Core Viewpoint - The article discusses the quantitative timing and crowding alerts in the financial market, providing insights into market trends and potential investment opportunities [1]. Group 1: Quantitative Timing - The report highlights the importance of quantitative timing in investment strategies, emphasizing its role in identifying optimal entry and exit points in the market [1]. - It presents data on market performance metrics, indicating significant fluctuations in key indices over the past week [1]. Group 2: Crowding Alerts - The article outlines the concept of crowding in investment positions, warning that excessive concentration in certain assets can lead to increased volatility [1]. - It provides statistics on the current levels of crowding in various sectors, suggesting that some sectors are nearing critical thresholds that could trigger market corrections [1]. Group 3: Market Trends - The report analyzes recent market trends, noting shifts in investor sentiment and sector performance [1]. - It includes projections for future market movements based on current data, indicating potential areas for investment growth [1].

金融工程 | 金工定期报告 金融工程 证券研究报告 2025 年 05 月 11 日 量化择时周报：重大事件落地前维持中性仓位 重大事件落地前维持中性仓位 上周周报(20250505)认为：在风险偏好承压叠加市场格局触发下行趋势， 全 A 指数的 30 日均线构成压力位，但考虑到估值不高，建议在压力位突 破前维持中性仓位。最终 wind 全 A 周二突破 30 日均线，随后迎来上涨。 市值维度上，上周代表小市值股票的中证 2000 上涨 3.58%，中盘股中证 500 上涨 1.6%，沪深 300 上涨 2%，上证 50 上涨 1.93%;上周中信一级行业中， 表现较强行业包括国防军工、通信，国防军工上涨 6.44%，消费者服务、房 地产表现较弱，消费者服务微涨 0.3%。上周成交活跃度上，军工和通信资 金流入明显。 从择时体系来看，我们定义的用来区别市场整体环境的 wind 全 A 长期均 线（120 日）和短期均线（20 日）的距离开始收窄，最新数据显示 20 日 线收于 4946，120 日线收于 5088 点，短期均线继续位于长线均线之下， 两线差值由上周的-3.63%缩小至-2.80%，距离绝对值开 ...

Quantitative Models and Construction Methods - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term perspectives, focusing on sectors with potential for recovery or growth trends[2][3][10] **Model Construction Process**: The model identifies sectors with recovery potential ("困境反转型板块") and growth opportunities. It recommends sectors such as healthcare (恒生医疗), export-related consumer sectors (e.g., light industry and home appliances), and technology sectors (信创, communication, solid-state batteries). Additionally, it highlights sectors with ongoing upward trends, such as banking and gold[2][3][10] **Model Evaluation**: The model provides actionable insights for medium-term industry allocation, emphasizing sectors with recovery potential and growth trends[2][3][10] - **Model Name**: TWO BETA Model **Model Construction Idea**: This model focuses on identifying technology-related sectors with growth potential[2][3][10] **Model Construction Process**: The TWO BETA model recommends technology sectors, including 信创, communication, and solid-state batteries, based on their growth potential and market trends[2][3][10] **Model Evaluation**: The model effectively identifies technology sectors with strong growth potential, aligning with market trends[2][3][10] - **Model Name**: Timing System Model **Model Construction Idea**: This model evaluates market conditions by analyzing the distance between short-term and long-term moving averages to determine market trends[2][9][14] **Model Construction Process**: 1. Define the short-term moving average (20-day) and long-term moving average (120-day) for the Wind All A Index 2. Calculate the difference between the two moving averages: $ \text{Difference} = \text{20-day MA} - \text{120-day MA} $ - Latest values: 20-day MA = 4946, 120-day MA = 5088 - Difference = -2.80% (previous week: -3.63%) 3. Monitor the absolute value of the difference; when it falls below 3%, the market is considered to be in a consolidation phase[2][9][14] **Model Evaluation**: The model provides a clear signal for market consolidation, aiding in timing decisions[2][9][14] - **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation levels and short-term market trends[3][10] **Model Construction Process**: 1. Assess valuation levels of the Wind All A Index: - PE ratio: 50th percentile (medium level) - PB ratio: 10th percentile (low level) 2. Combine valuation levels with short-term market trends to recommend a 60% equity allocation for absolute return products[3][10] **Model Evaluation**: The model provides a systematic approach to position management, balancing valuation and market trends[3][10] Backtesting Results of Models - **Industry Allocation Model**: No specific numerical backtesting results provided[2][3][10] - **TWO BETA Model**: No specific numerical backtesting results provided[2][3][10] - **Timing System Model**: - Latest moving average difference: -2.80% - Previous week difference: -3.63% - Absolute difference < 3%, indicating a consolidation phase[2][9][14] - **Position Management Model**: - Recommended equity allocation: 60%[3][10]

