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]

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 ...