Quantitative Models and Construction Methods Model 1: Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Model Name**: Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Construction Idea**: The strategy aims to select funds with high stock price difference income, active trading motivation, and low possibility of performance dressing[2] - **Construction Process**: - The strategy combines the trading motivation factor and the stock price difference income factor - The trading motivation factor is constructed by classifying the trading motivations of funds[23] - The stock price difference income factor is derived from the stock price difference income in the fund's income statement[23] - The strategy adopts a semi-annual rebalancing approach, rebalancing at the end of March and August each year[23] - **Evaluation**: The strategy significantly outperformed the Wind Partial Equity Hybrid Fund Index in January 2026[2] Model 2: Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Model Name**: Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Construction Idea**: The strategy aims to capture the unique trading patterns of fund managers to generate excess returns[3] - **Construction Process**: - Construct a network based on the detailed holdings and transactions of fund managers[31] - Develop an indicator to measure the uniqueness of fund managers' trading[31] - The strategy adopts a semi-annual rebalancing approach, rebalancing at the beginning of April and September each year[31] - **Evaluation**: The strategy outperformed the Wind Partial Equity Hybrid Fund Index in January 2026[3] Model 3: Industry Theme ETF Rotation Strategy Based on Application Information - **Model Name**: Industry Theme ETF Rotation Strategy Based on Application Information - **Construction Idea**: The strategy aims to select industry theme ETFs similar to the applied ETFs to capture market investment hotspots[4] - **Construction Process**: - Conduct event-driven research on the entire issuance process of funds[36] - Construct the industry theme application similarity factor (T+1) based on the information disclosed during the application material public stage[36] - The strategy adopts a monthly rebalancing approach, with a transaction fee rate of 0.1% per side[36] - **Evaluation**: The strategy significantly outperformed the CSI 800 Index in January 2026[4] Model Backtesting Results Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Monthly Return**: 10.96%[27] - **Annualized Return**: 11.56%[27] - **Annualized Volatility**: 21.60%[27] - **Sharpe Ratio**: 0.54[27] - **Maximum Drawdown**: 48.39%[27] - **Annualized Excess Return**: 3.87%[27] - **Excess Maximum Drawdown**: 19.22%[27] - **Information Ratio (IR)**: 0.64[27] - **Monthly Excess Return**: 3.60%[27] Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Monthly Return**: 8.03%[35] - **Annualized Return**: 14.26%[35] - **Annualized Volatility**: 19.47%[35] - **Sharpe Ratio**: 0.73[35] - **Maximum Drawdown**: 37.26%[35] - **Annualized Excess Return**: 5.70%[35] - **Excess Maximum Drawdown**: 10.84%[35] - **Information Ratio (IR)**: 1.10[35] - **Monthly Excess Return**: 0.86%[35] Industry Theme ETF Rotation Strategy Based on Application Information - **Monthly Return**: 22.66%[40] - **Annualized Return**: 22.45%[40] - **Annualized Volatility**: 21.39%[40] - **Sharpe Ratio**: 1.05[40] - **Maximum Drawdown**: 34.89%[43] - **Annualized Excess Return**: 13.84%[43] - **Excess Maximum Drawdown**: 19.07%[43] - **Information Ratio (IR)**: 0.76[43] - **Monthly Excess Return**: 18.18%[43]

- The quantitative model "Bank of China Quantitative Stock Selection" achieved an excess return of 3.18% relative to the Wind Partial Equity Hybrid Fund Index in 2025, with a relative maximum drawdown of only 4.13%, an annualized tracking error of 5.44%, an IR of 0.49, and a return-drawdown ratio of 0.77[2][23][24] - The model demonstrated strong risk management capabilities, with a rolling 3-month annualized tracking error averaging only 5.26% in 2025, and a daily return correlation coefficient with the benchmark index averaging 0.96, indicating stable operations targeting the median of active equity funds[25][26][27] - The model ranked in the top 50% of all active equity funds for most of 2025 in terms of cumulative returns, showcasing its ability to consistently generate stable excess returns[27][29][31] - The return-drawdown ratio of the model ranked in the top 30% of the market (1072/3588), and its relative maximum drawdown ranked in the top 0.5% of all active equity funds (21/3588), reflecting strong drawdown control capabilities[29][30][31]

Core Viewpoint - The article discusses the recent fluctuations in the Shanghai Composite Index, emphasizing the importance of maintaining investment discipline and focusing on long-term value rather than short-term market volatility [3][6][10]. Market Analysis - The Shanghai Composite Index has experienced increased volatility as it surpasses key levels such as 3500, 3600, 3700, and 3800 points, with average daily fluctuations rising significantly [4][5]. - Historical data shows that market corrections often occur around these integer levels, driven by profit-taking behaviors from investors [5][10]. Long-term Investment Perspective - Over the past 20 years, the Wind All A Index has shown a cumulative increase of 807.99% with an annualized return of 12.01%, outperforming other asset classes like gold and bonds [8][9]. - The sustained growth of quality listed companies in China is a key driver behind these returns, highlighting the importance of long-term holding strategies [10]. Investment Strategy - The article advocates for a core-satellite investment strategy, which combines stable broad-based indices with high-potential assets to capture structural opportunities while managing overall portfolio volatility [11][12]. - Investors are encouraged to focus on asset allocation that reflects their risk tolerance, including exposure to broad indices like CSI A500 and Shanghai-Shenzhen 300 [12].