Core Insights - The report indicates that the market is currently in a consolidation phase, with a potential for further short-term adjustments as trading volume continues to decrease, but remains above critical thresholds [2][5][6] - The overall market (wind All A index) experienced a decline of 4.13% last week, with small-cap stocks (CSI 1000) dropping by 5.25% and mid-cap stocks (CSI 500) falling by 5.82% [6][7] - Key sectors showing resilience include telecommunications and banking, while materials such as non-ferrous metals and steel have underperformed significantly [6][7] Market Dynamics - The distance between the short-term (20-day) and long-term (120-day) moving averages has narrowed to 4.33%, indicating a bearish market sentiment with a negative profit effect of -4.35% [5][6][9] - The report highlights that the core variable to observe is the change in risk appetite, influenced by factors such as shifts in Federal Reserve interest rate expectations and ongoing geopolitical tensions in the Middle East [7][9] - A trading volume below 17 trillion is anticipated to signal a potential rebound in the market [5][7] Sector Allocation - The mid-term industry allocation model suggests focusing on sectors related to computing power, such as semiconductor equipment (ETF code 159516.SZ) and telecommunications (ETF code 515880.SH), as well as cyclical sectors like oil and gas (ETF code 159309.SZ) and energy chemicals (ETF code 159981.SH) [5][12] - In a defensive strategy, short-term attention should be given to banking ETFs and tourism ETFs [5][12] Valuation Metrics - The wind All A index's PE ratio is positioned around the 85th percentile, indicating a moderately high valuation level, while the PB ratio is at the 50th percentile, reflecting a medium valuation level [7][9] - Based on the current market conditions, a 50% allocation in absolute return products based on the wind All A index is recommended [5][7]

Quantitative Models and Construction Methods 1. Model Name: Timing System Model - **Model Construction Idea**: The 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 identify market trends and timing signals[2][7][12] - **Model Construction Process**: 1. Calculate the 20-day moving average and 120-day moving average of the Wind All A Index 2. Compute the distance between the two moving averages: $ Distance = \frac{MA_{20} - MA_{120}}{MA_{120}} $ 3. Define thresholds: If the absolute value of the distance is greater than 3%, it indicates a significant trend signal[7][12] 4. Incorporate additional metrics such as market trend line (6796 points) and profitability effect (-0.02%) to refine the signal[7][12] - **Model Evaluation**: The model effectively captures market oscillations and provides actionable timing signals during periods of market uncertainty[7][12] 2. Model Name: Mid-term Industry Allocation Model - **Model Construction Idea**: This model identifies industries with strong performance potential based on earnings trends and macroeconomic factors[6][8][13] - **Model Construction Process**: 1. Analyze earnings trends across industries to identify sectors with upward momentum 2. Incorporate macroeconomic indicators and policy drivers to refine sector selection 3. Highlight key sectors such as computing power (e.g., semiconductor equipment, communication), cyclical industries (e.g., oil and gas, energy chemicals), and agriculture[6][8][13] - **Model Evaluation**: The model provides a robust framework for sector rotation and aligns with defensive strategies during market uncertainty[6][8][13] --- Model Backtesting Results 1. Timing System Model - Moving average distance: 5.28% (greater than the 3% threshold)[7][12] - Market trend line: 6796 points[7][12] - Profitability effect: -0.02% (not yet positive)[7][12] 2. Mid-term Industry Allocation Model - Key sectors identified: - Computing power: Semiconductor equipment ETF (159516.SZ), Communication ETF (515880.SH) - Cyclical industries: Oil and gas ETF (159309.SZ), Energy chemicals ETF (159981.SH) - Agriculture: Agriculture ETF (562900.SH)[6][8][13] --- Quantitative Factors and Construction Methods 1. Factor Name: Moving Average Distance - **Factor Construction Idea**: Measures the relative distance between short-term and long-term moving averages to capture market momentum[7][12] - **Factor Construction Process**: 1. Calculate the 20-day and 120-day moving averages of the Wind All A Index 2. Compute the relative distance using the formula: $ Distance = \frac{MA_{20} - MA_{120}}{MA_{120}} $ 3. Use a threshold of 3% to determine significant signals[7][12] - **Factor Evaluation**: The factor is effective in identifying market trends and oscillations, providing a clear signal for timing decisions[7][12] --- Factor Backtesting Results 1. Moving Average Distance Factor - Current value: 5.28% (above the 3% threshold)[7][12]

