Timing Perspective - The three-dimensional timing framework has entered a cautious state, indicating a judgment of oscillating decline due to a downward trend in liquidity and an upward trend in divergence[5] - The Shanghai Composite Index has repeatedly tested the demand line without breaking through, suggesting that while the upward trend remains, market volume is significantly shrinking[5] Sector Rotation - The communication equipment index saw a substantial inflow of 208% over the past week, while the oil and gas industry had a 630% inflow over the past month[27] - The ETF hot trend strategy has achieved a return of 54.82% since 2025, outperforming the Shanghai Composite Index by 33.27%[28] All-Weather Allocation - The high-volatility version of the all-weather strategy has an annualized return of 11.8% with a maximum drawdown of 3.6% and a Sharpe ratio of 2.3[59] - Since 2026, the high-volatility and low-volatility versions have returns of 2.3% and 0.9%, respectively[59] Factor Tracking - The market is currently characterized by a "high value, high leverage, high volatility" style, with the value factor achieving a positive return of 1.48% this week[61] - The liquidity shock factor has shown strong performance with a multi-head excess return of 1.56% over the past week[66] Risk Warning - Quantitative conclusions are based on historical statistics, and future market environment changes may lead to potential invalidation of these conclusions[69]

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]

Quantitative Models and Construction Timing Model: Three-Dimensional Timing Framework - **Model Name**: Three-Dimensional Timing Framework - **Construction Idea**: The model integrates liquidity, divergence, and prosperity indices to assess market timing opportunities. It aims to identify optimal investment periods by analyzing these three dimensions. [7][12][14] - **Construction Process**: 1. **Liquidity Index**: Tracks market liquidity trends using aggregated data from financial markets. 2. **Divergence Index**: Measures market disagreement or dispersion among participants. 3. **Prosperity Index**: Evaluates economic and market growth indicators. 4. Combine these indices into a unified framework to determine market timing signals. - **Evaluation**: The model has historically shown strong performance in identifying favorable market conditions. [7][12][14] Funds Flow Convergence Strategy - **Model Name**: Funds Flow Convergence Strategy - **Construction Idea**: Combines financing and large-order flows to identify industries with synchronized capital inflows. [28][31][33] - **Construction Process**: 1. **Financing Factor**: Defined as the net financing buy minus net financing sell, neutralized by Barra market capitalization factor. Calculated as the two-week change in the 50-day moving average. 2. **Large-Order Factor**: Measures net inflows based on industry transaction volume, neutralized by time series. Calculated using the 10-day moving average. 3. Combine the two factors, excluding extreme industries and large financial sectors, to enhance strategy stability. 4. Backtest results show annualized excess returns of 13.5% since 2018, with an IR of 1.7. [31][33] - **Evaluation**: The strategy demonstrates stable positive excess returns and lower drawdowns compared to other convergence strategies. [31][33] --- Quantitative Factors and Construction Style Factors - **Factor Name**: Value, Size, Volatility, Liquidity - **Construction Idea**: Style factors are constructed to capture specific market characteristics such as valuation, size, risk, and liquidity. [35][36] - **Construction Process**: 1. **Value Factor**: Measures the performance of low-valuation stocks relative to high-valuation stocks. 2. **Size Factor**: Tracks the performance of small-cap stocks versus large-cap stocks. 3. **Volatility Factor**: Compares low-volatility stocks to high-volatility stocks. 4. **Liquidity Factor**: Evaluates the performance of low-liquidity stocks against high-liquidity stocks. - **Evaluation**: Value factor recorded positive returns (+0.92%), while size (-0.21%), volatility (-2.38%), and liquidity (-2.23%) factors showed negative returns, reflecting market preferences for low-risk and low-liquidity stocks. [35][36] Alpha Factors - **Factor Name**: Momentum (mom_1y, mom_2y), Turnover Standard Rate (turnover_stdrate_1m, turnover_stdrate_3m), Analyst Forecast (ana_cov) - **Construction Idea**: Alpha factors aim to capture excess returns through predictive metrics such as price momentum, turnover rates, and analyst forecasts. [38][40] - **Construction Process**: 1. **Momentum Factors**: Measure stock returns over 1-year and 2-year periods. 2. **Turnover Standard Rate Factors**: Evaluate turnover rates over 1-month and 3-month periods. 3. **Analyst Forecast Factor**: Tracks the number of analyst forecasts over the past 90 trading days. - **Evaluation**: Momentum factors (mom_1y: +1.58%, mom_2y: +1.26%) and turnover factors (turnover_stdrate_1m: +1.30%, turnover_stdrate_3m: +1.56%) performed well, indicating strong predictive power. Analyst forecast factor (ana_cov: +1.22%) also showed positive returns. [38][40] Cross-Index Factors - **Factor Name**: PE_G, SUE, Turnover Standard Rate (turnover_stdrate_1m, turnover_stdrate_3m) - **Construction Idea**: These factors are designed to perform across different market indices, including large-cap and small-cap stocks. [41][42] - **Construction Process**: 1. **PE_G Factor**: Measures the difference between PE rankings and expected net profit growth rankings. 2. **SUE Factor**: Tracks net profit changes over the past eight quarters. 3. **Turnover Standard Rate Factors**: Evaluate turnover rates over 1-month and 3-month periods. - **Evaluation**: PE_G and SUE factors performed better in large-cap indices (e.g., HS300: PE_G +4.97%, SUE +4.09%) compared to small-cap indices (e.g., CN2000: PE_G +1.15%, SUE +1.34%). Turnover factors also showed higher returns in large-cap indices. [41][42] --- Backtesting Results Timing Model: Three-Dimensional Timing Framework - **Liquidity Index**: Positive trend observed - **Divergence Index**: Declining trend - **Prosperity Index**: Rising trend - **Overall Signal**: Full allocation recommended [7][12][14] Funds Flow Convergence Strategy - **Annualized Excess Return**: 13.5% - **IR**: 1.7 - **Weekly Excess Return**: +0.2% - **Absolute Weekly Return**: +2.8% [31][33] Style Factors - **Value**: +0.92% - **Size**: -0.21% - **Volatility**: -2.38% - **Liquidity**: -2.23% [35][36] Alpha Factors - **Momentum (mom_1y)**: +1.58% - **Momentum (mom_2y)**: +1.26% - **Turnover Standard Rate (turnover_stdrate_1m)**: +1.30% - **Turnover Standard Rate (turnover_stdrate_3m)**: +1.56% - **Analyst Forecast (ana_cov)**: +1.22% [38][40] Cross-Index Factors - **PE_G (HS300)**: +4.97% - **PE_G (CN2000)**: +1.15% - **SUE (HS300)**: +4.09% - **SUE (CN2000)**: +1.34% - **Turnover Standard Rate (turnover_stdrate_1m, HS300)**: +6.99% - **Turnover Standard Rate (turnover_stdrate_1m, CN2000)**: +0.02% [41][42]