金融工程 短期：成交量模型所有宽基指数中性。特征龙虎榜机构模型中性。特征成交量 模型看空。智能算法沪深 300 模型中性，智能算法中证 500 模型看空。 中期：涨跌停模型中性。上下行收益差模型国证 2000 指数与全 A 指数信号由 看多转为中性。月历效应模型中性。 长期：长期动量模型看多。 综合：A 股综合兵器 V3 模型看空。A 股综合国证 2000 模型看空。 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。 证 券 研 究 报 告 【金工周报】（20251117-20251121） 指数信号整体中性偏空，短期震荡偏空 ❖ 本周回顾 本周市场普遍下跌，上证指数单周下跌 3.9%，创业板指单周下跌 6.15%。 A 股模型： 本周行业指数全线下跌，跌幅前五的行业为：综合、电力设备及新能源、基础 化工、综合金融、钢铁。从资金流向角度来说，所有行业主力资金净流出，其 中电子、电力设备及新能源、基础化工、医药、机械主力资金净流出居前。 本周股票型基金总仓位为 95.82%，相较于上周减少了 52 个 bps，混合型基金 总仓位 88.71%，相较于上周减少了 218 个 bps。 本周汽 ...

证券研究报告 | 金融工程 gszqdatemark 2025 11 15 年 月 日 量化分析报告 择时雷达六面图：本周资金面好转 择时雷达六面图：基于多维视角的择时框架。权益市场的表现受到多维度 指标因素的共同影响，我们尝试从流动性、经济面、估值面、资金面、技 术面、拥挤度选取二十一个指标对市场进行刻画，并将其概括为"估值性 价比"、"宏观基本面"、"资金&趋势"、"拥挤度&反转"四大类，从而生成 [-1,1]之间的综合择时分数。 本周综合打分。本周市场的估值性价比、趋势 &资金分数上升，宏观基本 面、拥挤度&反转分数下降，综合打分位于[-1,1]之间，当前的综合打分为 0.03 分，整体为中性观点。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。本周增长方向指标发出看多信号，通胀方向、通胀强度发出看 空信号，当前经济面综合得分为-0.25 分，综合来看发出中性偏空信号。 估值面。本周席勒 ERP、PB、AIAE 指标分数均上升，当前市场的估 值面得分为-0.47 分 ...

- 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].

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model uses trading volume as a key indicator to predict market trends in the short term[12][65] **Construction Process**: The model evaluates the trading volume of broad-based indices to generate buy or sell signals. A higher trading volume relative to historical averages indicates a "bullish" signal, while lower volumes may indicate neutrality or bearishness[12][65] **Evaluation**: The model is effective in capturing short-term market momentum and is widely applicable across broad indices[12][65] - **Model Name**: Low Volatility Model **Construction Idea**: This model focuses on the volatility of indices to assess market stability and predict trends[12][65] **Construction Process**: The model calculates the historical volatility of indices over a defined period. If the volatility is low, the model remains neutral, indicating a stable market environment[12][65] **Evaluation**: The model is useful for identifying periods of market stability but may lack predictive power during high-volatility phases[12][65] - **Model Name**: Institutional Feature Model (Top Trader) **Construction Idea**: This model analyzes institutional trading patterns to predict market movements[12][65] **Construction Process**: The model tracks the trading activity of institutional investors, particularly their buying and selling patterns. A high level of institutional selling generates a "bearish" signal[12][65] **Evaluation**: The model provides insights into institutional sentiment but may be less effective in retail-dominated markets[12][65] - **Model Name**: Momentum Model **Construction Idea**: This model leverages price momentum to predict long-term market trends[14][67] **Construction Process**: The model calculates the rate of price change over a long-term horizon. Positive momentum generates a "bullish" signal, while negative momentum indicates bearishness[14][67] **Evaluation**: The model is effective in identifying long-term trends but may lag during sudden market reversals[14][67] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This is a composite model that integrates multiple signals across different time horizons[15][68] **Construction Process**: The model combines short-term, medium-term, and long-term signals from various sub-models (e.g., volume, momentum, institutional features) to generate an overall market outlook[15][68] **Evaluation**: The model balances short-term and long-term perspectives, making it robust for comprehensive market analysis[15][68] - **Model Name**: Hang Seng Turnover-to-Volatility Model **Construction Idea**: This model uses the ratio of turnover to volatility to predict medium-term trends in the Hong Kong market[16][69] **Construction Process**: The model calculates the turnover-to-volatility ratio for the Hang Seng Index. A higher ratio indicates a "bullish" signal, suggesting strong market participation relative to risk[16][69] **Evaluation**: The model is effective in capturing medium-term trends but may be less responsive to short-term fluctuations[16][69] Model Backtesting Results - **Volume Model**: All broad-based indices showed "bullish" signals in the short term[12][65] - **Low Volatility Model**: Neutral signals were observed, indicating stable market conditions[12][65] - **Institutional Feature Model**: Bearish signals were generated due to high institutional selling activity[12][65] - **Momentum Model**: Long-term "bullish" signals were observed, indicating positive price momentum[14][67] - **A-Share Comprehensive Weapon V3 Model**: Overall "bullish" signals were generated, reflecting a positive market outlook[15][68] - **Hang Seng Turnover-to-Volatility Model**: "Bullish" signals were observed, suggesting optimism in the Hong Kong market[16][69]

