- The report discusses the "Three-Dimensional Timing Framework" which includes liquidity, divergence, and prosperity as key factors for market timing[8][12][13] - The "ETF Hot Trend Strategy" is constructed by selecting ETFs with both highest and lowest prices in an upward trend, and further selecting those with the highest turnover rate in the past 5 days relative to the past 20 days to form a risk parity portfolio[29] - The "Three-Strategy Fusion" combines industry rotation strategies based on fundamental rotation, quality low volatility, and distressed reversal to achieve factor and style complementarity, reducing the risk of a single strategy[32][33][34] Model Backtesting Results - The "ETF Hot Trend Strategy" achieved a return of 43.6% year-to-date, with an excess return of 22.4% compared to the CSI 300 Index[29] - The "Three-Strategy Fusion" ETF rotation strategy had a return of 12.18% and a Sharpe ratio of 0.74 as of December 31, 2025, with a year-to-date return of 27.29%[37][38] Factor Construction and Performance - The "Beta Factor" recorded a positive return of 1.47% for the week, indicating a preference for high-beta stocks[50] - The "Growth Factor" recorded a positive return of 0.26% for the week, reflecting market attention to high-growth stocks[50] - The "Liquidity Factor" recorded a positive return of 0.16% for the week, indicating market preference for highly liquid stocks[50] Alpha Factor Performance - The "3-Month Average Trading Volume" factor showed the best performance with an excess return of 0.68% for the week[54][56] - The "3-Month Trading Volume Standard Deviation" factor also performed well with an excess return of 0.65% for the week[54][56] - In large-cap indices, the "Single Quarter ROA YoY Change" factor had an excess return of 28.46% in the CSI 300 Index[57][58] - In small-cap indices, the "Consensus Earnings Change (FY1)" factor had an excess return of 21.95% in the CSI 800 Index[57][58] Multi-Style Enhanced Strategy - The "Dividend Enhancement Strategy" performed well with an excess return of 0.68% for the week and an annualized excess return of 14.44% year-to-date[62][63]

- **Quantitative model and construction method** - **Model name**: Three-dimensional timing framework - **Model construction idea**: The model integrates liquidity, divergence, and prosperity indicators to assess market trends and provide timing signals [7][11][12] - **Model construction process**: 1. **Liquidity index**: Calculated based on market trading volume and other liquidity-related metrics [18] 2. **Divergence index**: Measures the degree of disagreement among market participants [16] 3. **Prosperity index**: Reflects the overall economic and market health, scaled to match the dimension of the Shanghai Composite Index [20] 4. Combine the three indices into a unified framework to evaluate market conditions and predict trends [12] - **Model evaluation**: The model maintains a stable performance in predicting market trends, with historical data showing its effectiveness in identifying periods of market oscillation and downturns [14] - **Quantitative factor and construction method** - **Factor name**: Growth factor - **Factor construction idea**: Measures the growth potential of stocks based on financial metrics such as revenue and profit growth [39][40] - **Factor construction process**: 1. Calculate the growth rate of key financial metrics, such as revenue, profit, and liabilities [42][44] 2. Normalize the metrics by market capitalization and industry to ensure comparability [41] 3. Construct the factor by aggregating the normalized metrics into a composite score [42][44] - **Factor evaluation**: The growth factor demonstrated positive returns, with high-growth stocks outperforming low-growth stocks in the recent week [40][42] - **Factor name**: Size factor - **Factor construction idea**: Evaluates the performance of stocks based on their market capitalization [39] - **Factor construction process**: 1. Divide stocks into groups based on market capitalization [39] 2. Calculate the average return for each group [39] 3. Compare the performance of large-cap stocks against small-cap stocks [39] - **Factor evaluation**: Large-cap stocks outperformed small-cap stocks, with the size factor recording positive returns [39] - **Factor name**: Beta factor - **Factor construction idea**: Measures the sensitivity of stocks to market movements [40] - **Factor construction process**: 1. Calculate the beta of each stock based on historical price movements relative to the market [40] 2. Group stocks into high-beta and low-beta categories [40] 3. Compare the performance of high-beta stocks against low-beta stocks [40] - **Factor evaluation**: High-beta stocks outperformed low-beta stocks, with the beta factor recording positive returns [40] - **Factor name**: Alpha factors (multiple) - **Factor construction idea**: Focuses on growth-related metrics and analyst adjustments to predict stock performance [42][46] - **Factor construction process**: 1. Calculate metrics such as single-quarter ROE growth, revenue growth, and analyst forecast adjustments [42][46] 2. Normalize these metrics by market capitalization and industry [41] 3. Aggregate the metrics into individual alpha factors [42][46] - **Factor evaluation**: Alpha factors such as single-quarter ROE growth and analyst forecast adjustments showed strong performance, particularly in small and mid-cap stocks [46][47] - **Model backtesting results** - **Three-dimensional timing framework**: Historical performance indicates stable prediction of market oscillations and downturns [14] - **Factor backtesting results** - **Growth factor**: Weekly long-side excess return of 0.42% [40] - **Size factor**: Weekly long-side excess return of 1.57% [39] - **Beta factor**: Weekly long-side excess return of 1.08% [40] - **Alpha factors**: - Single-quarter ROE growth (considering quick reports and forecasts): Weekly excess return of 1.61%, monthly excess return of 10.17% [44][47] - Analyst forecast adjustment (np_FY1): Weekly excess return of 7.14% in CSI 300, 5.60% in CSI 500, 9.54% in CSI 1000, and 4.19% in CSI 2000 [47] - Single-quarter ROE growth (report): Weekly excess return of 7.47% in CSI 300, 3.84% in CSI 500, 8.11% in CSI 1000, and 3.09% in CSI 2000 [47]

