证券研究报告 | 金融工程 gszqdatemark 2026 01 10 年 月 日 本周综合打分。本周市场的拥挤度&反转分数、估值性价比分数下降，宏 观基本面分数、趋势&资金分数维持不变，综合打分位于[-1,1]之间，当前 的综合打分为-0.19 分，比上周下降 0.08 分（上周综合分数为-0.11 分）， 维持中性观点。当前六面图各个维度的观点如下： 流动性。本周货币方向、信用方向发出看多信号，货币强度信号中性， 信用强度发出看空信号，当前流动性得分为 0.25 分，综合来看发出中性 偏多信号。 经济面。本周增长强度指标发出看多信号，增长方向、通胀方向、通胀 强度发出看空信号，当前经济面综合得分为-0.50 分，综合来看发出中性 偏空信号。 估值面。本周席勒 ERP 指标下降 0.12 分，PB 指标下降 0.19 分，AIAE 指标下降 0.09 分，当前市场的估值面得分为-0.65 分，综合来看发出中性 偏空信号。 资金面。本周内资的两融增量、成交额趋势、海外风险厌恶指数、外资 的中国主权 CDS 利差全部看多。资金面当前为 1.00 分，综合来看发出看 多信号。 技术面。本周衡量中长期动量的价格 ...

【金工周报】(20260105-20260109) 短期择时信号翻多，后市或乐观向上 证 券 研 究 报 告 本周市场普遍上涨，上证指数单周上涨 3.82%，创业板指单周上涨 3.89%。 A 股模型： 短期：成交量模型所有宽基指数看多。特征龙虎榜机构模型看多。特征成交量 模型看多。智能算法沪深 300 模型看多，智能算法中证 500 模型看多。 中期：涨跌停模型看多。上下行收益差模型所有宽基指数看多。月历效应模型 中性。 ❖ 本周回顾 长期：长期动量模型部分宽基看多。 综合：A 股综合兵器 V3 模型看多。A 股综合国证 2000 模型看多。 港股模型： 中期：成交额倒波幅模型看空。恒生指数上下行收益差模型中性。 本周行业指数普遍上涨，除银行、交通运输外所有行业均上涨，涨幅前五的行 业为：国防军工、传媒、有色金属、计算机、医药。从资金流向角度来说，银 行、传媒、煤炭、石油石化、建筑主力资金净流入居前，电子、通信、基础化 工、国防军工、计算机主力资金净流出居前。 本周股票型基金总仓位为 95.17%，相较于上周减少了 57 个 bps，混合型基金 总仓位 88.28%，相较于上周减少了 13 个 bps。 本 ...

Market-timers hate bonds — it's time to consider buying them. ...

- Short-term volume models for some broad-based indices turned bullish[1][3][11] - Feature-based institutional model turned bullish[1][3][11] - Feature-based volume model remained neutral[1][3][11] - Intelligent algorithm model for CSI 300 remained neutral, while for CSI 500 turned bullish[1][3][11] - Mid-term limit-up and limit-down model turned bullish[1][3][12] - Up-down return difference model turned bullish for all broad-based indices[1][3][12] - Calendar effect model remained neutral[1][3][12] - Long-term momentum model turned bullish for some broad-based indices[1][3][13] - Comprehensive A-share V3 model turned bullish[1][3][13] - Comprehensive A-share Guozheng 2000 model turned bullish[1][3][13] - Mid-term turnover amplitude model for Hong Kong stocks turned bullish[1][3][14] - Hang Seng Index up-down return difference model remained neutral[1][3][14]

- The report discusses multiple quantitative models for market timing, including short-term, medium-term, and long-term models. These models are constructed based on principles such as price-volume relationships, momentum, and calendar effects. The short-term models include the "Volume Model," "Feature Institutional Model," and "Feature Volume Model," while medium-term models include the "Limit-Up/Down Model" and "Up/Down Return Difference Model." The long-term model is the "Long-Term Momentum Model"[8][11][12][13] - The construction process of these models involves combining signals from different time horizons and strategies. For example, the "Volume Model" evaluates market activity through trading volume, while the "Momentum Model" focuses on price trends. The "Limit-Up/Down Model" identifies market sentiment by analyzing the frequency of limit-up and limit-down events. The "Up/Down Return Difference Model" measures the difference between upward and downward returns to gauge market direction[8][11][12] - The evaluation of these models suggests that combining signals from different models enhances robustness. For instance, some models are defensive, while others are aggressive, allowing for a balanced approach. The report emphasizes that simplicity in model design often leads to better generalization and performance[8][11][12] - Backtesting results for these models indicate varying levels of effectiveness. For example, the "Long-Term Momentum Model" is currently bullish, while the "Up/Down Return Difference Model" shows a positive outlook across all broad-based indices. The "Feature Institutional Model" is bullish, whereas the "Feature Volume Model" is bearish. The "Volume Model" remains neutral across all indices[11][12][13]