Quantitative Models and Construction Timing Radar Hexagon Model - **Model Name**: Timing Radar Hexagon Model - **Model Construction Idea**: The model evaluates equity market performance through a multi-dimensional framework, incorporating liquidity, economic fundamentals, valuation, capital flows, technical signals, and crowding indicators. These dimensions are aggregated into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a composite timing score within the range of [-1, 1][1][6][9] - **Model Construction Process**: 1. Select 21 indicators across six dimensions (liquidity, economic fundamentals, valuation, capital flows, technical signals, and crowding)[1][6] 2. Aggregate these indicators into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal"[6] 3. Normalize the composite score to fall within the range of [-1, 1][6] - **Model Evaluation**: The model provides a comprehensive and systematic approach to market timing by integrating multiple dimensions of market dynamics[6] --- Quantitative Factors and Construction Liquidity Factors 1. **Factor Name**: Monetary Direction Factor - **Construction Idea**: Measures the direction of monetary policy based on changes in central bank policy rates and short-term market rates over the past 90 days[12] - **Construction Process**: - Calculate the average change in central bank policy rates and short-term market rates over the past 90 days - If the factor > 0, monetary policy is deemed accommodative; if < 0, it is deemed tight[12] - **Current View**: The factor is positive this week, signaling accommodative monetary policy, with a score of 1[12] 2. **Factor Name**: Monetary Strength Factor - **Construction Idea**: Captures the deviation of short-term market rates from policy rates using the "interest rate corridor" concept[15] - **Construction Process**: - Compute the deviation = DR007/7-year reverse repo rate - 1 - Smooth and z-score the deviation - If the factor < -1.5 standard deviations, it signals a loose environment (score = 1); if > 1.5 standard deviations, it signals a tight environment (score = -1)[15] - **Current View**: The factor signals a tight environment this week, with a score of -1[15] 3. **Factor Name**: Credit Direction Factor - **Construction Idea**: Reflects the transmission of credit to the real economy using medium- and long-term loan data[18] - **Construction Process**: - Calculate the year-over-year growth of the past 12 months' medium- and long-term loan increments - If the factor rises compared to three months ago, it signals a positive trend (score = 1); otherwise, it signals a negative trend (score = -1)[18] - **Current View**: The factor is in an upward trend this week, signaling a positive outlook, with a score of 1[19] 4. **Factor Name**: Credit Strength Factor - **Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[21] - **Construction Process**: - Compute the credit strength factor = (new RMB loans - median forecast) / forecast standard deviation - If the factor > 1.5 standard deviations, it signals a significantly positive credit environment (score = 1); if < -1.5 standard deviations, it signals a negative environment (score = -1)[21] - **Current View**: The factor signals a negative environment this week, with a score of -1[21] --- Backtesting Results of Factors Liquidity Factors 1. **Monetary Direction Factor**: Current score = 1[12] 2. **Monetary Strength Factor**: Current score = -1[15] 3. **Credit Direction Factor**: Current score = 1[19] 4. **Credit Strength Factor**: Current score = -1[21]

- The short-term volume model indicates a bullish outlook for most broad-based indices[2][12][70] - The low volatility model is neutral in the short term[2][12][70] - The institutional model based on the characteristics of the Dragon and Tiger list is bearish in the short term[2][12][70] - The characteristic volume model is bullish in the short term[2][12][70] - The intelligent algorithm models for the CSI 300 and CSI 500 indices are bullish in the short term[2][12][70] - The mid-term limit-up and limit-down model is bullish[2][13][71] - The mid-term calendar effect model is neutral[2][13][71] - The long-term momentum model is bullish[2][14][72] - The comprehensive A-share models, including the A-share Comprehensive Weapon V3 model and the A-share Comprehensive Guozheng 2000 model, are bullish[2][15][73] - The mid-term turnover rate inverse volatility model for Hong Kong stocks is bullish[2][16][74]