Investment Rating - The industry investment rating is "Increase" with an expectation of a relative increase of over 10% compared to the benchmark index in the next 6 to 12 months [17]. Core Insights - The market is currently in an upward trend, with the core observation variable being the change in profit effect, which is at 1.91%, indicating a potential for continued market growth [5][8]. - The upcoming Two Sessions (Lianghui) period is expected to drive short-term policy focus, historically associated with stable market performance [5][8]. - The market has shown resilience despite geopolitical tensions in the Middle East, which may suppress risk appetite [5][8]. Summary by Sections Market Overview - The overall market (WIND All A Index) has shown an increase of 2.75% and reached a new high, with small-cap stocks (CSI 1000) rising by 4.34% and mid-cap stocks (CSI 500) by 4.32% [2][7]. - The steel sector has performed particularly well, with an increase of 11.8%, while the media sector has declined by 4.44% [2][7]. Timing System Analysis - The distance between the 20-day and 120-day moving averages is 6.28%, indicating a positive market trend, with the short-term average above the long-term average [2][5]. - The market trend line is positioned around 6812 points, suggesting a favorable environment for continued investment [5][8]. Sector Allocation - The industry trend configuration model suggests waiting for a reversal signal in the real estate chain (Construction Materials ETF code 159745.SZ) during the Two Sessions window, which may present short-term opportunities [6][15]. - The TWO BETA model continues to recommend the technology sector, particularly focusing on commercial aerospace (Satellite ETF code 563230.SH) for rebound opportunities [6][15]. - The performance trend model highlights the importance of focusing on the computing-related industry chain (Semiconductor Equipment ETF code 159516.SZ, Communication ETF code 515880.SH) as well as non-ferrous metals (Industrial Non-ferrous ETF code 560860.SH, Rare Earth ETF code 516150.SH) and chemicals (Chemical ETF code 159870.SZ) [6][15]. Valuation Metrics - The PE ratio of the WIND All A Index is near the 90th percentile, indicating a high valuation level, while the PB ratio is at the 50th percentile, suggesting a moderate valuation level [9][11]. - Based on the short-term trend assessment, an 80% allocation in absolute return products based on the WIND All A Index is recommended [9].

Quantitative Models and Construction Methods 1. Model Name: Timing System Signal - **Model Construction Idea**: The model uses the distance between the short-term and long-term moving averages of the WIND All A Index to determine market trends and timing signals [2][7][13] - **Model Construction Process**: 1. Define the short-term moving average (20-day) and long-term moving average (120-day) of the WIND All A Index 2. Calculate the distance between the two moving averages: $ Distance = \frac{Short\text{-}term\ MA - Long\text{-}term\ MA}{Long\text{-}term\ MA} $ 3. If the absolute value of the distance is greater than 3%, it indicates a significant trend signal [2][7][13] - **Model Evaluation**: The model effectively identifies market trends and provides actionable timing signals [2][7][13] 2. Model Name: Industry Trend Allocation Model - **Model Construction Idea**: This model identifies industry allocation opportunities based on medium-term reversal expectations and performance trends [6][8][15] - **Model Construction Process**: 1. Monitor medium-term reversal signals for specific industries, such as the real estate chain 2. Use performance trend analysis to identify industries with strong growth potential, such as technology, semiconductors, and chemicals 3. Recommend ETF products corresponding to these industries for allocation [6][8][15] - **Model Evaluation**: The model provides clear industry allocation guidance and captures sectoral opportunities effectively [6][8][15] 3. Model Name: Position Management Model - **Model Construction Idea**: This model determines the recommended equity allocation ratio based on valuation levels and market trends [9] - **Model Construction Process**: 1. Assess the PE and PB valuation levels of the WIND All A Index 2. Combine valuation levels with short-term market trends to determine the recommended equity allocation ratio 3. For example, with the current PE at the 90th percentile and PB at the 50th percentile, the model suggests an 80% equity allocation [9] - **Model Evaluation**: The model provides a systematic approach to position management, balancing valuation and trend considerations [9] --- Model Backtesting Results 1. Timing System Signal - Moving average distance: 6.28% (absolute value > 3%) - Market trend line: 6812 points - Profitability effect: 1.91% (significantly > 0) [2][7][13] 2. Industry Trend Allocation Model - Recommended sectors: - Real estate chain (e.g., Building Materials ETF: 159745.SZ) - Technology (e.g., Satellite ETF: 563230.SH) - Semiconductors and communication (e.g., Semiconductor Equipment ETF: 159516.SZ, Communication ETF: 515880.SH) - Metals and chemicals (e.g., Industrial Metals ETF: 560860.SH, Rare Earth ETF: 516150.SH, Chemical ETF: 159870.SZ) [6][8][15] 3. Position Management Model - Recommended equity allocation: 80% [9] --- Quantitative Factors and Construction Methods 1. Factor Name: Profitability Effect - **Factor Construction Idea**: Measures the market's profitability to assess upward momentum [2][7][13] - **Factor Construction Process**: 1. Calculate the profitability effect as a percentage of profitable stocks in the market 2. A positive profitability effect indicates upward momentum [2][7][13] - **Factor Evaluation**: The factor effectively captures market sentiment and momentum [2][7][13] --- Factor Backtesting Results 1. Profitability Effect - Current value: 1.91% (significantly > 0) [2][7][13]

