量化选基月报：申报信息ETF轮动策略本月获得18.18%超额收益率-20260209

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]