金融工程 金工周报 2025 年 08 月 24 日 【金工周报】（20250818-20250822） 大部分指数依旧看多，后市或乐观向上 本周回顾 本周市场普遍上涨，上证指数单周上涨 3.49%，创业板指单周上涨 5.85%。 A 股模型： 短期：成交量模型大部分宽基看多。低波动率模型中性。特征龙虎榜机构模型 看空。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。 证 券 研 究 报 告 中期：涨跌停模型看多。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多。 本周行业指数普遍上涨，涨幅前五的行业为：通信、电子、计算机、传媒、综 合。从资金流向角度来说，除通信、消费者服务、综合外所有行业主力资金净 流出，其中机械、医药、计算机、电力设备及新能源、基础化工主力资金净流 出居前。 本周股票型基金总仓位为 98.71%，相较于上周减少了 40 个 bps，混合型基金 总仓位 95.36%，相较于上周增加了 264 个 bps。 本周电力设备及新能源与通信获得最大机构 ...

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]

Core Viewpoint - The article reflects on the author's 16-year career in the finance industry, emphasizing the importance of mentorship, professional growth, and the aspiration to become a top fund manager in China [1][2][7]. Group 1: Career Development - The author began their career in 2006 at Pacific Securities, where they received early encouragement to become an excellent fund manager, which became a guiding principle [2]. - Transitioning to Haitong Securities, the author focused on quantitative analysis, contributing to the team's recognition in the market [3]. - The move to CITIC Securities was influenced by a mentor's recognition of the author's research methodology, reinforcing their career aspirations [4]. Group 2: Market Insights and Contributions - In 2018, during a challenging market environment due to US-China trade tensions, the author utilized a quantitative macro-timing system to provide accurate market predictions [4]. - The author played a significant role in providing market stabilization suggestions during the trade conflict, receiving recognition from the Shanghai Stock Exchange [4]. - In 2020, the author led a pivotal investment decision that significantly boosted the company's quarterly performance, marking a high point in their career [5]. Group 3: Future Aspirations - In 2023, the author set a goal to become the top fund manager in China, supported by the leadership's commitment to talent development [7]. - The author achieved the highest annual timing return in their career, reflecting the effectiveness of their research and investment strategies [8]. - The establishment of Shanghai Ruicheng Fund is aimed at fulfilling the author's long-term vision of creating a competitive investment product that leverages quantitative strategies and economic cycle theories [11].

Quantitative Models and Construction 1. Model Name: Volume Model - **Construction Idea**: This model evaluates market trends based on trading volume dynamics to provide short-term signals [12][65] - **Construction Process**: The model analyzes trading volume data to determine whether the market is in a neutral, bullish, or bearish state. Specific formulas or parameters are not disclosed in the report [12][65] - **Evaluation**: The model currently provides a neutral signal for the short term, indicating no strong directional bias [12][65] 2. Model Name: Low Volatility Model - **Construction Idea**: This model assesses market conditions by analyzing the volatility of stock prices over a short-term horizon [12][65] - **Construction Process**: The model calculates the volatility of stock prices and categorizes the market state as neutral, bullish, or bearish. Detailed formulas are not provided [12][65] - **Evaluation**: The model is currently neutral, suggesting a lack of significant market movement [12][65] 3. Model Name: Institutional Feature Model (LHB) - **Construction Idea**: This model uses institutional trading data from the "Dragon and Tiger List" (龙虎榜) to predict short-term market trends [12][65] - **Construction Process**: The model aggregates institutional trading activity and evaluates its impact on market direction. Specific formulas are not disclosed [12][65] - **Evaluation**: The model is neutral, indicating no clear institutional bias in the market [12][65] 4. Model Name: Feature Volume Model - **Construction Idea**: This model combines trading volume features to assess short-term market trends [12][65] - **Construction Process**: The model analyzes specific volume-related features to determine market sentiment. Detailed formulas are not provided [12][65] - **Evaluation**: The model is bearish, suggesting a negative outlook for the short term [12][65] 5. Model Name: Smart HS300 Model - **Construction Idea**: This model uses intelligent algorithms to predict short-term trends for the CSI 300 Index [12][65] - **Construction Process**: The model applies machine learning or algorithmic techniques to analyze market data. Specific methodologies are not disclosed [12][65] - **Evaluation**: The model is bearish, indicating a negative outlook for the CSI 300 Index [12][65] 6. Model Name: Smart CSI500 Model - **Construction Idea**: This model uses intelligent algorithms to predict short-term trends for the CSI 500 Index [12][65] - **Construction Process**: Similar to the Smart HS300 Model, this model applies algorithmic techniques to analyze market data. Specific methodologies are not disclosed [12][65] - **Evaluation**: The model is bullish, indicating a positive outlook for the CSI 500 Index [12][65] 7. Model Name: Limit-Up/Down Model - **Construction Idea**: This model evaluates mid-term market trends based on the frequency of limit-up and limit-down events [13][66] - **Construction Process**: The model tracks the occurrence of daily limit-up and limit-down events to assess market sentiment. Specific formulas are not disclosed [13][66] - **Evaluation**: The model is neutral, indicating no strong mid-term market bias [13][66] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model analyzes seasonal or calendar-based patterns to predict mid-term market trends [13][66] - **Construction Process**: The model evaluates historical market performance during specific calendar periods. Detailed methodologies are not provided [13][66] - **Evaluation**: The model is neutral, suggesting no significant calendar-based market trends [13][66] 9. Model Name: Long-Term Momentum Model - **Construction Idea**: This model assesses long-term market trends based on momentum indicators [14][67] - **Construction Process**: The model calculates momentum metrics for broad-based indices to determine long-term market direction. Specific formulas are not disclosed [14][67] - **Evaluation**: The model is neutral for all broad-based indices, indicating no strong long-term market trends [14][67] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Construction Idea**: This composite model integrates multiple short, mid, and long-term signals to provide an overall market outlook [15][68] - **Construction Process**: The model combines signals from various sub-models (e.g., volume, volatility, institutional activity) to generate a comprehensive market view. Specific integration methods are not disclosed [15][68] - **Evaluation**: The model is bearish, indicating an overall negative outlook for the A-share market [15][68] 11. Model Name: A-Share Comprehensive Guozheng 2000 Model - **Construction Idea**: This composite model focuses on the Guozheng 2000 Index, integrating multiple signals to provide an overall market outlook [15][68] - **Construction Process**: Similar to the V3 Model, this model aggregates signals from various sub-models. Specific methodologies are not disclosed [15][68] - **Evaluation**: The model is bearish, indicating a negative outlook for the Guozheng 2000 Index [15][68] 12. Model Name: HK Stock Turnover-to-Volatility Model - **Construction Idea**: This model evaluates mid-term trends in the Hong Kong market by analyzing the ratio of turnover to volatility [16][69] - **Construction Process**: The model calculates the turnover-to-volatility ratio to assess market sentiment. Specific formulas are not disclosed [16][69] - **Evaluation**: The model is bearish, suggesting a negative outlook for the Hong Kong market [16][69] --- Backtesting Results of Models 1. Volume Model - **Signal**: Neutral [12][65] 2. Low Volatility Model - **Signal**: Neutral [12][65] 3. Institutional Feature Model (LHB) - **Signal**: Neutral [12][65] 4. Feature Volume Model - **Signal**: Bearish [12][65] 5. Smart HS300 Model - **Signal**: Bearish [12][65] 6. Smart CSI500 Model - **Signal**: Bullish [12][65] 7. Limit-Up/Down Model - **Signal**: Neutral [13][66] 8. Calendar Effect Model - **Signal**: Neutral [13][66] 9. Long-Term Momentum Model - **Signal**: Neutral [14][67] 10. A-Share Comprehensive Weapon V3 Model - **Signal**: Bearish [15][68] 11. A-Share Comprehensive Guozheng 2000 Model - **Signal**: Bearish [15][68] 12. HK Stock Turnover-to-Volatility Model - **Signal**: Bearish [16][69]