Quantitative Models and Construction Methods 1. Model Name: Market Sentiment Timing Model - **Model Construction Idea**: This model is designed to quantify market sentiment using a structured approach, incorporating multiple sub-indicators to assess the overall sentiment direction [7][8] - **Model Construction Process**: 1. Sub-indicators used include: industry trading volatility, industry trading congestion, price-volume consistency, Sci-Tech 50 trading proportion, industry trend, RSI, main buying force, PCR combined with VIX, and financing balance proportion [8] 2. Each sub-indicator is scored based on its sentiment direction and position within Bollinger Bands, with scores categorized as (-1, 0, 1) [8] 3. The final sentiment structure indicator is calculated as the 20-day moving average of the summed scores, oscillating around the zero axis within the range of [-6, 6] [8] - Formula: $ \text{Sentiment Indicator} = \text{20-day MA of (Sum of Sub-indicator Scores)} $ - **Model Evaluation**: The model effectively captures market sentiment fluctuations, with significant sentiment recovery observed since April 2024 [8][9] 2. Model Name: Moving Average Sequence Scoring (MASS) Model - **Model Construction Idea**: This model evaluates the long-term and short-term trends of indices by analyzing the relative positions of moving averages over different time horizons [20] - **Model Construction Process**: 1. For a given period \( N \) (e.g., \( N=360 \) for long-term, \( N=60 \) for short-term), calculate scores for \( N \) moving averages [20] 2. If a shorter moving average \( k \) is above the longer moving average \( k+1 \), assign a score of 1; otherwise, assign 0 [20] 3. Normalize the scores to a range of 0-100 and compute the average score for the index at a specific time point [20] 4. Calculate the 100-day and 20-day moving averages of the trend scores to generate buy/sell signals [20] - Formula: $ \text{Trend Score} = \frac{\text{Sum of Scores}}{N} \times 100 $ - **Model Evaluation**: The model provides clear signals for trend reversals, with recent results indicating a shift towards growth-oriented sectors [20][21] 3. Model Name: RSI Style Timing Model - **Model Construction Idea**: This model uses the Relative Strength Index (RSI) to evaluate the relative strength of different market styles (e.g., growth vs. value, small-cap vs. large-cap) [24] - **Model Construction Process**: 1. Calculate the net value ratio of two style indices (e.g., growth/value) over a fixed period [24] 2. Compute the RSI using the formula: $ \text{RSI} = 100 - \frac{100}{1 + \frac{\text{Average Gain}}{\text{Average Loss}}} $ - Where "Gain" represents average positive changes, and "Loss" represents average negative changes over \( N \) days [24] 3. Compare the 20-day RSI with the 60-day RSI to determine the dominant style [24] - **Model Evaluation**: The model indicates a clear shift from large-cap value to small-cap growth styles, with strong confirmation from recent RSI trends [24][27] --- Model Backtesting Results 1. Market Sentiment Timing Model - Sentiment Indicator Value: 1.5 as of May 9, 2025, indicating a positive sentiment recovery [9] 2. Moving Average Sequence Scoring (MASS) Model - Short-term signals: Positive for indices such as CSI 300, CSI A500, and ChiNext, with short-term scores ranging from 33.90 to 40.68 [36] - Long-term signals: Positive for most indices, with long-term scores exceeding 66.57 for indices like ChiNext [36] 3. RSI Style Timing Model - Growth/Value RSI: Growth-dominant with RSI values of 57.91 (short-term) and 55.24 (long-term) for the CSI Growth/Value index [27] - Small/Large Cap RSI: Small-cap dominant with RSI values of 59.84 (short-term) and 60.16 (long-term) for the Small/Large Cap index [27] --- Quantitative Factors and Construction Methods 1. Factor Name: Price-Volume Consistency - **Factor Construction Idea**: Measures the stability of market sentiment based on the alignment of price and volume movements [8] - **Factor Construction Process**: 1. Calculate the correlation between price changes and trading volume over a fixed period [8] 2. Assign scores based on the strength of the correlation, with higher scores indicating stronger consistency [8] - **Factor Evaluation**: The factor showed significant improvement in recent weeks, contributing to the overall sentiment recovery [11][16] 2. Factor Name: RSI - **Factor Construction Idea**: Reflects the relative strength of buying vs. selling pressure over a specific period [24] - **Factor Construction Process**: 1. Compute average gains and losses over \( N \) days [24] 2. Use the RSI formula to calculate the index value [24] - **Factor Evaluation**: RSI values above 50 indicate strong buying pressure, with recent results favoring growth and small-cap styles [24][27] --- Factor Backtesting Results 1. Price-Volume Consistency - Recent Score: Increased to 1 as of May 9, 2025, indicating improved alignment between price and volume [12] 2. RSI - Growth/Value RSI: Growth-dominant with short-term RSI of 57.91 [27] - Small/Large Cap RSI: Small-cap dominant with short-term RSI of 59.84 [27]

