Group 1 - The public fund industry has seen significant gains in the past three quarters, with an average return of 8.21% for fund managers managing over 10 billion, and 3,816 fund managers achieving an average return close to 20% [1] - The Shanghai Composite Index rose by 15.84%, the Shenzhen Component Index by 29.88%, and the ChiNext Index by 51.2%, indicating that even the less volatile Shanghai index is close to the average market return of 20% [1] - Despite the overall market gains, recent volatility in technology stocks poses a risk to investors who have been heavily invested in this sector, potentially leading to a significant drawdown in returns [1] Group 2 - Investing in index funds is generally more cost-effective for most investors compared to actively managed funds, as the performance of active funds can vary significantly based on the fund manager's style and market conditions [3] - It is recommended that industry-specific funds should not exceed 20% of an investment portfolio due to their cyclical nature, with a preference for balanced funds that outperform index funds [3] - Index funds should adopt a balanced style, avoiding heavy bias towards large or small caps, and consider strategies like the CSI 500 Index for better sector allocation and elasticity [3] Group 3 - The market offers numerous opportunities for profit, but success hinges on making informed investment choices and having a scientific investment allocation strategy [4] - The primary goal for most investors should be to outperform the index before seeking excess returns [4]

Core Viewpoint - Multiple public fund institutions have initiated a new round of portfolio adjustments to respond to the changing market environment, indicating a proactive approach to asset allocation in light of market volatility [1] Group 1: Portfolio Adjustments - Some portfolios have reduced their allocation to mixed funds while increasing the proportion of fixed-income funds [1] - Certain portfolios are seeking quality assets that benefit from the Federal Reserve's interest rate cuts [1] - Other portfolios have increased their holdings in balanced funds that have shown relatively stable net value performance over the medium to long term [1] Group 2: Market Outlook - Despite short-term fluctuations, investment advisory institutions believe that the overall upward trend of the equity market remains intact [1] - There is an emphasis on rationally viewing profit-taking sell-offs and avoiding the pitfalls of chasing highs and selling lows [1] - A balanced asset allocation with a medium to long-term perspective is recommended [1]

Core Insights - Multiple public fund institutions have initiated a new round of portfolio adjustments in response to the changing market environment [1] - Some portfolios have reduced their allocation to mixed funds while increasing their allocation to fixed-income funds [1] - Other portfolios are seeking quality assets that benefit from the Federal Reserve's interest rate cuts [1] - Certain portfolios have increased their positions in balanced funds that have shown relatively stable net value performance over the medium to long term [1] - Investment advisory institutions indicate that short-term fluctuations do not alter the overall upward trend of the equity market, advocating for a rational view on profit-taking and a balanced asset allocation from a medium to long-term perspective [1]

Quantitative Models and Construction Methods - **Model Name**: Carhart Four-Factor Model **Construction Idea**: Incorporates momentum factor into the Fama-French three-factor model to capture the "stronger gets stronger" phenomenon in stock markets[14] **Construction Process**: Formula: $ R_{p}-r_{f}\!\sim\!\!\alpha+\beta_{1}(R_{M}-r_{f})+\beta_{2}(R_{M}-r_{f})^{2}+\beta_{3}S M B+\beta_{4}H M L+\varepsilon_{p} $ - $R_{p}-r_{f}$ represents excess return of the portfolio relative to the risk-free rate - $R_{M}-r_{f}$ represents market excess return - $SMB$ and $HML$ represent size and value premiums respectively[28][30] **Evaluation**: Widely applicable across various asset classes, but its effectiveness in predicting future returns in A-shares is limited due to strong short-term reversal effects[14][17] - **Model Name**: Industry-Stripped Alpha Momentum **Construction Idea**: Removes market and industry beta risks to isolate alpha returns for momentum factor construction[47] **Construction Process**: Formula: $ R_{p}-r_{f}{\sim}\alpha+\beta_{1}(R_{M}-r_{f})+\beta_{2}(R_{M}-r_{f})^{2}+\sum_{i=1}^{11}\beta_{2+i}\,l n d_{i}+\varepsilon_{p} $ - Adds industry index returns ($ln d_{i}$) to the regression model to strip industry beta risks[51] **Evaluation**: Improves stability compared to traditional momentum factors but shows weaker positive selection effects since 2019[52][53] - **Model Name**: Low-Diversification Momentum **Construction Idea**: Identifies dates with low fund diversification to reduce beta risk interference and enhance predictive power[5][56] **Construction Process**: - Groups fund daily returns by diversification levels (using standard deviation of returns) - Constructs three sub-factors: low-diversification return factor, sorting momentum factor, and Sharpe ratio factor - Combines these sub-factors equally to form the low-diversification momentum factor[65][93] **Evaluation**: Demonstrates strong predictive power with low correlation to traditional momentum factors, indicating reduced beta risk interference[93][104] Model Backtesting Results - **Carhart Four-Factor Model**: - Rank IC: 6.01% (past 122 days alpha momentum)[31] - Rank ICIR: 0.57 (past 122 days alpha momentum)[31] - Quarterly long-short win rate: 66.67%[31] - **Industry-Stripped Alpha Momentum**: - Rank IC: 7.81% (past 122 days)[53] - Rank ICIR: 0.97 (past 122 days)[53] - Quarterly long-short win rate: 69.92%[53] - **Low-Diversification Momentum**: - Rank IC: 10.10%[93] - Rank ICIR: 1.09[93] - Quarterly long-short win rate: 71%[93] - Annualized long-short return: 10.81%[98] Quantitative Factors and Construction Methods - **Factor Name**: Historical Return Factor **Construction Idea**: Uses past fund returns to predict future performance[19] **Construction Process**: - Calculates returns over different time windows (e.g., past 20, 61, 122 days) - Tests predictive power using Rank IC and Rank ICIR metrics[20][22] **Evaluation**: Short-term returns show weak predictive power; long-term returns improve prediction but remain unstable[22][23] - **Factor Name**: Sharpe Ratio Factor **Construction Idea**: Adjusts fund returns for volatility to improve stability[24] **Construction Process**: - Calculates Sharpe ratios over different time windows (e.g., past 20, 61, 122 days) - Tests predictive power using Rank IC and Rank ICIR metrics[25][26] **Evaluation**: Stability improves compared to historical return factor but fails to address beta risk interference effectively[26][27] - **Factor Name**: Low-Diversification Return Factor **Construction Idea**: Focuses on low-diversification dates to reduce beta risk interference[65] **Construction Process**: - Groups fund daily returns by diversification levels - Uses average returns of the lowest-diversification group as the factor score[65][67] **Evaluation**: Strong predictive power with stable performance across different time windows[67][72] Factor Backtesting Results - **Historical Return Factor**: - Rank IC: 6.44% (past 244 days)[20] - Rank ICIR: 0.54 (past 244 days)[20] - Quarterly long-short win rate: 59.35%[20] - **Sharpe Ratio Factor**: - Rank IC: 6.44% (past 244 days)[25] - Rank ICIR: 0.64 (past 244 days)[25] - Quarterly long-short win rate: 61.79%[25] - **Low-Diversification Return Factor**: - Rank IC: 10.03% (past 3 months)[68] - Rank ICIR: 1.06 (past 3 months)[68] - Quarterly long-short win rate: 69.11%[68]