Core Insights - The report highlights the April calendar effect, indicating that the large-cap style tends to outperform in most years, while sectors such as beauty, food and beverage, home appliances, and banking are expected to perform relatively well [2][7] - The average absolute monthly return for the Tonghuashun All A (weighted) index in April over the past 10 years is -1.6%, suggesting a general decline in the market during this month [7][8] - Small-cap and micro-cap styles have significantly underperformed compared to large-cap styles, indicating a structural characteristic in the market [7][10] Industry Performance - The sectors that are expected to outperform in April include beauty, food and beverage, home appliances, banking, and pharmaceuticals, while sectors such as computer, comprehensive, light industry, military, and textile are anticipated to lag [7][13] - The report provides a detailed analysis of the average monthly excess returns of various industry indices compared to the Tonghuashun All A (weighted) index over the past 10 years, showing that certain sectors consistently yield better returns [13][15] - Specific data points indicate that the beauty sector has an average excess return of 2.8%, while the computer sector shows a negative average excess return of -0.7% in April [13][15]

Market Overview - The market is currently in a state of stock game with significant funding divergence, as indicated by the liquidity shock index for the CSI 300, which was -0.49 last Friday, lower than the previous week at 0.49, suggesting current market liquidity is above the average level of the past year by -0.49 standard deviations [1] - The PUT-CALL ratio for the SSE 50 ETF options has been rising, reaching 0.83 last Friday, up from 0.67 the previous week, indicating increased caution among investors regarding the short-term performance of the SSE 50 ETF [1] - The average turnover rates for the SSE Composite Index and Wind All A-shares were 1.38% and 1.94%, respectively, indicating a decrease in trading activity, positioned at the 78.04% and 81.46% percentile since 2005 [1] Macro Factors - The RMB exchange rate fluctuated last week, with onshore and offshore rates showing weekly declines of -0.42% and -0.2%, respectively [1] - The U.S. stock market experienced a downward trend, with the Dow Jones Industrial Average, S&P 500, and Nasdaq indices reporting weekly returns of -0.9%, -2.12%, and -3.23% [1] - The National Bureau of Statistics reported that profits of large-scale industrial enterprises in China reached 1.02 trillion yuan in January-February 2026, a year-on-year increase of 15.2%, accelerating by 14.6 percentage points compared to the previous year [1] Market Sentiment - The A-share market experienced some fluctuations and divergence last week, with heightened risk aversion due to ongoing geopolitical tensions, which have suppressed short-term risk appetite [1] - Technical analysis indicates multiple intraday reversals in the A-share market, suggesting significant funding divergence and a stock game state, leading to a low probability of upward trends in the short term [1] Factor Analysis - The overall market PE (TTM) stands at 22.5 times, positioned at the 77.5% percentile since 2005 [2] - The small-cap factor's congestion level has decreased to -0.11, while the low valuation factor's congestion level is at -0.54, indicating a shift in market dynamics [2] - Industry congestion levels are relatively high in sectors such as comprehensive, communication, non-ferrous metals, basic chemicals, and oil & petrochemicals, with the latter two sectors showing a significant increase in congestion [2]

Market Overview - The A-share market indices all rose today, with the Shanghai Composite Index increasing by 1.30% to close at 3931.84 points, the Shenzhen Component Index rising by 1.95% to 13801.00 points, the Sci-Tech 50 up by 1.91% to 1315.41 points, and the ChiNext Index gaining 2.01% to 3316.97 points [1][13] - Overall, 4871 stocks rose while 559 stocks fell, with a total trading volume of 21798 billion yuan, a decrease of 970 billion yuan compared to the previous trading day [1] Industry Performance - Among the Shenwan first-level industries, the leading sectors included Comprehensive, Communication, Non-ferrous Metals, Electronics, and Social Services, with respective increases of 3.87%, 3.71%, 2.97%, 2.65%, and 2.61%. The Coal and Oil & Petrochemical industries experienced declines of 1.29% and 0.44% respectively [1][13] Sector Highlights - The top-performing concept sectors today included Cross-Strait Integration, Optical Communication, Optical Modules (CPO), East Data West Computing, High-Speed Copper Connections, Glass Fiber, Thermal Power, Hydropower, IDC (Computing Power Leasing), Mohr Thread, AI Computing Power, Rare Earths, Selected Aviation Transport, Huakun Zhenyu, and Cloud Computing [2][12]