Market Performance - The recent 5 trading days saw the Sci-Tech 50 Index increase by 0.24%, the ChiNext Index rise by 4.13%, large-cap value stocks up by 1.55%, large-cap growth stocks up by 2.05%, the SSE 50 Index up by 1.46%, and the small-cap represented by the CSI 2000 up by 3.77% [1] - The defense and military industry, as well as the communication sector, performed well, while steel and retail sectors lagged behind [1] Risk Premium Analysis - The static PE of the CSI All Index minus the yield of 10-year government bonds indicates a risk premium, which has historically reached extreme levels at two standard deviations above the mean during significant market bottoms, such as in 2012, 2018, and 2020 [1] - As of April 26, 2022, the risk premium reached 4.17%, and on October 28, 2022, it was 4.08%, with a recent reading of 4.11% on January 19, 2024, marking the fifth occurrence since 2016 of exceeding 4% [1] Valuation Levels - As of May 9, 2025, the CSI All Index's PETTM is at the 50th percentile, with the SSE 50 and CSI 300 at 61% and 47% respectively, while the ChiNext Index is close to 11% [2] - The ChiNext Index's valuation is relatively low compared to historical averages [2] Long-term Market Trends - The technical analysis of the Deep 100 Index indicates a pattern of bear markets every three years followed by bull markets, with previous declines ranging from 40% to 45% [2] - The current adjustment cycle began in Q1 2021, suggesting a potential for upward movement from the bottom [2] Fund Flow and Trading Activity - In the last 5 trading days, ETF funds saw an outflow of 17.9 billion yuan, while margin trading increased by approximately 4.4 billion yuan [2] - The average daily trading volume across both markets was 1.2918 trillion yuan [2] AI and Machine Learning Insights - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes, with a current focus on banking [2][7] Market Sentiment - The proportion of stocks above the 200-day moving average is being tracked to gauge market sentiment [9] Equity and Bond Risk Preference - Ongoing monitoring of risk preferences between equity and bond assets is being conducted [11]

Investment Rating - The industry investment rating is "Neutral" with an expected industry index increase of -5% to 5% relative to the CSI 300 index over the next six months [22]. Core Insights - The market is currently in a downtrend, with a focus on when the profit effect will turn positive. The current profit effect is around -1% [2][10]. - The report suggests maintaining a neutral position until the 30-day moving average of the wind All A index is breached, considering the low valuation levels [4][10]. - The industry configuration model recommends focusing on "dilemma reversal" sectors, particularly in healthcare and consumer sectors related to export chains such as light industry and home appliances [3][10]. - The TWO BETA model continues to recommend the technology sector, emphasizing domestic substitution in the fields of information technology and AI chips [3][10]. - Despite a significant drop on Friday, the banking sector, which is still in an upward trend, remains worthy of attention [3][10]. Summary by Sections Market Overview - The wind All A index is currently in a downtrend, with the 20-day moving average at 4908 and the 120-day moving average at 5092.8, indicating a distance of -3.63% [2][9]. - The market's current environment is characterized by uncertainty due to upcoming Federal Reserve meetings and the release of April import and export data [4][10]. Valuation Metrics - The overall PE ratio of the wind All A index is around the 50th percentile, indicating a medium level, while the PB ratio is around the 20th percentile, indicating a relatively low level [3][10]. Positioning Recommendations - The report advises a 50% allocation in absolute return products based on the wind All A index as the main stock allocation [3][10].

