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]
回踩幅度决定趋势强度
Guolian Minsheng Securities·2026-01-18 14:12