Group 1: Macroeconomic Liquidity - The US dollar index continued to rise, and the degree of inversion in the China-US interest rate spread deepened, with inflation expectations also increasing [2][16] - Offshore US dollar liquidity has marginally tightened, while the domestic interbank funding situation remains balanced [2][23] Group 2: Market Trading Activity and Volatility - Market trading activity has decreased, with major indices experiencing increased volatility; sectors such as oil and petrochemicals, electric new energy, public utilities, and construction are above the 90th percentile in trading activity [3][28] - The volatility of major indices, including the CSI 300 and ChiNext, has continued to rise, with steel and military sectors also showing volatility above the 90th historical percentile [3][35] Group 3: Institutional Research - The banking, electronics, electric new energy, computing, and automotive sectors are leading in research activity, with banking and automotive sectors showing a month-on-month increase in research heat [4][46] Group 4: Analyst Forecasts - Analysts have simultaneously raised net profit forecasts for the entire A-share market for 2026/2027, with increases noted in sectors such as electric new energy, non-ferrous metals, construction, machinery, and pharmaceuticals [5][19] - The proportion of stocks with upward revisions in net profit forecasts for 2026/2027 has increased across the A-share market [5][17] Group 5: Northbound Trading Activity - Northbound trading activity has decreased, continuing to net sell A-shares, with a notable increase in the buy/sell ratio for electric new energy, electronics, and automotive sectors [6][32] - Northbound trading primarily net bought coal and oil and petrochemical sectors, while net selling occurred in electronics, computing, and chemicals [6][33] Group 6: Margin Financing Activity - Margin financing activity has slightly increased but remains at a low level, with net buying primarily in electric new energy, chemicals, and computing sectors [7][35] - The proportion of financing purchases has increased across most sectors, with net buying focused on mid-cap growth and mid/small-cap value stocks [7][38] Group 7: Active Equity Funds and ETFs - Active equity funds have increased their positions, particularly in military, machinery, and automotive sectors, while reducing positions in non-ferrous metals, oil and petrochemicals, and steel [9][45] - ETFs have continued to experience net redemptions, particularly in broad-based indices like CSI 500, CSI 300, and ChiNext, while sectors such as electric power and public utilities saw net inflows [9][52]

Market Overview - On March 11, the Shanghai Composite Index rose by 0.25%, the CSI 300 increased by 0.64%, the STAR Market 50 fell by 1.37%, the CSI 1000 rose by 0.16%, the ChiNext Index increased by 1.31%, and the Hang Seng Index decreased by 0.24% [3][4] - The best-performing sectors on March 11 were coal (+2.53%), electric equipment (+2.43%), basic chemicals (+2.08%), utilities (+1.67%), and construction decoration (+1.63%). The worst-performing sectors were comprehensive (-1.98%), defense and military (-1.37%), media (-1.17%), electronics (-0.78%), and social services (-0.59%) [3][4] - The total trading volume of the A-share market on March 11 was 25,282.94 billion, with a net inflow of 3.448 billion HKD from southbound funds [3][4] Key Insights - The report discusses the strategy regarding the significant rise and subsequent fall of oil prices, questioning the future direction of the gold-oil ratio [2][5] - It is anticipated that the macro-friendliness of the gold-oil ratio may decline over the next six months, indicating a potential regression towards the mean [5] - The macro-friendliness of the gold-oil ratio is primarily influenced by factors such as the US dollar index, real interest rates on US Treasury bonds, and the US manufacturing PMI [5] - The report incorporates a variable for US dollar credit to assess the impact of the volatility of Trump’s policies, the appointment of the Federal Reserve Chair, and the record-high US Treasury bond issuance on dollar credit [5]

Market Overview - The A-share market showed a mixed performance today, with the Shanghai Composite Index rising by 0.25% to close at 4133.43 points, and the Shenzhen Component Index increasing by 0.78% to 14465.41 points. The ChiNext Index, however, fell by 1.37% to 1401.08 points, while the Growth Enterprise Market Index rose by 1.31% to 3349.53 points. Overall, 2055 stocks rose, 3284 stocks fell, and 145 stocks remained unchanged, with a total trading volume of 25084 billion yuan, an increase of 1105 billion yuan compared to the previous trading day [1][10]. Industry Performance - Among the Shenwan first-level industries, coal, electric equipment, basic chemicals, and public utilities saw the highest gains, with increases of 2.53%, 2.43%, 2.08%, and 1.67% respectively. Conversely, the comprehensive, defense military industry, and media sectors experienced declines of 1.98%, 1.37%, and 1.17% respectively [1][10]. Concept Sector Performance - The leading concept sectors today included photovoltaic inverters, lithium battery electrolytes, selected chemical raw materials, selected chemical fibers, power batteries, major infrastructure central enterprises, sodium-ion batteries, energy storage, lithium battery anodes, central enterprise coal, high transfer, cultivated diamonds, selected coal mining, solid-state batteries, and lithium batteries [2].

