Group 1 - The market sentiment score has further declined, with the current score at 1.75, down from 2.5 the previous week, indicating a bearish outlook [1][4][6] - The financing balance ratio and the 300 RSI index scores have decreased, reflecting an increase in bearish sentiment among investors [4][9] - The overall market lacks a clear investment theme, with industry performance trends remaining weak and negative [17][20] Group 2 - The total transaction volume in the A-share market has seen a slight increase, with a daily transaction amount of 1.17 trillion RMB, but significant outflows of main funds have negatively impacted market performance [12] - The short-term trend scores for industries such as communication, real estate, and media have shown significant increases, with communication and real estate industries rising by 41.67% [23][24] - The small-cap growth style is currently favored, with a strong signal indicating its superiority over other styles, while the differentiation between growth and value styles remains weak [25]

Core Viewpoint - The market is expected to continue a trend of oscillating upward in the coming week, supported by technical indicators and liquidity metrics [1][2]. Market Indicators - The liquidity shock index for the CSI 300 was 0.30, indicating higher liquidity than the average level over the past year by 0.30 standard deviations [2]. - The PUT-CALL ratio for the SSE 50 ETF options decreased to 0.85, reflecting a reduced caution among investors regarding short-term movements [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 0.82% and 1.40%, respectively, indicating increased trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates saw weekly increases of 0.15% and 0.25%, respectively [2]. - The official manufacturing PMI for China in May was reported at 49.5, matching expectations, while the Caixin manufacturing PMI was lower at 48.3 [2]. Technical Analysis - The Wind All A index broke through the SAR point on June 4, signaling a buy opportunity, with the moving average strength index scoring 207, placing it in the 81.6% percentile since 2021 [2][3]. Market Performance - For the week of June 2 to June 6, the SSE 50 index rose by 0.38%, the CSI 300 index increased by 0.88%, the CSI 500 index grew by 1.6%, and the ChiNext index surged by 2.32% [3]. - The overall market PE (TTM) stands at 19.2 times, which is in the 52.3% percentile since 2005 [3]. Factor Analysis - Small-cap factors performed well, with a crowding degree of 1.05, while low valuation factors had a crowding degree of 0.06 [3]. - The industry crowding degree is relatively high in machinery, comprehensive, retail, environmental protection, and beauty care sectors, with notable increases in beauty care and banking [3].

Quantitative Models and Construction Methods - **Model Name**: Timing System Model **Model Construction Idea**: This model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the Wind All A Index to determine the overall market environment and identify market trends [1][9][12] **Model Construction Process**: 1. Calculate the 20-day moving average (short-term) and the 120-day moving average (long-term) of the Wind All A Index 2. Compute the difference between the two moving averages: $ \text{Difference} = \text{20-day MA} - \text{120-day MA} $ 3. Evaluate the absolute value of the difference. If the absolute value is less than 3%, the market is considered to be in a consolidation phase [1][9][12] **Model Evaluation**: The model effectively captures the market's consolidation phase and provides a clear signal for timing decisions [1][9][12] - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model identifies industries with medium-term growth potential and recommends allocation based on sectoral trends and macroeconomic factors [2][3][10] **Model Construction Process**: 1. Analyze macroeconomic factors and market sentiment 2. Identify sectors with potential for recovery or growth, such as "distressed reversal" sectors 3. Recommend specific industries, such as innovative pharmaceuticals, automobiles, and new consumption in the Hong Kong market, as well as technology sectors like consumer electronics [2][3][10] **Model Evaluation**: The model provides actionable insights for medium-term industry allocation, focusing on sectors with growth potential [2][3][10] - **Model Name**: TWO BETA Model **Model Construction Idea**: This model focuses on identifying high-growth sectors, particularly in technology, and recommends allocation based on their performance trends [2][3][10] **Model Construction Process**: 1. Analyze the performance of high-beta sectors, such as technology and consumer electronics 2. Monitor the upward trend of specific industries, such as banking and gold stocks, to identify allocation opportunities [2][3][10] **Model Evaluation**: The model is effective in identifying high-growth sectors and provides a focused approach to sectoral allocation [2][3][10] - **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation indicators and short-term market trends [2][10][12] **Model Construction Process**: 1. Evaluate the PE and PB valuation levels of the Wind All A Index 2. Assess the relative position of these indicators within their historical ranges 3. Combine valuation analysis with short-term market trend signals to recommend an equity allocation level (e.g., 50% for absolute return products) [2][10][12] **Model Evaluation**: The model provides a balanced approach to equity allocation, considering both valuation and market trends [2][10][12] Model Backtesting Results - **Timing System Model**: The moving average difference is 0.68%, with the absolute value remaining below 3%, indicating a consolidation phase [1][9][12] - **Position Management Model**: - PE valuation level: 60th percentile, indicating a medium level - PB valuation level: 20th percentile, indicating a relatively low level - Recommended equity allocation: 50% [2][10][12]