- The report discusses multiple quantitative models for A-share and Hong Kong stock markets, including short-term, medium-term, and long-term models. These models are constructed based on price-volume, momentum, acceleration, and trend perspectives, among others. The report emphasizes the importance of combining signals from different models and periods to achieve a balanced strategy[9][12][13] - For A-shares, the short-term models include the "Volume Model" (neutral for all broad-based indices), "Feature Institutional Model" (bearish), "Feature Volume Model" (bearish), and "Smart Algorithm Models" (neutral for CSI 300, bullish for CSI 500)[12][71] - Medium-term A-share models include the "Limit-Up and Limit-Down Model" (neutral), "Up-Down Return Difference Model" (bullish for all broad-based indices), and "Calendar Effect Model" (neutral)[13][72] - The long-term A-share model, "Long-Term Momentum Model," is bullish[14][73] - Comprehensive A-share models, such as "A-Share Comprehensive Weapon V3 Model" and "A-Share Comprehensive Guozheng 2000 Model," are bearish[15][74] - For Hong Kong stocks, the medium-term models include the "Turnover to Volatility Model" (bearish) and "Hang Seng Index Up-Down Return Difference Model" (neutral)[16][74] - The report highlights that the quantitative models are designed to provide market timing signals and are based on historical data, emphasizing simplicity and universality in their construction[9][12] - The backtesting results for the "Double Bottom Pattern" and "Cup and Handle Pattern" show that the double bottom pattern outperformed the Shanghai Composite Index by 1.93% this week, while the cup and handle pattern outperformed by 2.5%[44][50] - The cumulative performance of the double bottom pattern since December 31, 2020, is 13.99%, outperforming the Shanghai Composite Index by 2.02%. However, the cup and handle pattern underperformed the Shanghai Composite Index by -1.14% over the same period[44][50]

- The report includes multiple quantitative models for market timing, categorized into short-term, mid-term, and long-term models, such as the "Volume Model," "Feature Volume Model," "Smart Algorithm Model," "Up-Down Return Difference Model," "Calendar Effect Model," and "Long-Term Momentum Model" [1][11][12][13][63][64] - The "Volume Model" is constructed based on trading volume data to predict market trends, while the "Feature Volume Model" incorporates specific volume characteristics to enhance prediction accuracy [11][63] - The "Smart Algorithm Model" uses machine learning techniques to analyze historical data and generate market timing signals [11][63] - The "Up-Down Return Difference Model" calculates the difference between upward and downward returns to assess market sentiment [12][63] - The "Calendar Effect Model" leverages historical seasonal patterns to predict market movements [12][63] - The "Long-Term Momentum Model" focuses on long-term price trends to identify potential upward movements [13][64] - The report evaluates the models as effective tools for market timing, emphasizing their simplicity and universality, aligning with the principle of "大道至简" (great simplicity) [8][63] - The models are tested across various indices, including the Shanghai Composite Index, CSI 300, and CSI 500, with signals ranging from neutral to bearish for short-term models and bullish for long-term models [11][63][64] - The "Volume Model" shows neutral signals across all broad-based indices [11][63] - The "Feature Volume Model" indicates bearish signals for the CSI 500 and All-A indices [11][63] - The "Smart Algorithm Model" provides neutral signals for the CSI 300 and bearish signals for the CSI 500 [11][63] - The "Up-Down Return Difference Model" transitions from bullish to neutral for the CSI 2000 and All-A indices [12][63] - The "Long-Term Momentum Model" maintains bullish signals for long-term market trends [13][64]