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model evaluates market trends based on trading volume changes over time [12][72] **Construction Process**: The model analyzes the trading volume of broad-based indices to determine short-term market sentiment. It transitions between "bullish," "neutral," and "bearish" signals based on volume dynamics [12][72] **Evaluation**: The model is effective in capturing short-term market sentiment but may require integration with other indicators for comprehensive analysis [12][72] - **Model Name**: Low Volatility Model **Construction Idea**: This model assesses market conditions by analyzing the volatility of indices [12][72] **Construction Process**: The model calculates the historical volatility of indices and assigns a "neutral" signal when volatility remains within a predefined range [12][72] **Evaluation**: The model provides a stable perspective on market conditions but may lag in highly volatile environments [12][72] - **Model Name**: Intelligent Algorithm Model (CSI 300 and CSI 500) **Construction Idea**: This model uses machine learning algorithms to predict market trends for specific indices [12][72] **Construction Process**: The model applies advanced algorithms to historical price and volume data, generating "bullish" signals for the CSI 300 and CSI 500 indices [12][72] **Evaluation**: The model demonstrates strong predictive capabilities for these indices, particularly in short-term scenarios [12][72] - **Model Name**: Limit-Up/Limit-Down Model **Construction Idea**: This model evaluates market sentiment based on the frequency of limit-up and limit-down events [13][73] **Construction Process**: The model tracks the number of stocks hitting daily price limits and assigns a "neutral" signal when no significant trend is observed [13][73] **Evaluation**: The model is useful for identifying extreme market conditions but may not capture subtle trends [13][73] - **Model Name**: Long-Term Momentum Model **Construction Idea**: This model identifies long-term trends by analyzing momentum indicators [14][74] **Construction Process**: The model calculates momentum metrics for indices like the SSE 50, which recently transitioned to a "bullish" signal [14][74] **Evaluation**: The model is effective for long-term trend analysis but may miss short-term fluctuations [14][74] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This composite model integrates multiple signals to provide an overall market outlook [15][75] **Construction Process**: The model aggregates signals from various short-term, medium-term, and long-term models, currently indicating a "bearish" outlook [15][75] **Evaluation**: The model offers a holistic view but may dilute the impact of individual signals [15][75] - **Model Name**: HK Stock Turnover-to-Volatility Model **Construction Idea**: This model evaluates the Hong Kong market by analyzing turnover relative to volatility [16][76] **Construction Process**: The model calculates the ratio of turnover to volatility, currently signaling a "bullish" outlook for the Hang Seng Index [16][76] **Evaluation**: The model is effective for medium-term analysis but may require additional factors for short-term predictions [16][76] Model Backtesting Results - **Volume Model**: Short-term signal transitioned to "neutral" for most broad-based indices [12][72] - **Low Volatility Model**: Maintains a "neutral" signal [12][72] - **Intelligent Algorithm Model**: "Bullish" signals for CSI 300 and CSI 500 indices [12][72] - **Limit-Up/Limit-Down Model**: "Neutral" signal for medium-term analysis [13][73] - **Long-Term Momentum Model**: SSE 50 transitioned to "bullish" [14][74] - **A-Share Comprehensive Weapon V3 Model**: Overall "bearish" signal [15][75] - **HK Stock Turnover-to-Volatility Model**: "Bullish" signal for the Hang Seng Index [16][76]

- The report includes multiple quantitative models for A-share market timing, such as the "Volume Model," "Low Volatility Model," "Feature Institutional Model," "Feature Volume Model," "Smart Algorithm Model," and "Long-term Momentum Model" [12][13][14][76] - The "Volume Model" indicates a bullish signal for most broad-based indices in the short term [12][76] - The "Low Volatility Model" provides a neutral signal for the short term [12][76] - The "Feature Institutional Model" shows a bearish signal for the short term [12][76] - The "Feature Volume Model" indicates a bullish signal for the short term [12][76] - The "Smart Algorithm Model" shows bullish signals for the CSI 300 and CSI 500 indices in the short term [12][76] - The "Long-term Momentum Model" flips to bullish for the SSE 50 index in the long term [14][78] - The "Comprehensive Weapon V3 Model" and "Comprehensive Guozheng 2000 Model" indicate bullish signals for the A-share market [15][79] - For the Hong Kong market, the "Turnover-to-Volatility Model" provides a bullish signal for the mid-term [16][80] - Backtesting results for the "Double Bottom Pattern" show a weekly return of 1.73%, outperforming the SSE Composite Index by 0.05% [46][53] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly return of 2.87%, outperforming the SSE Composite Index by 1.2% [46][47]

Group 1: Stock Selection and Timing - The importance of selecting the right stocks and buying at reasonable prices is emphasized, as many investors aim to find the next big company but often overlook the challenges in predicting future industry leaders [3][4] - Ignoring valuation can lead to significant investment risks, as buying stocks at overvalued prices may result in long periods of low returns [4][5] - Market timing is not a sustainable strategy, as small market fluctuations can distract investors from larger trends, and traditional technical analysis may be losing effectiveness in the current algorithm-driven market [6] Group 2: Human Behavior in Investing - Investors must combat greed by recognizing that market patterns tend to repeat, and high valuations at the end of bull markets require careful position management [7] - Fear during market downturns can lead to panic selling, causing investors to miss opportunities to buy undervalued stocks [7] - A personal anecdote illustrates the risk of selling during a market panic, where a missed opportunity resulted in a significant profit loss [7] Group 3: Company Research - Investors should avoid the trap of investing based solely on a preference for a company's products; instead, they should assess whether the business is fundamentally attractive [8] - Financial analysis should prioritize cash flow over profit figures, as stagnant or shrinking cash flow alongside rising profits may indicate underlying issues [8][9] - A comprehensive financial analysis requires examining the interplay between the income statement, balance sheet, and cash flow statement to avoid financial pitfalls [9]