观点简述： 上周市场全周继续微涨趋势，沪深300指数周涨幅0.38%，上证综指周涨幅0.56%，中证500指数 周涨幅1.20%。市场维持了少见的窄幅震荡状态，资金重点期盼重要会议的方向指引。 每周思考总第626期 《 久盘滞涨，建议再降仓 》 本系列周度择时观点回溯表现（2023.1.1 至今），其中2024年全年累计收益53.69%。2025年至4 月27日累计收益7.79%。 1 本周建议 | 预测标的 | 仓位建议 | | --- | --- | | 主板 | 低仓位 | | 中小市值板块 | 低仓位 | | 风格判断 | 均衡 | 基本面上，美国经济数据的割裂度不断提升。 国内方面，上周央行MLF超额续作，但市场重点 关注的财政与货币刺激政策时间表依旧未能在4月政治局会议中有明确指示，这也意味着4月关税冲击 对国内经济基本面的影响或将难免；海外方面，美国继续披露各维度经济数据，各条线数据分化有所 扩大，如成屋销售继续低迷但新房销售重回高位、就业PMI维持弱势但失业人数保持平稳、最为矛盾 的是消费同比保持高增但消费者信心与投资者信心都已跌至过去4年新低，美国经济基本面的锚定指 标出现如此巨幅分化走势是 ...

每周思考总第620期 《 新高如期兑现，首次左侧预警 》 本系列周度择时观点回溯表现（2023.1.1 至今），其中2024年全年累计收益53.69%。2025年至3 月16日累计收益7.79%。 中小市值板块择时观点： 短期交易中的政策窗口期尾声阶段，同步于主板开启谨慎博弈策略，自1月 翻多后首次建议重回 中等仓位 ，风格建议转向均衡； 短期动量（趋势）模型建议关注行业：无。 | 预测标的 | 仓位建议 | | --- | --- | | 主板 | 中仓位 | | 中小市值板块 | 中仓位 | | 风格判断 | 均衡 | 观点简述： 上周市场如期上行创出本轮新高，沪深300指数涨幅1.59%，上证综指周涨幅1.39%，中证500指 数周涨幅1.43%。上周我们标题观点提示"A股仍在政策窗口期"，实际A股上周五大涨重要催化因素也 是周四央行关于降准降息的最新强调表态。 基本面上，中国经济仍只是温和企稳。 国内方面，上周发布的2月货币供应数据整体喜忧参半， 社融同比回升而M1同比回落，表明国内经济仍在底部夯实状态而非强劲反转走强， 重申留意三月美 国全球关税加征后对中国外需的更显著冲击，在此阶段央行重提"年内择 ...