Quantitative Models and Construction Methods - **Model Name**: Timing Radar Six-Dimensional Framework **Model Construction Idea**: The equity market is influenced by multiple dimensions of factors. This model selects 21 indicators from six dimensions: liquidity, economic fundamentals, valuation, capital flow, technicals, and crowding. These are summarized into four categories: "valuation cost-effectiveness," "macro fundamentals," "capital & trend," and "crowding & reversal," generating a composite timing score between [-1, 1][1][6][8] **Model Construction Process**: 1. Select 21 indicators across six dimensions 2. Group indicators into four categories: - Valuation cost-effectiveness - Macro fundamentals - Capital & trend - Crowding & reversal 3. Normalize the composite score to a range of [-1, 1] **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, offering a balanced perspective on market timing[1][6][8] --- Quantitative Factors and Construction Methods Liquidity Factors 1. **Factor Name**: Monetary Direction Factor **Factor Construction Idea**: Measures the direction of monetary policy using central bank policy rates and short-term market rates[10] **Factor Construction Process**: - Calculate the average change in policy rates and short-term market rates over the past 90 days - If the factor > 0, monetary policy is considered expansionary; if < 0, it is contractionary **Factor Evaluation**: Effectively captures monetary policy direction[10] 2. **Factor Name**: Monetary Intensity Factor **Factor Construction Idea**: Based on the "interest rate corridor" concept, measures the deviation of short-term market rates from policy rates[12] **Factor Construction Process**: - Calculate deviation = DR007/7-year reverse repo rate - 1 - Smooth and z-score the deviation to form the factor - If the factor < -1.5 standard deviations, it indicates a loose environment; if > 1.5, it indicates a tight environment **Factor Evaluation**: Captures the relative intensity of monetary policy[12] 3. **Factor Name**: Credit Direction Factor **Factor Construction Idea**: Reflects the transmission of credit to the real economy using medium- and long-term loan data[15] **Factor Construction Process**: - Calculate the year-on-year growth of medium- and long-term loans over the past 12 months - If the factor rises compared to three months ago, it signals a positive trend; otherwise, negative **Factor Evaluation**: Provides insights into credit flow trends[15] 4. **Factor Name**: Credit Intensity Factor **Factor Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[18] **Factor Construction Process**: - Calculate (new RMB loans - median forecast)/forecast standard deviation - If the factor > 1.5 standard deviations, it indicates a significantly positive credit environment; if < -1.5, it indicates a negative environment **Factor Evaluation**: Captures unexpected credit changes effectively[18] Economic Factors 1. **Factor Name**: Growth Direction Factor **Factor Construction Idea**: Based on PMI data, measures the direction of economic growth[20] **Factor Construction Process**: - Calculate the 12-month moving average of PMI and its year-on-year change - If the factor rises compared to three months ago, it signals a positive trend; otherwise, negative **Factor Evaluation**: Reflects economic growth trends accurately[20] 2. **Factor Name**: Growth Intensity Factor **Factor Construction Idea**: Measures whether economic growth data significantly exceeds or falls short of expectations[23] **Factor Construction Process**: - Calculate (PMI - median forecast)/forecast standard deviation - If the factor > 1.5 standard deviations, it indicates significantly positive growth; if < -1.5, it indicates negative growth **Factor Evaluation**: Captures unexpected economic growth changes effectively[23] 3. **Factor Name**: Inflation Direction Factor **Factor Construction Idea**: Measures the direction of inflation using CPI and PPI data[25] **Factor Construction Process**: - Calculate 0.5 × smoothed CPI year-on-year + 0.5 × raw PPI year-on-year - If the factor decreases compared to three months ago, it signals a deflationary environment; otherwise, inflationary **Factor Evaluation**: Reflects inflation trends effectively[25] 4. **Factor Name**: Inflation Intensity Factor **Factor Construction Idea**: Measures whether inflation data significantly exceeds or falls short of expectations[27] **Factor Construction Process**: - Calculate (CPI or PPI - median forecast)/forecast standard deviation - If the factor < -1.5, it indicates significantly lower-than-expected inflation; if > 1.5, it indicates higher-than-expected inflation **Factor Evaluation**: Captures unexpected inflation changes effectively[27] Valuation Factors 1. **Factor Name**: Shiller ERP **Factor Construction Idea**: Adjusts for economic cycles to measure equity risk premium[28] **Factor Construction Process**: - Calculate Shiller PE using 6-year inflation-adjusted average earnings - Compute ERP = 1/Shiller PE - 10-year government bond yield - Normalize using a 6-year z-score **Factor Evaluation**: Provides a cyclically adjusted view of equity valuation[28] 2. **Factor Name**: PB **Factor Construction Idea**: Measures valuation using price-to-book ratio[31] **Factor Construction Process**: - Multiply PB by -1 and normalize using a 6-year z-score - Standardize to ±1 after 1.5 standard deviation truncation **Factor Evaluation**: Offers a straightforward valuation metric[31] 3. **Factor Name**: AIAE **Factor Construction Idea**: Reflects market-wide equity allocation and risk appetite[33] **Factor Construction Process**: - Calculate AIAE = total market cap of CSI All Share/(total market cap + total corporate debt) - Multiply AIAE by -1 and normalize using a 6-year z-score **Factor Evaluation**: Captures market risk appetite effectively[33] Capital Flow Factors 1. **Factor Name**: Margin Trading Increment **Factor Construction Idea**: Measures market leverage through margin trading trends[36] **Factor Construction Process**: - Calculate the difference between financing and short-selling balances - Compare the 120-day average increment with the 240-day average increment - If the 120-day increment > 240-day increment, it signals a positive trend; otherwise, negative **Factor Evaluation**: Reflects market sentiment and leverage trends[36] 2. **Factor Name**: Turnover Trend **Factor Construction Idea**: Measures market activity through turnover trends[39] **Factor Construction Process**: - Calculate log turnover moving average distance = ma120/ma240 - 1 - If max(10, 30, 60-day moving averages) is positive, it signals a positive trend; otherwise, negative **Factor Evaluation**: Captures market activity effectively[39] 3. **Factor Name**: China Sovereign CDS Spread **Factor Construction Idea**: Reflects foreign investors' perception of China's economic and credit risk[43] **Factor Construction Process**: - Calculate the 20-day difference of smoothed CDS spreads - If the difference < 0, it signals a positive trend; otherwise, negative **Factor Evaluation**: Provides insights into foreign capital flow trends[43] 4. **Factor Name**: Overseas Risk Aversion Index **Factor Construction Idea**: Captures global market risk appetite using Citi RAI Index[45] **Factor Construction Process**: - Calculate the 20-day difference of smoothed RAI - If the difference < 0, it signals a positive trend; otherwise, negative **Factor Evaluation**: Reflects global risk sentiment effectively[45] Technical Factors 1. **Factor Name**: Price Trend **Factor Construction Idea**: Measures market trend direction and strength using moving averages[47] **Factor Construction Process**: - Calculate moving average distance = ma120/ma240 - 1 - Compute trend direction and strength scores, then average them **Factor Evaluation**: Captures market trend dynamics effectively[47] 2. **Factor Name**: New Highs and Lows **Factor Construction Idea**: Uses the difference between new highs and lows as a reversal signal[49] **Factor Construction Process**: - Calculate the 20-day moving average of new lows - new highs - If the value > 0, it signals a bottoming market; otherwise, a topping market **Factor Evaluation**: Provides reversal signals effectively[49] Crowding Factors 1. **Factor Name**: Implied Premium/Discount **Factor Construction Idea**: Derived from option pricing, reflects market sentiment[53] **Factor Construction Process**: - Use the put-call parity to calculate implied premium/discount