Group 1 - In the first week of March, the total net inflow of funds into the market was 49.3 billion, an increase from the previous week's inflow of 44.2 billion [1] - The short-term sentiment indicator is at a medium-high level since 2005, while the long-term sentiment indicator is at a medium-low level since 2005 [1][2] - From an industry perspective, the sectors with the highest trading volume share in the past week were defense and military, communication, and electric power equipment, with shares of 99%, 98%, and 97% respectively [2][14] Group 2 - In terms of fund inflows, the financing balance decreased by 24.2 billion, public fund issuance increased by 2.7 billion, ETF net subscriptions were 1.6 billion, and northbound funds estimated a net outflow of 9.2 billion [8] - The long-term sentiment indicator shows that the A-share risk premium was 2.49%, placing it at the 46th percentile historically, while the dividend yield of the CSI 300 index (excluding finance) was 1.22, at the 6th percentile historically [2][14] - The sectors with the highest financing transaction share were machinery and equipment at 89%, social services at 79%, and electric power equipment at 75%, while the lowest were banking at 7%, comprehensive at 8%, and coal at 14% [2][14]

Quantitative Models and Construction Methods 1. Model Name: Sentiment Model - **Model Construction Idea**: The sentiment model is designed to measure the strength of market sentiment by analyzing factors such as the proportion of limit-up and limit-down stocks, and the profitability of high-frequency trading strategies[12][16] - **Model Construction Process**: The sentiment model is built using factors related to market sentiment, including: - Proportion of net limit-up stocks - Next-day returns of limit-down stocks - Proportion of limit-up stocks - Proportion of limit-down stocks - Returns of high-frequency trading strategies The model assigns scores to these factors, with a maximum score of 5. The sentiment model score for the current period is 0[12][16] - **Model Evaluation**: The sentiment model indicates a weakening of market sentiment, as reflected by the score of 0[12][16] 2. Model Name: Trend Model - **Model Construction Idea**: The trend model aims to capture the directional movement of the market by analyzing price trends and other technical indicators[12] - **Model Construction Process**: The trend model generates signals based on the analysis of market trends. For the current period, the trend model provides a positive signal, indicating an upward trend in the market[12] - **Model Evaluation**: The trend model continues to emit positive signals, suggesting a favorable market trend[12] 3. Model Name: High-Frequency Capital Flow Model - **Model Construction Idea**: This model uses high-frequency capital flow data to generate buy and sell signals for major broad-based indices[12][16] - **Model Construction Process**: The model evaluates the capital flow trends for indices such as CSI 300, CSI 500, CSI 1000, and CSI 2000. The signals are categorized as aggressive long, aggressive short, conservative long, and conservative short. For the current period, the model emits negative signals for all indices[12][16] - **Model Evaluation**: The high-frequency capital flow model continues to emit negative signals, indicating a bearish outlook for the indices[12][16] --- Model Backtesting Results 1. Sentiment Model - Sentiment model score: 0 (out of 5)[12][16] 2. Trend Model - Trend model signal: Positive[12] 3. High-Frequency Capital Flow Model - CSI 300: Aggressive short (-1), Conservative short (-1)[12][16] - CSI 500: Aggressive short (-1), Conservative short (-1)[12][16] - CSI 1000: Aggressive short (-1), Conservative short (-1)[12][16] - CSI 2000: Aggressive short (-1), Conservative short (-1)[12][16] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Measures the performance and crowding of small-cap stocks[17][19] - **Factor Construction Process**: The small-cap factor's crowding is assessed using four metrics: - Valuation spread - Pairwise correlation - Market volatility - Return reversal The composite score for the small-cap factor is -0.06[17][19] - **Factor Evaluation**: The small-cap factor shows a slight decline in crowding, as indicated by the composite score[17][19] 2. Factor Name: Low-Valuation Factor - **Factor Construction Idea**: Evaluates the performance and crowding of low-valuation stocks[17][19] - **Factor Construction Process**: The low-valuation factor's crowding is assessed using the same four metrics as the small-cap factor. The composite score for the low-valuation factor is -0.67[17][19] - **Factor Evaluation**: The low-valuation factor exhibits a higher level of crowding, as reflected by the negative composite score[17][19] 3. Factor Name: High-Profitability Factor - **Factor Construction Idea**: Measures the performance and crowding of high-profitability stocks[17][19] - **Factor Construction Process**: The high-profitability factor's crowding is assessed using the same four metrics. The composite score for the high-profitability factor is 0.13[17][19] - **Factor Evaluation**: The high-profitability factor shows a moderate level of crowding, with a positive composite score[17][19] 4. Factor Name: High-Growth Factor - **Factor Construction Idea**: Evaluates the performance and crowding of high-growth stocks[17][19] - **Factor Construction Process**: The high-growth factor's crowding is assessed using the same four metrics. The composite score for the high-growth factor is 0.21[17][19] - **Factor Evaluation**: The high-growth factor demonstrates a relatively low level of crowding, as indicated by the positive composite score[17][19] --- Factor Backtesting Results 1. Small-Cap Factor - Composite crowding score: -0.06[17][19] 2. Low-Valuation Factor - Composite crowding score: -0.67[17][19] 3. High-Profitability Factor - Composite crowding score: 0.13[17][19] 4. High-Growth Factor - Composite crowding score: 0.21[17][19]

