Group 1 - The report highlights a continuous rebound in social financing (社融) in April, with an increase of 1.16 trillion yuan, which is 12.25 billion yuan more than the same period last year, and a year-on-year growth rate of 8.7% [2][26][27] - The M2 growth is seen as a foundation for the rebound in social financing, with the central bank emphasizing the importance of revitalizing existing financial resources and preventing idle capital [2][26] - The report indicates that while there are signs of improvement in data, further support is needed, particularly in the real estate sector, where the proportion of domestic loans for real estate development has risen to 14%, nearing levels seen in 2019-2020 [2][26] Group 2 - The financial data for April shows a significant year-on-year decrease in new RMB loans, with an addition of 280 billion yuan, which is 450 billion yuan less than the previous year, and a notable decline in new social financing [6] - The report notes that government bonds have been a major driver of social financing growth, with April's social financing growth rate potentially being the peak for the year [6] - The M2 growth acceleration is attributed to a low base effect, while M1 growth has slightly declined, indicating a need to monitor the effectiveness of monetary policy [6] Group 3 - The report on the computer industry emphasizes the potential of AI agents, particularly in the consumer (C-end) and business (B-end) sectors, with major companies like Alibaba and Tencent leading the C-end market [11] - The B-end market is segmented into head clients and small to medium clients, with different strategies for adopting AI solutions based on their needs and capabilities [11] - The report anticipates a significant growth in AI infrastructure, with the market for intelligent computing centers expected to exceed 288.6 billion yuan by 2028, growing at a compound annual growth rate of 26.8% from 2023 to 2028 [11][12] Group 4 - The report on the electric new energy sector highlights Jinlei Co., which achieved a total operating income of 505 million yuan in Q1 2025, a year-on-year increase of 97.5%, driven by increased shipment volumes [13] - The company’s dual business model of forging and casting is expected to enhance its market share, with significant growth in its wind power casting business [13] - The report also mentions an employee stock ownership plan that could stimulate operational vitality, involving up to 2.805 million shares at a grant price of 11.53 yuan per share [13]

Group 1 - The market sentiment indicator has risen to 2 as of May 16, indicating a continued upward repair of market sentiment over 17 consecutive trading days since the low on April 18, with a bullish model perspective [9][3][7] - Future improvements in market sentiment require dual support from trading volume and investment themes, as the A-share market shows signs of sentiment recovery, but risk appetite has declined and industry trend scores have turned negative [12][3] - The overall trading volume in the A-share market has shown a downward trend, with total trading volume on Friday reaching 1.12 trillion RMB and daily trading volume dropping to 95.291 billion shares [14][3][12] Group 2 - The trend scores for various industries have turned negative, indicating a lack of investment themes in the current market [20][3] - The short-term trend scores for industries such as comprehensive services and transportation have significantly increased, with scores rising nearly 60% [24][3][25] - The overall market style is shifting towards large-cap stocks, although the growth style remains dominant [27][3][28]

- The report defines a market timing system using the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index to distinguish the overall market environment[2][8][13] - The distance between the 20-day moving average and the 120-day moving average has narrowed from -2.80% to -1.33%, indicating the market is in a volatile state[2][8][13] - The industry allocation model recommends sectors such as Hang Seng Medical, Hong Kong automotive, and new consumption industries from a mid-term perspective[2][3][9] - The TWO BETA model continues to recommend the technology sector, focusing on information innovation and communication[2][3][9] - The Wind All A Index's overall PE is around the 60th percentile, indicating a medium level, while the PB is around the 10th percentile, indicating a relatively low level[3][9] - The position management model suggests an absolute return product with Wind All A as the main stock allocation should have a 50% position[3][9] - The market is expected to continue to decline in trading volume, with a potential rebound when the volume shrinks to around 900 billion[2][3][9] Model Backtest Results - The distance between the 20-day and 120-day moving averages is -1.33%[2][8][13] - The Wind All A Index's PE is at the 60th percentile[3][9] - The Wind All A Index's PB is at the 10th percentile[3][9] - The recommended position for absolute return products is 50%[3][9]

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]