- The short-term trading volume model is neutral for A-shares[2][12] - The low volatility model is neutral for A-shares[2][12] - The characteristic institutional model is bearish for A-shares[2][12] - The characteristic trading volume model is bearish for A-shares[2][12] - The intelligent algorithm model for the CSI 300 is bearish for A-shares[2][12] - The intelligent algorithm model for the CSI 500 is bearish for A-shares[2][12] - The mid-term limit-up and limit-down model is neutral for A-shares[2][13] - The mid-term calendar effect model is neutral for A-shares[2][13] - The long-term momentum model is bullish for A-shares[2][14] - The comprehensive A-share model V3 is bearish[2][15] - The comprehensive A-share model for the CSI 2000 is bearish[2][15] - The mid-term trading volume to volatility model is bearish for Hong Kong stocks[2][16]

Core Viewpoint - The market is showing its first bearish signal in half a year, with a recommendation to reduce positions in the main board and small-cap sectors to low levels, indicating a potential shift in market dynamics [5]. Market Performance - Last week, the market recorded gains, with the CSI 300 index up 1.38%, the Shanghai Composite Index up 1.52%, and the CSI 500 index up 3.38%. Speculative funds became active again, pushing various sector indices to their highs before August [3]. Economic Indicators - The domestic economy is showing signs of weakening while inflation is rising. Recent import and export data showed significant weakness, which has hindered the enthusiasm of some institutional investors. Financial data released last Friday appeared stable but is actually weakening, with expectations of a slowdown in year-on-year growth in the coming months. Meanwhile, CPI and PPI data have shown a rebound, indicating a temporary stagflation cycle in the Chinese economy [3][4]. Technical Analysis - There is a deepening divergence in the funding landscape. While the market has been driven by funds since June, institutional funds have shown a more decisive reduction in positions, while speculative funds are attempting a final upward push. The strength of technical signals has weakened [4]. Sector Positioning - The main board's market-driving forces are becoming increasingly differentiated, shifting from fundamentals to funds, and then to speculative funds. This indicates that market volatility is likely to increase further. The recommendation is to reduce positions in the main board to low levels, marking the first sell signal in half a year. The small-cap sector also showed slight advantages due to speculative activity, but overall differentiation has increased, suggesting a balanced style for the time being [5]. Short-term Focus - The short-term momentum model suggests focusing on the communication industry [5].