stock deviation based on tracking error to control portfolio risk[146][147] - **Construction Process**: 1. Calculate rolling 3-month tracking error for different stock deviation levels[153] 2. Set the maximum stock deviation that keeps tracking error within the target range[153] - **Evaluation**: Adaptive stock deviation control effectively reduces tracking error during high market volatility, maintaining portfolio stability[146][147] Model Backtest Results Traditional CSI 500 Enhanced Index - **Annualized Excess Return**: 18.77%[5][162] - **Maximum Drawdown**: 9.68%[5][162] - **Information Ratio (IR)**: 3.56[5][162] - **Return-to-Drawdown Ratio**: 1.94[5][162] - **Annualized Tracking Error**: 4.88%[5][162] CSI 500 Enhanced Index with Full-Process Risk Control - **Annualized Excess Return**: 16.51%[5][169] - **Maximum Drawdown**: 4.90%[5][169] - **Information Ratio (IR)**: 3.94[5][169] - **Return-to-Drawdown Ratio**: 3.37[5][169] - **Annualized Tracking Error**: 3.98%[5][169]

Macro and Strategy - The report highlights a seasonal increase in funding rates expected in June due to the peak of maturing funds and quarter-end factors [8][9] - The analysis of the public utility and environmental protection industry indicates a mixed performance, with the public utility index rising by 0.26% while the environmental index fell by 1.19% [9][10] Industry and Company - The public utility and environmental protection sector is undergoing significant changes, with the National Energy Administration initiating hydrogen energy pilot projects and Guangdong province issuing a "waste-free city" construction plan [10][11] - The report identifies a strong growth trajectory for the electronic specialty resin segment within Shengquan Group, driven by high demand in manufacturing and technology sectors [25][26] - AIA Group is recognized as a leading life insurance company in the Asia-Pacific region, achieving an annual new premium income of $8.606 billion and a net profit of $6.883 billion in 2024 [22][23] - The report emphasizes the unmet needs in the IBD treatment market, with a global market size exceeding $20 billion and significant opportunities for new drug development targeting this area [20][21] Investment Strategies - The public utility sector is recommended for investment, particularly in large coal-fired power companies and renewable energy leaders, as policies continue to support the growth of the renewable sector [11][12] - The education sector is highlighted for its resilience, with K12 education expected to maintain strong demand despite demographic challenges, and AI education products gaining traction [15][18] - The report suggests focusing on companies with strong cash flow in the waste incineration industry, as they are expected to benefit from improving cash flow dynamics [10][12]

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]