Quantitative Models and Construction Methods - **Model Name**: All-Weather Strategy **Model Construction Idea**: The strategy aims to achieve stable returns by avoiding reliance on predictions, leveraging diversified risk allocation principles[44][53] **Model Construction Process**: 1. **Asset Selection**: Diversify across equities, bonds, and commodities 2. **Risk Adjustment**: Balance risk exposure through structured layers 3. **Structural Hedging**: Implement cyclic hedging to smooth volatility - High-volatility version: Four-layer structure focusing on equity, bond, and gold risk parity - Low-volatility version: Five-layer structure emphasizing risk budgeting **Model Evaluation**: The strategy effectively balances risk and return, achieving stable absolute returns without relying on leverage or macroeconomic assumptions[44][53] - **Model Name**: Hotspot Trend ETF Strategy **Model Construction Idea**: Identify ETFs with strong short-term market attention and construct a risk-parity portfolio[32] **Model Construction Process**: 1. Select ETFs with both highest and lowest price trends in the past 20 days 2. Construct support-resistance factors based on the steepness of regression coefficients of the highest and lowest prices 3. Choose the top 10 ETFs with the highest turnover ratio (5-day/20-day) from the long factor group 4. Construct a risk-parity portfolio using these ETFs **Model Evaluation**: The strategy demonstrates strong excess returns over the benchmark, indicating its effectiveness in capturing market trends[32][35] - **Model Name**: Capital Flow Resonance Strategy **Model Construction Idea**: Combine financing and large-order capital flow factors to identify industries with capital flow resonance[40] **Model Construction Process**: 1. Define financing capital flow factor: Neutralize the financing net buy-sell data by market capitalization and calculate the 50-day average two-week change rate 2. Define large-order capital flow factor: Neutralize the industry’s one-year transaction volume and calculate the 10-day average net inflow ranking 3. Combine the two factors, excluding extreme industries and large financial sectors 4. Construct a weekly rebalancing strategy based on the combined factor scores **Model Evaluation**: The strategy achieves stable positive excess returns with reduced drawdowns compared to other capital flow strategies[40][42] Model Backtesting Results - **All-Weather Strategy**: - High-volatility version: Annualized return 11.8%, maximum drawdown 3.6%, Sharpe ratio 1.9 (2025 data)[53] - Low-volatility version: Annualized return 6.7%, maximum drawdown 2.0%, Sharpe ratio 2.4 (2025 data)[53] - 2026 YTD returns: High-volatility version 1.9%, low-volatility version 1.1%[53] - **Hotspot Trend ETF Strategy**: - 2025 cumulative return: 58.34% - Excess return over CSI 300 Index: 38.80%[32][35] - **Capital Flow Resonance Strategy**: - Annualized excess return since 2018: 14.3% - Information ratio (IR): 1.4 - Weekly absolute return: -2.53%, excess return: 1.88% (latest week)[40][42] Quantitative Factors and Construction Methods - **Factor Name**: Return Standard Deviation (1 Month) **Factor Construction Idea**: Measure the standard deviation of returns over the past month to capture volatility trends[61] **Factor Construction Process**: 1. Calculate daily returns over the past month 2. Compute the standard deviation of these returns **Factor Evaluation**: Demonstrates strong stock selection ability with consistent positive excess returns[61] - **Factor Name**: Average Turnover Rate (63 Days) **Factor Construction Idea**: Use the natural logarithm of the average turnover rate over the past 63 trading days to assess liquidity trends[61] **Factor Construction Process**: 1. Calculate the daily turnover rate for the past 63 trading days 2. Compute the natural logarithm of the average turnover rate **Factor Evaluation**: Exhibits robust performance in identifying high-liquidity stocks[61] - **Factor Name**: Consensus Forecast Net Profit Change (FY1) **Factor Construction Idea**: Measure the change in consensus forecast net profit (FY1) over different time horizons to capture earnings revisions[63] **Factor Construction Process**: 1. Calculate the difference between the current consensus forecast net profit (FY1) and the forecast from 1/3 months ago 2. Normalize the change by dividing it by the absolute value of the forecast from 1/3 months ago **Factor Evaluation**: Performs well in small-cap indices, reflecting market sensitivity to earnings revisions[63] Factor Backtesting Results - **Return Standard Deviation (1 Month)**: Weekly excess return 1.27%, monthly excess return 1.14%[62] - **Average Turnover Rate (63 Days)**: Weekly excess return 1.26%, monthly excess return 0.83%[62] - **Consensus Forecast Net Profit Change (FY1)**: - CSI 300: 28.03% (3-month horizon) - CSI 500: 16.98% (1-month horizon) - CSI 800: 26.83% (3-month horizon) - CSI 1000: 15.94% (3-month horizon)[63][64]

- The report introduces a "Three-dimensional Timing Framework" to assess market trends, incorporating liquidity, divergence, and prosperity indices to predict market movements[6][9][10] - The "Hotspot Trend ETF Strategy" is constructed by selecting ETFs with simultaneous upward trends in their highest and lowest prices, further filtered by regression coefficients and turnover ratios to form a risk-parity portfolio[27][28][29] - The "All-weather Strategy" employs a cyclic hedging design to balance long-term asset volatility without relying on leverage or macroeconomic assumptions, divided into high-volatility and low-volatility versions[38][46][47] Models' Backtesting Results - Three-dimensional Timing Framework: Historical performance indicates its effectiveness in predicting market downturns and rebounds[12][14] - Hotspot Trend ETF Strategy: Achieved a cumulative return of 65.46% since 2025, with an excess return of 43.25% over the CSI 300 Index[27][28] - All-weather Strategy: High-volatility version recorded an annualized return of 11.8% with a Sharpe ratio of 1.9, while the low-volatility version achieved an annualized return of 6.7% with a Sharpe ratio of 2.4 as of 2025[46][47] Factor Construction and Analysis - **Style Factors**: High-value, high-profitability, and high-leverage stocks outperformed, with respective returns of 3.08%, 2.10%, and 1.93% this week[49] - **Alpha Factors**: Future three-year inverse P/E ratio and one-week return standard deviation factors showed strong performance, with weekly excess returns of 1.13% and 1.07%, respectively[51][54] - **Index-specific Factors**: Large-cap indices favored rating adjustment and ROA delta factors, while small-cap indices preferred FY1 net profit change factors, with excess returns ranging from 15.80% to 34.58%[56][57]