Quantitative Models and Construction Methods - **Model Name**: Three-dimensional Timing Framework **Model Construction Idea**: The model integrates liquidity, divergence, and prosperity indicators to assess market timing[6][13][15] **Model Construction Process**: 1. Liquidity Index: Measures market liquidity trends[24] 2. Divergence Index: Captures market disagreement levels[19] 3. Prosperity Index: Reflects economic activity and market sentiment[22] These three dimensions are combined to form a comprehensive timing framework[13][15] **Model Evaluation**: The framework indicates a downward market trend with limited short-term rebound potential[6][13] - **Model Name**: All-weather Strategy **Model Construction Idea**: Focuses on risk diversification and avoids reliance on predictions for stable returns[42][53] **Model Construction Process**: 1. Asset Selection: Diversified across equities, bonds, and commodities[55] 2. Risk Adjustment: Balances risk exposure through structured layers[46][48] 3. Structural Hedging: Implements cyclic hedging to smooth volatility[42][47][48] **Model Evaluation**: High-wave version achieves higher returns with moderate risk, while low-wave version prioritizes stability[53] - **Model Name**: Hotspot Trend ETF Strategy **Model Construction Idea**: Identifies ETFs with strong upward trends and high market attention[29][32] **Model Construction Process**: 1. Select ETFs with simultaneous upward trends in highest and lowest prices[29] 2. Construct support-resistance factors based on 20-day regression slopes[29] 3. Choose top ETFs with the highest turnover ratios in the past 5 and 20 days[29] **Model Evaluation**: The strategy outperformed the CSI 300 index with a 56.47% return since 2025[29][30] Model Backtesting Results - **Three-dimensional Timing Framework**: No specific numerical backtesting results provided - **All-weather Strategy**: - High-wave version: Annualized return 11.8%, max drawdown 3.6%, Sharpe ratio 1.9 (2025)[53] - Low-wave version: Annualized return 6.7%, max drawdown 2.0%, Sharpe ratio 2.4 (2025)[53] - 2026 YTD: High-wave return 1.8%, low-wave return 1.2%[53] - **Hotspot Trend ETF Strategy**: - Return since 2025: 56.47% - Excess return over CSI 300: 38.62%[29][30] Quantitative Factors and Construction Methods - **Factor Name**: Volatility Factor **Factor Construction Idea**: Captures stocks with high price fluctuations[56] **Factor Construction Process**: Measures weekly returns of high-volatility stocks[56] **Factor Evaluation**: Positive weekly return of 1.95%, indicating market preference for high-volatility stocks[56][57] - **Factor Name**: Momentum Factor **Factor Construction Idea**: Identifies stocks with strong upward price trends[56] **Factor Construction Process**: Calculates weekly returns of high-momentum stocks[56] **Factor Evaluation**: Positive weekly return of 1.58%, reflecting market interest in momentum stocks[56][57] - **Factor Name**: Leverage Factor **Factor Construction Idea**: Targets stocks with high financial leverage[56] **Factor Construction Process**: Measures weekly returns of high-leverage stocks[56] **Factor Evaluation**: Positive weekly return of 0.96%, showing market favor for leveraged stocks[56][57] - **Factor Name**: Twelve-month Residual Momentum **Factor Construction Idea**: Tracks residual momentum over a 12-month period[61] **Factor Construction Process**: $ specific\_mom12 = residual\_momentum\_12months $ Measures excess returns of stocks with strong residual momentum[61][62] **Factor Evaluation**: Weekly excess return of 0.87%, monthly excess return of 0.46%[61][62] - **Factor Name**: 1-year-1-month Return Factor **Factor Construction Idea**: Compares returns between 1-year and 1-month periods[61] **Factor Construction Process**: $ mom\_1y\_1m = (return\_1year - return\_1month) $ Calculates excess returns based on the difference between long-term and short-term returns[61][62] **Factor Evaluation**: Weekly excess return of 0.79%, monthly excess return of -0.03%[61][62] Factor Backtesting Results - **Volatility Factor**: Weekly return 1.95%[56][57] - **Momentum Factor**: Weekly return 1.58%[56][57] - **Leverage Factor**: Weekly return 0.96%[56][57] - **Twelve-month Residual Momentum**: Weekly excess return 0.87%, monthly excess return 0.46%[61][62] - **1-year-1-month Return Factor**: Weekly excess return 0.79%, monthly excess return -0.03%[61][62]

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]

