Quantitative Models and Construction Methods 1. Model Name: Hotspot Trend ETF Strategy - **Model Construction Idea**: This strategy identifies ETFs with strong upward trends in both their highest and lowest prices, further refining the selection based on the steepness of their 20-day regression coefficients. It aims to capture short-term market attention and construct a risk-parity portfolio[32] - **Model Construction Process**: 1. Select ETFs where both the highest and lowest prices exhibit an upward trend 2. Calculate the regression coefficients of the highest and lowest prices over the past 20 days 3. Construct a support-resistance factor based on the steepness of these coefficients 4. Choose the top 10 ETFs from the factor's long group with the highest 5-day turnover rate/20-day turnover rate ratio 5. Construct a risk-parity portfolio using these ETFs[32] - **Model Evaluation**: The strategy has demonstrated significant excess returns over the benchmark index, indicating its effectiveness in capturing market trends[32] 2. Model Name: All-Weather Strategy - **Model Construction Idea**: This strategy aims to achieve stable returns by avoiding reliance on predictions, leverage, or macroeconomic assumptions. It uses diversified asset allocation, risk adjustment, and structural hedging to smooth volatility[44] - **Model Construction Process**: 1. **Asset Selection**: Diversify across equities, bonds, and commodities 2. **Risk Adjustment**: Balance risk exposure across asset classes 3. **Structural Hedging**: Implement multi-layered hedging to mitigate risks 4. Divide the portfolio into two versions: High-Volatility (High-Vol) and Low-Volatility (Low-Vol) 5. High-Vol employs a four-layer structure focusing on equity, bond, and gold risk parity, while Low-Vol uses a five-layer structure emphasizing risk budgeting[44][53] - **Model Evaluation**: The strategy has shown consistent performance with low drawdowns and high Sharpe ratios, particularly in the Low-Vol version[53] --- Model Backtesting Results 1. Hotspot Trend ETF Strategy - **2025 Performance**: Total return of 58.34%, with an excess return of 38.80% over the CSI 300 Index[32] 2. All-Weather Strategy - **High-Vol Version**: - Annualized Return (2025): 11.8% - Maximum Drawdown: 3.6% - Sharpe Ratio: 1.9 - 2026 YTD Return: 1.9%[53] - **Low-Vol Version**: - Annualized Return (2025): 6.7% - Maximum Drawdown: 2.0% - Sharpe Ratio: 2.4 - 2026 YTD Return: 1.1%[53] --- Quantitative Factors and Construction Methods 1. Factor Name: Return Std 1M - **Factor Construction Idea**: Measures the standard deviation of returns over the past month to capture volatility trends[61] - **Factor Construction Process**: 1. Calculate daily returns for the past 1 month 2. Compute the standard deviation of these returns 3. Normalize the factor by market capitalization and industry[61] - **Factor Evaluation**: Demonstrates strong stock selection ability with stable excess returns[61] 2. Factor Name: Turnover Mean 1M - **Factor Construction Idea**: Uses the average turnover rate over the past month to identify stocks with high liquidity and market attention[61] - **Factor Construction Process**: 1. Calculate daily turnover rates for the past 1 month 2. Compute the average turnover rate 3. Normalize the factor by market capitalization and industry[61] - **Factor Evaluation**: Exhibits robust performance in identifying high-liquidity stocks with consistent excess returns[61] 3. Factor Name: FY1 Net Profit Change (1M) - **Factor Construction Idea**: Tracks changes in consensus forecasts for net profit (FY1) over the past month to gauge market sentiment[63] - **Factor Construction Process**: 1. Obtain consensus net profit forecasts for FY1 from 1 month ago and the current period 2. Calculate the percentage change: $ \text{Change} = \frac{\text{Current FY1 Forecast} - \text{1 Month Ago FY1 Forecast}}{\text{1 Month Ago FY1 Forecast}} $ 3. Normalize the factor by market capitalization and industry[63] - **Factor Evaluation**: Particularly effective in small-cap indices, reflecting market sensitivity to profit changes[63] --- Factor Backtesting Results 1. Return Std 1M - **1-Week Excess Return**: 1.27% - **1-Month Excess Return**: 1.14%[62] 2. Turnover Mean 1M - **1-Week Excess Return**: 1.26% - **1-Month Excess Return**: 1.12%[62] 3. FY1 Net Profit Change (1M) - **Excess Return in CSI 300**: 16.99% - **Excess Return in CSI 500**: 16.98% - **Excess Return in CSI 800**: 25.58% - **Excess Return in CSI 1000**: 9.70%[64]

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]