Quantitative Models and Construction Methods 1. Model Name: Market Sentiment Timing Model - **Model Construction Idea**: The model is built from a structural perspective to quantify market sentiment using various sub-indicators[7] - **Model Construction Process**: - The model uses sub-indicators such as industry trading volatility, trading crowding, price-volume consistency, Sci-Tech Innovation Board (STAR 50) trading proportion, industry trend, RSI, main buying force, PCR combined with VIX, and financing balance ratio[8] - Each sub-indicator is scored based on its sentiment direction and position within Bollinger Bands. Scores are categorized as (-1, 0, 1)[8] - The final sentiment structural indicator is the 20-day moving average of the summed scores. The indicator fluctuates around 0 within the range of [-6, 6][8] - **Model Evaluation**: The model effectively captures market sentiment trends and provides actionable insights for timing decisions[8] 2. Model Name: Moving Average Scoring System (MASS) - **Model Construction Idea**: This model evaluates long-term and short-term trends of indices using N-day moving averages to generate timing signals[18] - **Model Construction Process**: - For N moving averages (N=360 for long-term, N=60 for short-term), scores are assigned based on the relative position of adjacent moving averages. If a shorter moving average is above a longer one, it scores 1; otherwise, it scores 0[18] - The scores are standardized to a 0-100 scale and averaged to derive the trend score at a specific time point[18] - Long/short-term timing signals are generated based on the crossover of the trend score with its 100/20-day moving average[18] - **Model Evaluation**: The model provides clear signals for sector rotation and market style preferences, favoring value and defensive sectors in the current environment[18] 3. Model Name: RSI Style Timing Model - **Model Construction Idea**: The model uses the Relative Strength Index (RSI) to compare the relative strength of different market styles (e.g., growth vs. value, small-cap vs. large-cap)[22] - **Model Construction Process**: - For two indices A and B, calculate the standardized ratio of their net values over a fixed period[22] - Compute the average gain (Gain) and average loss (Loss) over N days, where gains on down days are treated as 0 and losses on up days are treated as 0[22] - RSI formula: $ RSI = 100 - 100 / (1 + Gain / Loss) $ - RSI values range from 0 to 100, with values above 50 indicating stronger buying pressure[22] - The model calculates 5-day, 20-day, and 60-day RSI values. When the 20-day RSI exceeds the 60-day RSI, the numerator style is favored; otherwise, the denominator style is favored[22] - **Model Evaluation**: The model effectively identifies style dominance, currently favoring large-cap and value styles while noting short-term strengthening of growth and small-cap styles[22] --- Model Backtesting Results 1. Market Sentiment Timing Model - Sentiment indicator value as of April 30, 2025: 0.8, indicating a recovery in market sentiment[9] 2. Moving Average Scoring System (MASS) - Short-term signals: Positive for sectors like beauty care (72.88), utilities (86.44), banking (74.58), and oil & petrochemicals (22.03)[19] - Long-term signals: Positive for sectors like banking (95.54), machinery (78.55), and steel (51.25)[19] 3. RSI Style Timing Model - Growth/Value (300 Growth/300 Value): RSI 20-day = 53.02, RSI 60-day = 50.42, favoring value[25] - Small-cap/Large-cap (SW Small/SW Large): RSI 20-day = 48.84, RSI 60-day = 53.62, favoring large-cap[25] --- Quantitative Factors and Construction Methods 1. Factor Name: RSI - **Factor Construction Idea**: Measures the relative strength of buying and selling forces over a specific period[22] - **Factor Construction Process**: - Calculate the average gain (Gain) and average loss (Loss) over N days[22] - Formula: $ RSI = 100 - 100 / (1 + Gain / Loss) $ - RSI values range from 0 to 100, with higher values indicating stronger buying pressure[22] - **Factor Evaluation**: Provides a robust measure of market momentum and style preferences[22] --- Factor Backtesting Results 1. RSI - Growth/Value (300 Growth/300 Value): RSI 20-day = 53.02, RSI 60-day = 50.42, favoring value[25] - Small-cap/Large-cap (SW Small/SW Large): RSI 20-day = 48.84, RSI 60-day = 53.62, favoring large-cap[25]