Quantitative Models and Construction Methods 1. Model Name: Hotspot Trend ETF Strategy - **Model Construction Idea**: This strategy identifies ETFs with upward trends in both highest and lowest prices, further selecting those with high short-term market attention based on turnover rates[28] - **Model Construction Process**: - Select ETFs where both the highest and lowest prices exhibit an upward trend - Construct a support-resistance factor based on the relative steepness of the 20-day regression coefficient of the highest and lowest prices - Choose the top 10 ETFs with the highest ratio of 5-day turnover rate to 20-day turnover rate from the long group of the factor - Build a risk parity portfolio using these ETFs[28] - **Model Evaluation**: The strategy achieved a cumulative return of 52.22% since 2025, with an excess return of 28.36% over the CSI 300 Index[28] 2. Model Name: Three-Strategy Fusion ETF Rotation - **Model Construction Idea**: This model combines three industry rotation strategies—fundamental-driven, quality low-volatility, and distressed reversal—to achieve factor and style complementarity, reducing the risk of single-strategy dependence[31] - **Model Construction Process**: - Fundamental-driven strategy: Uses factors like unexpected prosperity, industry momentum, and inflation beta - Quality low-volatility strategy: Focuses on individual stock quality and low volatility - Distressed reversal strategy: Captures valuation recovery and performance reversal opportunities using factors like PB z-score and analyst long-term expectations - Combine the three strategies equally to form a diversified ETF rotation portfolio[31][32] - **Model Evaluation**: The strategy achieved a cumulative return of 12.18% from April 10, 2017, to January 16, 2026, with a Sharpe ratio of 0.74[36] 3. Model Name: All-Weather Strategy - **Model Construction Idea**: This strategy aims to achieve stable returns by avoiding reliance on predictions, using asset selection, risk adjustment, and structural hedging to smooth volatility[50] - **Model Construction Process**: - High-volatility version: Utilizes a four-layer structured risk parity approach across stocks, bonds, and gold - Low-volatility version: Employs a five-layer structured risk budgeting approach - Both versions are designed to bypass macroeconomic assumptions and achieve absolute returns without leverage[50][54][56] - **Model Evaluation**: - High-volatility version: Annualized return of 11.8%, maximum drawdown of 3.6%, and Sharpe ratio of 2.3 as of 2025 - Low-volatility version: Annualized return of 8.8%, maximum drawdown of 2.0%, and Sharpe ratio of 3.4 as of 2025[60][61] --- Model Backtesting Results 1. Hotspot Trend ETF Strategy - Cumulative return since 2025: 52.22% - Excess return over CSI 300 Index: 28.36%[28] 2. Three-Strategy Fusion ETF Rotation - Cumulative return (2017.04.10–2026.01.16): 12.18% - Sharpe ratio: 0.74 - Annualized return (2025): 27.29% - Maximum drawdown (2025): 7.18%[36][37] 3. All-Weather Strategy - High-volatility version: - Annualized return (2025): 11.8% - Maximum drawdown (2025): 3.6% - Sharpe ratio (2025): 2.3 - Low-volatility version: - Annualized return (2025): 8.8% - Maximum drawdown (2025): 2.0% - Sharpe ratio (2025): 3.4[60][61] --- Quantitative Factors and Construction Methods 1. Factor Name: Beta, Growth, and Momentum Factors - **Factor Construction Idea**: These style factors capture market preferences for high-beta, high-growth, and high-momentum stocks[62] - **Factor Construction Process**: - Beta factor: Measures the sensitivity of a stock's returns to market returns - Growth factor: Evaluates the growth potential of a stock based on metrics like earnings growth - Momentum factor: Assesses the continuation of a stock's price trend over a specific period[62] - **Factor Evaluation**: - Beta factor: Weekly return of 3.33% - Growth factor: Weekly return of 1.97% - Momentum factor: Weekly return of 0.45%[62][66] 2. Factor Name: Volume Mean and Volume Standard Deviation Factors - **Factor Construction Idea**: These alpha factors leverage trading volume trends over different time horizons to identify stocks with strong liquidity and trading activity[64] - **Factor Construction Process**: - Volume mean factors: Calculate the average trading volume over 1, 3, 6, and 12 months - Volume standard deviation factors: Measure the volatility of trading volume over the same time horizons - Normalize the factors by market capitalization and industry[64][67] - **Factor Evaluation**: - 1-month volume mean factor: Weekly excess return of 1.69% - 3-month volume mean factor: Weekly excess return of 1.66% - 6-month volume mean factor: Weekly excess return of 1.65%[67] 3. Factor Name: R&D to Assets and R&D to Sales Ratios - **Factor Construction Idea**: These factors highlight the importance of research and development (R&D) in driving company performance, particularly in small-cap stocks[68] - **Factor Construction Process**: - R&D to assets ratio: Total R&D expenditure divided by total assets - R&D to sales ratio: Total R&D expenditure divided by total sales - Normalize the factors by market capitalization and industry[68] - **Factor Evaluation**: - R&D to assets ratio: Excess return of 35.64% in the CSI 800 Index - R&D to sales ratio: Excess return of 29.45% in the CSI 1000 Index[68] --- Factor Backtesting Results 1. Beta, Growth, and Momentum Factors - Beta factor: Weekly return of 3.33% - Growth factor: Weekly return of 1.97% - Momentum factor: Weekly return of 0.45%[62][66] 2. Volume Mean and Volume Standard Deviation Factors - 1-month volume mean factor: Weekly excess return of 1.69% - 3-month volume mean factor: Weekly excess return of 1.66% - 6-month volume mean factor: Weekly excess return of 1.65%[67] 3. R&D to Assets and R&D to Sales Ratios - R&D to assets ratio: Excess return of 35.64% in the CSI 800 Index - R&D to sales ratio: Excess return of 29.45% in the CSI 1000 Index[68]