Core Viewpoint - The market is expected to experience a volatile upward trend led by small and mid-cap stocks after the holiday [1][2]. Market Indicators - The liquidity shock indicator for the CSI 300 index was 0.13, lower than the previous week (1.13), indicating current market liquidity is 0.13 standard deviations above the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options increased to 1.15, up from 0.94 the previous week, reflecting rising caution among investors regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 0.76% and 1.30%, respectively, indicating a decrease in trading activity, positioned at the 50.17% and 63.97% percentile since 2005 [2]. Macro Factors - The RMB exchange rate fluctuated last week, with onshore and offshore rates showing weekly declines of -0.08% and -0.48%, respectively [2]. - The US stock market showed a volatile upward trend, with the Dow Jones, S&P 500, and Nasdaq indices posting weekly returns of 1.6%, 1.88%, and 2.01% respectively [2]. - The US core PCE price index rose by 2.5% year-on-year, the lowest since March 2021, with a month-on-month increase of 0.1% [2]. Real Estate Sector - The total land acquisition amount for the top 100 enterprises from January to May 2025 reached 405.19 billion, a year-on-year increase of 28.8%, with the growth rate expanding by 2.2 percentage points compared to the previous month [2]. Technical Analysis - The Wind All A index broke below the SAR reversal indicator on May 23, indicating a bearish trend [2]. - The current market score based on the moving average strength index is 160, positioned at the 64.8% percentile since 2021 [2]. - The market has not yet formed a bottom, as the moving average strength index has not shown a significant decline [2]. Performance Overview - For the week of May 26 to May 30, the SSE 50 index fell by 1.22%, the CSI 300 index decreased by 1.08%, while the CSI 500 index rose by 0.32% and the ChiNext index dropped by 1.4% [3]. - The overall market PE (TTM) stands at 18.9 times, positioned at the 50.5% percentile since 2005 [3]. Factor and Industry Observations - Factor crowding remains stable, with small-cap factor crowding at 0.98, low valuation factor crowding at 0.11, high profitability factor crowding at -0.28, and high growth factor crowding at -0.04 [3]. - The industry crowding is relatively high in machinery, comprehensive, retail, environmental protection, and non-ferrous metals sectors, with transportation and non-ferrous metals showing significant increases in crowding [3].

Core Viewpoint - The A-share market is expected to continue its consolidation next week, influenced by technical indicators and upcoming holiday-related risk aversion among investors [1][2]. Market Analysis - The liquidity shock indicator for the CSI 300 index was 1.13 on Friday, lower than the previous week (2.63), indicating current market liquidity is 1.13 times above the average level of the past year [2]. - The put-call ratio for the SSE 50 ETF options decreased to 0.94 from 1.03, suggesting a decline in investor caution regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 0.85% and 1.40%, respectively, indicating a decrease in trading activity, positioned at the 58.47% and 68.54% percentiles since 2005 [2]. Macro Factors - The RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.2% and 0.52%, respectively [2]. - Historical data shows that from May 2005 onwards, the probability of the SSE Composite Index, CSI 300, CSI 500, and ChiNext Index rising in the latter half of May was 45%, 45%, 50%, and 47%, with average gains of -0.1%, -0.02%, 0.67%, and 1.71% [2]. Event-Driven Factors - The US stock market experienced a downward trend last week, with the Dow Jones, S&P 500, and Nasdaq indices reporting weekly returns of -2.47%, -2.61%, and -2.47%, respectively [2]. - The People's Bank of China conducted a 500 billion yuan MLF operation on May 23, with a one-year term, resulting in a net injection of 375 billion yuan for May, marking the third consecutive month of increased liquidity [2]. Technical Analysis - The Wind All A index broke below the SAR point on May 23, but the moving average strength index remains above average, indicating no bottoming pattern has emerged [2]. - The current market score based on the moving average strength index is 154, positioned at the 62.5% percentile since 2021 [2]. Performance Overview - For the week of May 19-23, the SSE 50 index fell by 0.18%, the CSI 300 index also decreased by 0.18%, the CSI 500 index dropped by 1.1%, and the ChiNext index declined by 0.88% [3]. - The overall market PE (TTM) stands at 19.0 times, which is at the 50.6% percentile since 2005 [3]. Factor Crowding Observations - The crowding degree for low valuation factors has decreased, with small-cap factor crowding at 0.91, low valuation factor crowding at 0.25, high profitability factor crowding at -0.23, and high profitability growth factor crowding at -0.03 [3]. - Industry crowding is relatively high in machinery equipment, comprehensive, retail, environmental protection, and automotive sectors, while transportation and non-ferrous metals sectors have seen a significant increase in crowding [3].

