量化周报：市场支撑较强-20251214

Quantitative Models and Construction Methods 1. Model Name: Three-Strategy Fusion ETF Rotation Strategy - Model Construction Idea: The strategy integrates three dimensions: fundamental-driven rotation, quality low-volatility style rotation, and distressed reversal industry discovery. It aims to achieve factor and style complementarity while reducing the risk of single-strategy exposure[35][36] - Model Construction Process: 1. Fundamental Rotation Strategy: Selects industries based on factors such as exceeding expected prosperity, industry leadership effects, momentum, crowding, and inflation beta[36] 2. Quality Low-Volatility Style Strategy: Focuses on individual stock quality, momentum, and low volatility to enhance defensiveness[36] 3. Distressed Reversal Strategy: Utilizes PB z-score, long-term analyst expectations, and short-term chip exchange to capture valuation recovery and performance reversal opportunities[36] 4. Combines the three strategies equally to form a composite ETF rotation strategy, achieving multi-dimensional industry screening and reducing single-strategy risks[35][36] - Model Evaluation: The strategy effectively balances factor complementarity and style adaptation, providing robust performance across different market conditions[35][36] 2. Model Name: Hotspot Trend ETF Strategy - Model Construction Idea: This strategy identifies ETFs with strong upward trends and high market attention, constructing a risk-parity portfolio based on support-resistance factors and turnover ratios[30] - Model Construction Process: 1. Select ETFs where both the highest and lowest prices exhibit an upward trend[30] 2. Calculate the relative steepness of the regression coefficients for the highest and lowest prices over the past 20 days to construct support-resistance factors[30] 3. Choose the top 10 ETFs with the highest 5-day turnover ratio/20-day turnover ratio from the long group of the support-resistance factor, indicating increased short-term market attention[30] 4. Construct a risk-parity portfolio using these ETFs[30] - Model Evaluation: The strategy demonstrates strong performance, achieving significant excess returns compared to the benchmark[30] 3. Model Name: Capital Flow Resonance Strategy - Model Construction Idea: This strategy identifies industries with resonant capital flows by combining financing margin and active large-order capital flow factors, aiming to enhance stability and reduce drawdowns[42][44][45] - Model Construction Process: 1. Define the financing margin factor as the market-neutralized financing net buy-in minus securities lending net sell-out, calculated as the two-week change in the 50-day moving average[45] 2. Define the active large-order capital flow factor as the market-neutralized net inflow ranking of industry trading volume over the past year, using the 10-day moving average[45] 3. Exclude extreme industries from the active large-order factor and apply a negative exclusion for the financing margin factor to improve strategy stability[45] 4. Perform weekly rebalancing to select industries with resonant capital flows for long positions[45] - Model Evaluation: The strategy achieves stable positive excess returns with reduced drawdowns compared to other capital flow strategies[45] --- Model Backtesting Results 1. Three-Strategy Fusion ETF Rotation Strategy - 2025 YTD Performance: Portfolio return 25.60%, benchmark return 21.83%, excess return 3.77%, Sharpe ratio 0.24, maximum drawdown -7.18%[39][40] - Overall Performance (2017-2025): Annualized excess return 10.28%, Sharpe ratio 1.09, maximum drawdown -24.55%[40] 2. Hotspot Trend ETF Strategy - 2025 YTD Performance: Portfolio return 34.49%, benchmark (CSI 300) excess return 19.58%[30] 3. Capital Flow Resonance Strategy - 2018-Present Performance: Annualized excess return 14.3%, IR 1.4, reduced drawdowns compared to Northbound-Large Order Resonance Strategy[45] - Last Week Performance: Absolute return -0.27%, excess return 0.37% (relative to industry equal weight)[45] --- Quantitative Factors and Construction Methods 1. Factor Name: Momentum Factor - Factor Construction Idea: Captures the continuation of stock price trends over a specific period[53] - Factor Construction Process: 1. Calculate the 1-year momentum as the return over the past 12 months, excluding the most recent month[53] 2. Rank stocks based on momentum and form quintile portfolios[53] - Factor Evaluation: Demonstrates strong performance, with the 1-year momentum factor achieving a weekly excess return of 1.13%[53] 2. Factor Name: R&D to Total Assets Ratio - Factor Construction Idea: Measures the proportion of R&D investment relative to total assets, reflecting innovation capability[56] - Factor Construction Process: 1. Calculate the ratio of total R&D expenses to total assets for each stock[56] 2. Rank stocks based on this ratio and form quintile portfolios[56] - Factor Evaluation: Performs well in small-cap indices, with an excess return of 20.25% in the CSI 500 index[56] 3. Factor Name: Single-Quarter ROA YoY Change - Factor Construction Idea: Tracks the year-over-year change in return on assets (ROA) for a single quarter, reflecting profitability trends[56] - Factor Construction Process: 1. Calculate the year-over-year change in ROA for the most recent quarter, considering preliminary and forecasted data[56] 2. Rank stocks based on this change and form quintile portfolios[56] - Factor Evaluation: Excels in large-cap indices, with an excess return of 25.52% in the CSI 300 index[56] --- Factor Backtesting Results 1. Momentum Factor - Weekly Excess Return: 1.13%[53] 2. R&D to Total Assets Ratio - Excess Return in CSI 500: 20.25%[56] 3. Single-Quarter ROA YoY Change - Excess Return in CSI 300: 25.52%[56] - Excess Return in CSI 500: 10.16%[56] - Excess Return in CSI 1000: 21.98%[56]