Group 1 - The A-share market showed mixed performance on February 27, with over 200 ETFs rising by more than 1% [1] - Metal sector ETFs experienced significant gains, with some ETFs accumulating over 30% increase year-to-date [2][3] - Semiconductor equipment sector ETFs faced notable declines, with many products dropping over 2% [2][3] Group 2 - On February 27, metal sector ETFs dominated the performance rankings, with all top ten ETFs rising over 4%, and more than half of these ETFs showing year-to-date gains exceeding 20% [3][4] - The semiconductor equipment sector saw eight ETFs among the top ten largest declines, primarily tracking semiconductor materials and equipment indices [4][6] Group 3 - On February 26, the overall ETF market experienced a net outflow of over 29 billion yuan, with broad-based ETFs leading the outflows [2][7] - The Hang Seng Technology ETF attracted significant inflows, with a net inflow of 1.315 billion yuan on February 26, marking the third consecutive day of inflows [7][8] - Broad-based ETFs faced substantial net outflows, with six ETFs experiencing outflows exceeding 1 billion yuan each on February 26 [7][9] Group 4 - Investment opportunities in the technology sector are expected to diversify in 2026, with a focus on AI and semiconductor breakthroughs [10] - The A-share market may see improved liquidity indicators compared to pre-Spring Festival levels, suggesting a potential spring rally [10]

Market Overview - The A-share market showed mixed performance with the Shanghai Composite Index rising by 0.39%, while the Shenzhen Component Index fell by 0.06%, and the ChiNext Index decreased by 1.04% [1] - The total trading volume in the Shanghai, Shenzhen, and Beijing markets reached 2.51 trillion yuan, slightly down from the previous day [1] Sector Performance - The rare metals and rare earth sectors led the gains, with several ETFs in these categories rising over 4%, including the Rare Metals ETF from ICBC, which increased by 4.96% [2] - Other notable performers included the Rare Metals ETFs from Jiashi and Guangfa, both up by 4.68%, and the Rare Metals ETF Fund from Huafu, which rose by 4.55% [2] - The non-ferrous metals sector also saw significant increases, with the Non-Ferrous Metals ETF from Tianhong up by 4.28% and the Non-Ferrous ETF from Huashan rising by 4.17% [3] Declining Sectors - The semiconductor equipment sector experienced a notable pullback, with several ETFs in this category showing declines, including the Semiconductor Equipment ETF from Guotai, which fell by 2.16% [4] - The small-cap growth style, particularly in the ChiNext and National 2000 indices, faced pressure as funds rotated out of previously popular themes into cyclical resource sectors for safety [4] Trading Activity - The Short-term Bond ETF from Haifutong led trading activity with a turnover of 576.06 billion yuan, followed by the National Debt ETF from Huaxia at 168.25 billion yuan and the Sci-Tech Bond ETF from Nanfang at 137.24 billion yuan [5] - The turnover rates were high for bond ETFs, with the Sci-Tech Bond ETF from Nanfang achieving a turnover rate of 156.87%, indicating strong liquidity in this segment [6] ETF Issuance - Six new ETFs are set to launch, focusing on Hong Kong Stock Connect and Sci-Tech sectors, with fundraising starting on March 2, 2026 [8] - The Cash Flow ETF from Great Wall will track the National Index of Free Cash Flow, while the Engineering Machinery ETF from Penghua will follow the China Securities Engineering Machinery Theme Index [9] - Cross-border products include the Internet ETFs from ICBC and Xingye, which will track the China Securities Hong Kong Stock Connect Internet Index [9][10]