Quantitative Models and Construction Methods 1. Model Name: Industry Allocation Model - **Model Construction Idea**: This model aims to identify and recommend industries with potential for medium-term outperformance based on specific market conditions and sectoral dynamics [2][3][9] - **Model Construction Process**: The model evaluates industries based on their recovery potential ("distressed reversal sectors") and ongoing trends. It recommends sectors such as Hong Kong-listed innovative pharmaceuticals, automobiles, and new consumption industries. Additionally, it highlights sectors like technology (e.g., consumer electronics) and those with upward momentum, such as banking and gold stocks [2][3][9] - **Model Evaluation**: The model is effective in identifying sectors with medium-term growth potential and aligns with current market trends [2][3][9] 2. Model Name: TWO BETA Model - **Model Construction Idea**: This model focuses on identifying sectors with high beta characteristics, particularly in the technology domain, to capture growth opportunities [2][3][9] - **Model Construction Process**: The TWO BETA model emphasizes technology-related sectors, such as consumer electronics, as key areas of focus. It also considers sectors with strong upward momentum, like banking and gold stocks [2][3][9] - **Model Evaluation**: The model is suitable for identifying high-growth sectors in a market environment characterized by volatility and selective sectoral strength [2][3][9] 3. Model Name: Timing System Signal - **Model Construction Idea**: This model uses moving average distances to assess market conditions and provide timing signals for market entry or exit [2][3][8] - **Model Construction Process**: - The model calculates the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the Wind All A Index - Current data: - 20-day moving average: 5063 - 120-day moving average: 5079 - Distance: -0.32% (short-term moving average below long-term moving average) - The absolute value of the distance is less than 3%, indicating a market in a consolidation phase [2][3][8] - **Model Evaluation**: The model provides a clear and quantitative framework for assessing market trends and timing decisions [2][3][8] 4. Model Name: Position Management Model - **Model Construction Idea**: This model determines optimal portfolio allocation based on valuation metrics and market trends [3][9] - **Model Construction Process**: - The model evaluates the Wind All A Index's valuation levels: - PE ratio: 60th percentile (moderate level) - PB ratio: 10th percentile (low level) - Based on these metrics and short-term market trends, the model recommends a portfolio allocation of 50% for absolute return products [3][9] - **Model Evaluation**: The model effectively balances valuation considerations with market dynamics to guide portfolio allocation [3][9] --- Backtesting Results of Models 1. Industry Allocation Model - Recommended sectors: Hong Kong-listed innovative pharmaceuticals, automobiles, new consumption, technology (e.g., consumer electronics), banking, and gold stocks [2][3][9] 2. TWO BETA Model - Focus sectors: Technology (e.g., consumer electronics), banking, and gold stocks [2][3][9] 3. Timing System Signal - Moving average distance: -0.32% (absolute value < 3%, indicating a consolidation phase) [2][3][8] 4. Position Management Model - Recommended portfolio allocation: 50% for absolute return products [3][9]