- The report constructs an ETF hotspot trend strategy based on the highest and lowest price trends of ETFs, selecting those with both highest and lowest prices in an upward trend. Further, it constructs a support-resistance factor based on the relative steepness of the regression coefficients of the highest and lowest prices over the past 20 days, and selects the top 10 ETFs with the highest turnover rate in the past 5 days/20 days to construct a risk parity portfolio[27][30] - The report tracks the performance of various style factors, noting that the value factor recorded a positive return of 2.36%, the leverage factor recorded a positive return of 1.08%, and the volatility factor slightly rebounded with a return of 0.19%[41][42] - The report evaluates the performance of different alpha factors, highlighting that the quick ratio factor had the best performance with a weekly excess return of 1.32%, followed by the debt-asset ratio factor with a weekly excess return of 1.21%, and the earnings variability over 5 years factor with a weekly excess return of 1.04%[44][46][47] - The ETF hotspot trend strategy recorded a cumulative excess return over the CSI 300 index since the beginning of the year[28][29] - The value factor achieved a weekly return of 2.36%, the leverage factor achieved a weekly return of 1.08%, and the volatility factor achieved a weekly return of 0.19%[41][42] - The quick ratio factor achieved a weekly excess return of 1.32%, the debt-asset ratio factor achieved a weekly excess return of 1.21%, and the earnings variability over 5 years factor achieved a weekly excess return of 1.04%[44][46][47]

Quantitative Models and Construction - **Model Name**: Three-dimensional Timing Framework **Construction Idea**: The model integrates liquidity, divergence, and prosperity indicators to assess market timing and trends[7][12][14] **Construction Process**: 1. Liquidity indicator measures market liquidity trends[17] 2. Divergence indicator tracks market disagreement levels[16] 3. Prosperity indicator evaluates market sentiment and economic activity[19] 4. Combine these three dimensions into a unified framework to predict market movements[12][14] **Evaluation**: The model shows historical effectiveness in identifying market support levels and timing trends[7][14] - **Model Name**: ETF Hotspot Trend Strategy **Construction Idea**: Select ETFs based on price movement patterns and market attention to construct a risk-parity portfolio[25][26] **Construction Process**: 1. Identify ETFs with simultaneous upward trends in highest and lowest prices[25] 2. Calculate regression coefficients of price movements over the past 20 days to construct support-resistance factors[25] 3. Select top 10 ETFs with the highest turnover ratio (5-day/20-day) for portfolio construction[25] **Evaluation**: The strategy demonstrates cumulative excess returns over the CSI 300 index[26] - **Model Name**: Capital Flow Resonance Strategy **Construction Idea**: Combine financing and large-order capital flows to identify industries with strong capital resonance[29][33] **Construction Process**: 1. Define financing factor as the net financing buy minus net financing sell, neutralized by Barra market capitalization[33] 2. Define large-order factor as net inflow sorted by industry and neutralized by one-year trading volume[33] 3. Combine the two factors, excluding extreme industries and large financial sectors, to enhance strategy stability[33][36] **Evaluation**: The strategy achieves annualized excess returns of 13.5% since 2018, with an IR of 1.7[33] Model Backtesting Results - **Three-dimensional Timing Framework**: Historical performance indicates effective identification of market support levels and timing trends[14] - **ETF Hotspot Trend Strategy**: Cumulative excess return over CSI 300 index observed since the beginning of the year[26] - **Capital Flow Resonance Strategy**: - Annualized excess return: 13.5% since 2018 - IR: 1.7 - Weekly absolute return: 2.86% - Weekly excess return: 0.19%[33] Quantitative Factors and Construction - **Factor Name**: Beta **Construction Idea**: Measure stock sensitivity to market movements[39] **Construction Process**: Calculate stock beta using historical price data and market index movements[39] **Evaluation**: High-beta stocks outperform low-beta stocks, achieving 3.05% weekly return[39] - **Factor Name**: Momentum **Construction Idea**: Capture the continuation of stock price trends[39] **Construction Process**: Calculate momentum based on past price performance over a defined period[39] **Evaluation**: Momentum factor records 1.28% weekly return, indicating strong performance of previously high-performing stocks[39] - **Factor Name**: Liquidity **Construction Idea**: Assess market preference for high-liquidity stocks[39] **Construction Process**: Measure liquidity using trading volume and turnover ratios[39] **Evaluation**: Liquidity factor achieves 2.06% weekly return, reflecting market favorability for liquid stocks[39] - **Factor Name**: Illiquidity (Illia) **Construction Idea**: Evaluate stock price impact driven by large trading volumes[44][45] **Construction Process**: Measure daily price changes driven by trading volumes exceeding one billion[45] **Evaluation**: Illiquidity factor achieves 1.48% weekly excess return and 2.11% monthly excess return[45] - **Factor Name**: Volume Mean and Standard Deviation **Construction Idea**: Analyze trading volume trends over different time windows[44][45] **Construction Process**: 1. Calculate mean and standard deviation of trading volumes over 1-month, 3-month, 6-month, and 12-month windows[45] 2. Normalize and rank stocks based on these metrics[45] **Evaluation**: Volume-related factors show consistent positive excess returns across different time windows, with weekly returns ranging from 0.64% to 0.99%[45] - **Factor Name**: R&D Intensity **Construction Idea**: Measure the proportion of R&D expenditure relative to sales revenue[45] **Construction Process**: Calculate R&D expenses divided by total sales revenue[45] **Evaluation**: R&D intensity factor records 0.59% weekly excess return and 0.67% monthly excess return[45] Factor Backtesting Results - **Beta Factor**: Weekly return: 3.05%[39] - **Momentum Factor**: Weekly return: 1.28%[39] - **Liquidity Factor**: Weekly return: 2.06%[39] - **Illiquidity Factor**: Weekly excess return: 1.48%, Monthly excess return: 2.11%[45] - **Volume Mean and Standard Deviation Factors**: Weekly returns range from 0.64% to 0.99%, Monthly returns range from 1.49% to 2.29%[45] - **R&D Intensity Factor**: Weekly excess return: 0.59%, Monthly excess return: 0.67%[45]