Core Viewpoint - Japanese companies are adopting a unique approach by retaining older employees, referred to as "window-side workers," who are paid to come to work but have minimal responsibilities, contrasting sharply with Western firms that are aggressively pursuing efficiency and implementing strict five-day work weeks [2][3]. Group 1: Characteristics of "Window-Side Workers" - "Window-side workers" are typically older employees, often in their 50s and 60s, who have been marginalized due to poor performance or redundancy, yet are still compensated handsomely [2][3]. - These employees are characterized by their loyalty and non-confrontational nature, making them less likely to complain about their reduced roles [3]. Group 2: Employment Trends and Statistics - Japan has a high employment rate among older individuals, with over 25% of those aged 65 and above still working as of 2022, compared to less than 20% in the U.S. and under 10% in the U.K. [3][4]. - Approximately 80% of Japanese employees express a desire to continue working post-retirement, with 70% preferring to stay with their current employer rather than seek new opportunities [3]. Group 3: Government Initiatives - The Japanese government has revised the "Law Concerning Stabilization of Employment of Older Persons" to encourage companies to provide employment opportunities for individuals up to the age of 70 [4]. - Subsidies are being offered to employers who support these initiatives, promoting the retention of older workers [4]. Group 4: Perception Among Younger Employees - A survey indicated that nearly 50% of younger employees in Japan have observed "window-side workers" in their companies, with common activities including smoking, snacking, chatting, and browsing the internet [5]. - Despite the cultural respect for older employees, younger generations are growing impatient, with 90% believing that these unproductive workers negatively impact workplace morale and increase the workload for others [6]. Group 5: Psychological Impact on the Workplace - The presence of "window-side workers" may provide a sense of psychological safety for younger employees, reducing the fear of sudden layoffs during challenging business periods [7].

Group 1 - The report indicates a downward trend in credit bond yields, with the overall change in issuance guidance rates ranging from -6BP to 1BP [1][15] - In February, the issuance scale of credit bonds decreased month-on-month due to holiday factors, with all varieties showing a decline in issuance amounts [1][12] - The net financing amount for credit bonds decreased month-on-month, with corporate bonds and targeted instruments showing an increase, while other varieties saw a decrease [1][12][13] Group 2 - The secondary market saw a decline in transaction volume for credit bonds, with a total transaction amount of 22,665.99 billion, down 39.05% month-on-month [1][17] - Credit spreads for most varieties narrowed in February, with many varieties' spreads at historical low levels [1][20][26] - The report suggests that the absolute yield perspective indicates a continuation of the recovery trend for credit bonds, driven by insufficient supply and relatively strong demand [1][62] Group 3 - The report highlights the recent policy adjustments in Shanghai's real estate market aimed at promoting stable and healthy development, including easing purchase restrictions and increasing public housing loan limits [2][63] - Continuous optimization of real estate policies by central and local governments is expected to support the stabilization of the real estate market, transitioning from a phase of large-scale expansion to one focused on quality improvement [3][65] - The report emphasizes the importance of focusing on high-quality development in the real estate sector, with an expectation of further policy announcements to support this transition [3][65][66]