Market Performance - The Sci-Tech 50 Index decreased by 1.47% over the last five trading days, while the ChiNext Index fell by 0.88%. In contrast, the large-cap value stocks rose by 0.48%, and the large-cap growth stocks declined by 0.40% [1] - The medical and biological sectors performed well, while the computer and machinery equipment sectors lagged behind [1] Risk Premium Analysis - The static PE of the CSI All Share Index minus the yield of 10-year government bonds indicates a risk premium. Historical extreme bottoms have shown this data to be at two standard deviations above the mean, with recent peaks at 4.17% on April 26, 2022, and 4.11% on January 19, 2024. As of May 23, 2025, the indicator stands at 3.84%, with the two standard deviation boundary at 4.76% [1] Valuation Levels - As of May 23, 2025, the CSI All Share Index's TTM PE is at the 51st percentile, with the SSE 50 and CSI 300 at 62% and 49%, respectively. The ChiNext Index is close to 11%, indicating a relatively low valuation level compared to historical averages [2] Long-term Market Trends - The Shenzhen 100 Index has experienced bear markets approximately every three years, followed by bull markets. The current adjustment, which began in Q1 2021, has shown sufficient time and space for a potential upward cycle [2] Fund Flow and Trading Activity - In the last five trading days, ETF funds saw an outflow of 24 billion yuan, while margin financing decreased by approximately 20 million yuan. The average daily trading volume across both markets was 1.1376 trillion yuan [3] AI and Machine Learning Applications - A convolutional neural network (CNN) has been utilized to model price and volume data, mapping learned features to industry themes. The latest focus is on sectors such as banking and dividends [2][10]

Investment Logic - The monthly timing model for the consumption sector under the Hong Kong Stock Connect shows a return of -1.66% for April 2025, outperforming the equal-weighted benchmark return of -2.21% and the CSI Hong Kong Stock Connect Consumption Index [1] - From November 2018 to April 2025, the strategy achieved an annualized return of 9.25%, with a maximum drawdown of -29.72% and a Sharpe ratio of 0.52, indicating superior performance across various metrics compared to the benchmark [1] - The strategy consistently generated positive excess returns in most years, effectively managing downside risk during periods of benchmark drawdown [1] Timing Strategy Framework - The timing strategy framework for the CSI Hong Kong Stock Connect Consumption Index is constructed based on dynamic macro event factors, focusing on the impact of China's macroeconomic conditions on the overall performance of Hong Kong consumption theme companies [2] - The strategy utilizes over 20 macroeconomic indicators across four dimensions: economy, inflation, monetary policy, and credit, to identify optimal event factors and data processing methods for each period [2] - Five macro factors were selected for their effectiveness in timing the CSI Hong Kong Stock Connect Consumption Index, with a scoring system to determine the timing position based on the proportion of bullish signals from these factors [2]

Group 1 - The core viewpoint of the article suggests that the A-share market is likely to maintain a range-bound fluctuation in the upcoming week, influenced by historical trends and current market indicators [1][2]. - The liquidity shock indicator for the CSI 300 index was reported at 2.63, indicating that current market liquidity is 2.63 times higher than the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 1.03, reflecting a reduced level of caution among investors regarding the short-term performance of the SSE 50 ETF [2]. Group 2 - The five-day average turnover rates for the SSE Composite Index and the Wind All A Index were 0.89% and 1.45%, respectively, indicating increased trading activity compared to historical levels [2]. - The RMB exchange rate fluctuated last week, with onshore and offshore rates increasing by 0.59% and 0.42%, respectively [2]. - In April, new RMB loans amounted to 280 billion, significantly lower than the consensus expectation of 764.44 billion and the previous value of 3.64 trillion [2]. - The M2 money supply grew by 8% year-on-year, surpassing the consensus expectation of 7.54% and the previous value of 7% [2]. Group 3 - Historical data shows that the probability of major A-share indices rising in the latter half of May is relatively low, with the SSE Composite Index, CSI 300, and ChiNext Index having average increases of -0.1%, -0.02%, and 1.71%, respectively [2]. - The Wind All A Index recently broke through the SAR reversal indicator on April 21, indicating a potential upward trend [2]. - The current market score based on the moving average strength index is 209, placing it in the 82.9 percentile since 2021 [2]. Group 4 - The A-share market experienced a recovery last week, with the SSE 50 Index rising by 1.22%, the CSI 300 Index by 1.12%, and the ChiNext Index by 1.38% [3]. - The overall market PE (TTM) stands at 19.0 times, which is at the 51.2 percentile since 2005 [3]. - The factor crowding metrics indicate a stable environment, with small-cap factor crowding at 0.91 and low valuation factor crowding at 0.53 [3].

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]