Quantitative Models and Construction - **Model Name**: Three-dimensional Timing Framework **Construction Idea**: Combines liquidity, divergence, and prosperity metrics to assess market timing and trends[7][14][19] **Construction Process**: 1. Define liquidity index, divergence index, and prosperity index 2. Combine these metrics into a three-dimensional framework to evaluate market conditions 3. Historical performance analysis shows its effectiveness in predicting market trends[7][14][19] **Evaluation**: Provides a comprehensive view of market timing by integrating multiple dimensions[7][14][19] - **Model Name**: ETF Hotspot Trend Strategy **Construction Idea**: Identifies ETFs with strong short-term market attention and constructs a risk-parity portfolio[30][31] **Construction Process**: 1. Select ETFs with simultaneous upward trends in highest and lowest prices 2. Use regression coefficients of the past 20 days to construct support-resistance factors 3. Choose top 10 ETFs with the highest turnover rates in the past 5 and 20 days 4. Build a risk-parity portfolio based on these ETFs[30][31] **Evaluation**: Effectively captures short-term market hotspots and enhances portfolio stability[30][31] - **Model Name**: Capital Flow Resonance Strategy **Construction Idea**: Combines financing and large-order capital flows to identify industries with strong resonance effects[33][35][38] **Construction Process**: 1. Define financing factor: Neutralize market capitalization and calculate the 50-day average of financing net buy minus net sell 2. Define large-order factor: Neutralize industry transaction volume and calculate the 10-day average of net inflows 3. Combine the two factors, excluding extreme industries and large financial sectors 4. Backtest results show annualized excess return of 13.5% and IR of 1.7 since 2018[33][35][38] **Evaluation**: Improves strategy stability by combining complementary factors[33][35][38] Model Backtesting Results - **Three-dimensional Timing Framework**: Historical performance demonstrates its ability to predict market trends effectively[14][19] - **ETF Hotspot Trend Strategy**: Weekly portfolio includes ETFs such as Hong Kong non-bank finance and communication equipment, showing strong market attention[30][31] - **Capital Flow Resonance Strategy**: Achieved absolute return of 0.3% and excess return of -1.7% last week[35][38] Quantitative Factors and Construction - **Factor Name**: Momentum **Construction Idea**: Measures stock price trends over a specific period[41][43] **Construction Process**: 1. Calculate 1-year minus 1-month return (mom_1y_1m) 2. Rank stocks based on momentum scores and construct portfolios[41][43] **Evaluation**: High-momentum stocks significantly outperform low-momentum stocks[41][43] - **Factor Name**: Liquidity **Construction Idea**: Evaluates stock liquidity and its impact on returns[41][43] **Construction Process**: 1. Define liquidity factor (liquidity) 2. Rank stocks based on liquidity scores and construct portfolios[41][43] **Evaluation**: High-liquidity stocks outperform low-liquidity stocks[41][43] - **Factor Name**: Value **Construction Idea**: Assesses stock valuation levels[41][43] **Construction Process**: 1. Define value factor (value) 2. Rank stocks based on valuation scores and construct portfolios[41][43] **Evaluation**: Low-valuation stocks underperform high-valuation stocks recently[41][43] - **Factor Name**: Alpha Factors (e.g., yoy_accpayable, yoy_or_q, cur_liab_yoy) **Construction Idea**: Measures financial metrics such as growth rates and profitability[45][47][49] **Construction Process**: 1. Calculate metrics like accounts payable growth (yoy_accpayable), quarterly revenue growth (yoy_or_q), and current liabilities growth (cur_liab_yoy) 2. Neutralize market capitalization and industry effects 3. Rank stocks based on factor scores and construct portfolios[45][47][49] **Evaluation**: Factors show strong excess returns, especially in large-cap stocks[45][47][49] Factor Backtesting Results - **Momentum Factor**: Weekly excess return of +2.05%[41][43] - **Liquidity Factor**: Weekly excess return of +3.38%[41][43] - **Value Factor**: Weekly excess return of -2.41%[41][43] - **Alpha Factors**: - yoy_accpayable: Weekly excess return of +3.51%[45][47] - yoy_or_q: Weekly excess return of +3.49%[45][47] - cur_liab_yoy: Weekly excess return of +3.37%[45][47] - roe_q_delta_adv: Weekly excess return of +2.80%[45][49]