量化周报 市场支撑较强 glmszqdatemark 2025 年 12 月 14 日 相关研究 本公司具备证券投资咨询业务资格，请务必阅读最后一页免责声明 证券研究报告 1 择时观点：市场支撑较强。当下流动性继续下行，分歧度保持下行趋势，景气度 处上行趋势，三维择时框架转为一致上涨判断。从技术形态来看，沪深 300 在经 历典型跌破支撑-自动反弹-二次测试后整体表现较强，12 月 12 日下探支撑线后 回升再次站稳，筹码整体供应有限。 指数监测：国债及政金债指数大幅流入。近 1 周国债及政金债 0-3、绿色电力、 国资央企 50、通信设备主题、凤凰 50 等大幅流入。近 1 周家居家电、全指航空、 中证 A 股、卫星产业、创业板综份额流出最多。 1． 基本面选股组合月报：AEG 估值潜力组合今 年实现 11.85%超额收益-2025/12/09 2． 量化周报：三维择时框架转多-2025/12/07 3． 资产配置月报 202512：中游制造哪些板块 CAPEX 最克制？-2025/12/07 4． 量化大势研判 202512：继续增配低估值质 量类资产-2025/12/03 5． 社融预测月报：2025 ...

- 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 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 - **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]