Market Overview - The Shanghai Composite Index rose by 0.39%, while the ChiNext Index fell by 1.04% [1] - The total trading volume in the Shanghai, Shenzhen, and Beijing markets was 25,055 billion yuan, a decrease of 512 billion yuan compared to the previous day [1] - Over 3,200 stocks across the three markets experienced gains [1] Sector Performance - Rare metal stocks saw a surge, with significant increases in magnesium and tungsten stocks [1] - The rare metal ETFs, including the Rare Metal ETF and Rare Earth ETF, reported gains of 4.68% and 4.11% respectively [1] - The power sector showed strength, with the Power ETF and Green Power ETF rising by 2.73% and 2.53% respectively [1] - The steel sector also performed well, with the Steel ETF increasing by 2.45% [1] Declining Sectors - The ChiNext Growth ETF and the Deep Growth ETF both fell by 2% [1] - The semiconductor sector experienced declines, with the Semiconductor Equipment ETF and the Sci-Tech Semiconductor ETF dropping by 2.16% and 2.15% respectively [1]

Group 1 - The investment logic in the computing power industry is shifting towards "efficiency first and commercial conversion rate first," moving from "unlimited long-term scale expansion" to "precise investment that is bounded, feasible, and highly aligned with commercialization pace" [1] - The global semiconductor industry is experiencing a systematic price increase across all segments, characterized by full-chain transmission and resonance both domestically and internationally [1] - The current electronic industry chain is undergoing a "three-line advance" in systematic price increases, including demand-driven (e.g., storage, CPU), cost transmission (e.g., CCL/electronic cloth, passive components), and supply contraction (e.g., niche storage, mature foundry) [1] Group 2 - The semiconductor equipment ETF (159516) tracks the semiconductor materials and equipment index (931743), focusing on key materials and equipment suppliers necessary for semiconductor manufacturing and packaging testing [1] - This index reflects the overall performance of listed companies in the upstream sector of the semiconductor industry [1]

Market Overview - The three major A-share indices experienced a collective decline in the morning session, with the Shanghai Composite Index down by 0.17%, the Shenzhen Component Index down by 0.68%, and the ChiNext Index down by 1.46% [1] - The North China 50 Index fell by 0.74%, and the total trading volume in the Shanghai and Shenzhen markets reached 1.5966 trillion yuan, a decrease of 53.2 billion yuan compared to the previous day [1] - Over 2,300 stocks in the market saw an increase [1] Sector Performance - The sectors that performed well included small metals, computing power leasing, cloud computing, coal mining and processing, cross-border payments, steel, photovoltaic equipment, AI applications, and tourism and hotel industries [1] - Conversely, the sectors that faced declines included paper making, PCB, CPO, storage chips, batteries, photolithography machines, and PET copper foil [1] ETF Performance - In the ETF market, AI applications showed a rebound, with the ChiNext Software ETF from Huaxia rising by 2.9%, the Software ETF increasing by 2.33%, and the Online Consumption ETF from Southern rising by 1.84% [1] - The small metals sector also strengthened, with the Rare Earth ETF from Jiashi and the Rare Metals ETF rising by 2.57% and 2.52%, respectively [1] - The computing power leasing sector saw gains, with the Computer ETF from Southern and the Big Data ETF increasing by 2.5% and 2.4%, respectively [1] - Growth sectors faced declines, with the ChiNext Growth ETF and the Shenzhen Growth ETF from Dacheng falling by 3% and 2.79% [1] - The semiconductor equipment sector experienced a pullback, with various ETFs in this category declining between 2.20% and 2.77% [1]

Core Viewpoint - The semiconductor equipment ETF (159516) has seen a significant inflow of over 54 million units, driven by ongoing expansions in advanced processes and memory production, indicating strong mid-to-long-term growth narratives in the semiconductor equipment sector [1] Group 1: Market Activity - On February 26, the semiconductor equipment ETF (159516) recorded a net inflow of 54 million units, reflecting aggressive capital allocation by investors [1] - Institutions indicate that the semiconductor equipment sector is a clear beneficiary of expansions in advanced processes and memory production, with multiple catalysts such as new listings and market share gains from Japanese manufacturers [1] Group 2: Industry Dynamics - The current narrative surrounding semiconductor equipment differs from previous cycles of recovery or domestic substitution, as it is fundamentally benefiting from the high demand driven by global AI advancements [1] - The semiconductor equipment ETF (159516) tracks the semiconductor materials and equipment index (931743), which focuses on the materials and equipment sectors within the semiconductor industry, selecting listed companies involved in semiconductor material R&D, production, and equipment manufacturing [1] Group 3: Investment Implications - The index reflects the overall performance of listed companies in the upstream semiconductor sector, characterized by high technical barriers and growth potential, making it a crucial indicator for capturing development opportunities in this field [1]