Quantitative Models and Construction 1. Model Name: Three-Dimensional Timing Framework - **Model Construction Idea**: The model integrates three dimensions—divergence, liquidity, and prosperity—to assess market timing and provide investment recommendations[7][13] - **Model Construction Process**: 1. **Divergence**: Measures the degree of disagreement among market participants, reflecting the balance between bullish and bearish sentiments 2. **Liquidity**: Tracks the overall market liquidity trend, indicating the availability of funds in the market 3. **Prosperity**: Evaluates the economic and market growth momentum 4. The model combines these three indicators to generate a composite signal for market timing decisions, such as reducing positions during a "divergence up, liquidity down" scenario[7][13] - **Model Evaluation**: The model provides a systematic and multi-dimensional approach to market timing, offering insights into market trends and potential risks[7][13] --- Quantitative Factors and Construction 1. Factor Name: Size Factor - **Factor Construction Idea**: Captures the performance difference between large-cap and small-cap stocks[39] - **Factor Construction Process**: 1. Define the market capitalization of stocks 2. Construct portfolios based on size rankings 3. Measure the return spread between large-cap and small-cap portfolios[39] - **Factor Evaluation**: The size factor recorded a positive return of 1.57% in the past week, indicating that large-cap stocks outperformed small-cap stocks during this period[39][43] 2. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements[40] - **Factor Construction Process**: 1. Calculate the beta of individual stocks using historical return data 2. Construct portfolios based on beta rankings 3. Measure the return spread between high-beta and low-beta portfolios[40] - **Factor Evaluation**: The beta factor achieved a return of 1.08% in the past week, suggesting that high-beta stocks outperformed low-beta stocks[40][43] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Identifies stocks with high growth potential based on financial metrics[40] - **Factor Construction Process**: 1. Use metrics such as revenue growth, earnings growth, and other growth-related indicators 2. Construct portfolios based on growth rankings 3. Measure the return spread between high-growth and low-growth portfolios[40] - **Factor Evaluation**: The growth factor recorded a return of 0.42% in the past week, indicating that high-growth stocks slightly outperformed their low-growth counterparts[40][43] 4. Factor Name: Single-Quarter ROE YoY Difference (ROE_Q_Delta) - **Factor Construction Idea**: Measures the year-over-year change in return on equity (ROE) for a single quarter, reflecting profitability trends[46][47] - **Factor Construction Process**: 1. Calculate the ROE for the current quarter and the same quarter in the previous year 2. Compute the difference between the two values 3. Construct portfolios based on the ROE YoY difference rankings[46][47] - **Factor Evaluation**: This factor performed well across various indices, with a multi-week excess return of 8.23% in the CSI 300 index and 9.38% in the CSI 1000 index[46][47] 5. Factor Name: Revenue Growth YoY (YOY_OR) - **Factor Construction Idea**: Tracks the year-over-year growth in revenue, highlighting companies with strong top-line growth[42][44] - **Factor Construction Process**: 1. Calculate the revenue growth rate for the current period compared to the same period in the previous year 2. Construct portfolios based on revenue growth rankings 3. Measure the return spread between high-growth and low-growth portfolios[42][44] - **Factor Evaluation**: The factor achieved a weekly excess return of 2.14% and a monthly excess return of 6.48%, demonstrating strong performance in identifying growth opportunities[42][44] --- Backtesting Results of Models and Factors 1. Three-Dimensional Timing Framework - **Annualized Excess Return**: 13.5% since 2018 - **IR**: 1.7 - **Weekly Absolute Return**: 0.9% - **Weekly Excess Return**: -1% relative to equal-weighted industry benchmarks[35][38] 2. Size Factor - **Weekly Return**: 1.57% - **Monthly Return**: 4.70% - **Year-to-Date Return**: -29.21%[43] 3. Beta Factor - **Weekly Return**: 1.08% - **Monthly Return**: 2.99% - **Year-to-Date Return**: 27.49%[43] 4. Growth Factor - **Weekly Return**: 0.42% - **Monthly Return**: 4.11% - **Year-to-Date Return**: -3.28%[43] 5. Single-Quarter ROE YoY Difference (ROE_Q_Delta) - **Weekly Excess Return**: 8.23% (CSI 300), 9.38% (CSI 1000) - **Monthly Excess Return**: 10.17% (CSI 1000)[46][47] 6. Revenue Growth YoY (YOY_OR) - **Weekly Excess Return**: 2.14% - **Monthly Excess Return**: 6.48%[42][44]