金融工程 | 金工定期报告 2025 年 05 月 05 日 作者 吴先兴 分析师 SAC 执业证书编号：S1110516120001 wuxianxing@tfzq.com 相关报告 1 《金融工程：金融工程-因子跟踪周 报 ： Beta 、换手率因子表现较好 -20250504》 2025-05-04 2 《金融工程：金融工程-哪些行业进 入高估区域？——估值与基金重仓股配 置监控 2025-05-03》 2025-05-03 3 《金融工程：金融工程-净利润断层 本周超额基准 0.92%》 2025-05-03 金融工程 证券研究报告 量化择时周报：突破压力位前保持中性 突破压力位前保持中性 上周周报(20250427)认为：全 A 指数的 30 日均线构成压力位，但考虑到估 值不高，建议在压力位突破前维持中性仓位。最终 wind 全 A 维持原状。 市值维度上，上周代表小市值股票的中证 2000 上涨 0.84%，中盘股中证 500 上涨 0.08%，沪深 300 下跌 0.43%，上证 50 下跌 0.59%;上周中信一级行业中， 表现较强行业包括传媒、计算机，传媒上涨 2.86%，综合金融、房地产 ...

市场情绪自3月20日持续调整，于4月18日下降至低点，数值为0.1。本周市场情绪指标在接近0轴处开始向上反弹，回升至0.5，数值较上周五(4/18)上升0.4，模型转多，市场 情绪有所缓和。 本周A股市场提示市场情绪有一定修复，较上周明显发生变化的指标有科创50成交占比、主力买入力量和期权波动率。主力流出速率减缓和VIX指标体现的恐慌程度减弱是本 周市场情绪回升的主要原因。 科创50成交占比、行业涨跌趋势性、主力买入力量和PCR结合VIX，分别代表了市场风险偏好程度下降，市场情绪不确定性增强，主力流出速度 减缓和期权市场恐慌情绪缓和。其他指标维持和上周一致的判断。 资金当前对成长高估值板块观点不确定性较强。 自上周科创50成交占比指标快速下跌至下轨以下后，本周科创50成交占比指标仍在持续下降。本周主力资金持续从科创板块 流出，累计净流出超过32亿人民币。 投资者信心逐渐恢复，市场的活跃度和投资者参与度都有了明显提升。 除了看到主力资金本周流出科创板，主力资金本周在全A仍然呈现净流出的态势，但流出速度较上周有 所减缓，主力流出主力买入力量指标有所回升。从主力资金净流出绝对量看，主力资金本周累计净流出超过370亿 ...

1. 情绪模型观点：市场情绪进一步下行 根 据 《 从 结 构 化 视 角 全 新 打 造 市 场 情 绪 择 时 模 型 》 文 中 提 到 的 构 建 思 路 ， 目 前 我 们 用 于 构 建 市 场 情 绪 结 构 指 标 所 用 到 的 细 分 指 标 如 下 表 | 指标简称 | 含义 | 情绪指示方向 | | --- | --- | --- | | 行业间交易波动率 | 资金在各板块间的交易活跃度 | 正向 | | 行业交易拥挤度 | 极值状态判断市场是否过热 | 负向 | | 价量一致性 | 资金情绪稳定性 | 正向 | | 科创 50 成交占比 | 资金风险偏好 | 正向 | | 行业涨跌趋势性 | 刻画市场轮涨补涨程度，趋势衡量 | 正向 | | RSI | 价格体现买方和卖方力量相对强弱 | 正向 | | 主力买入力量 | 主力资金净流入水平 | 正向 | | PCR 结合 VIX | 从期权指标看市场多空情绪 | 正向或负向 | | 融资余额占比 | 资金对当前和未来观点多空 | 6 公众号 · 普罗完酒会工 | 在指标合成方法上，模型采用打分的方式，根据每个分项指标所提示的情绪方向和 ...