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]

Core Viewpoint - The article discusses the recent talent exodus at Huabao Fund, highlighting the challenges the company faces in maintaining its competitive edge in the ETF and active equity investment sectors, as well as the implications of its management structure and strategic direction [2][4][26]. Talent Exodus - Huabao Fund has experienced significant departures of key personnel, including fund managers and investment directors, with notable figures like Qi Zhen and Hu Jie leaving for Tianhong Fund [2][3]. - The company has seen a pattern of talent loss, reminiscent of the 2021 exodus, which included several core fund managers who have since joined leading firms in the industry [3][19]. Competitive Challenges - Despite having a solid foundation in ETF and active equity investments, Huabao Fund has struggled to establish a stable competitive advantage, particularly as competition in the ETF market intensifies [4][10]. - The company’s ETF products, while initially successful, have not kept pace with industry growth, leading to a decline in its market ranking from 8th in 2024 to 11th in 2025 [10][11]. ETF Business Performance - Huabao Fund's non-cash ETF scale has grown significantly from 164 million yuan in 2019 to 130 billion yuan in 2025, but its industry ranking has fluctuated, indicating a lack of sustained competitive performance [9][10]. - The company’s focus on traditional industries in its ETF offerings has limited its growth potential in the current technology-driven market, where competitors have capitalized on more innovative products [13][14]. Management and Strategic Direction - The transition from a general manager-led structure to a chairman-led governance model has not yielded significant improvements in business performance, as the new leadership lacks deep experience in the public fund industry [26][28]. - The strategic focus on expanding into active equity and fixed income has not translated into tangible results, with the company struggling to enhance its active management capabilities [30][32]. Investment Strategy Issues - Huabao Fund's broad coverage of industry ETFs lacks focus on specific growth sectors, which has hindered its ability to capitalize on emerging market opportunities [15][16]. - The company’s reliance on a top-down investment approach has proven less effective in rapidly evolving sectors, where detailed research and agile responses are crucial for success [16][21].

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]

Group 1: Report Industry Investment Rating - No relevant information Group 2: Core Viewpoints of the Report - The strategy is based on two research reports, constructing a strategy portfolio of industry and theme ETFs [2] - From 20260302 - 20260306, the strategy's cumulative net return was about -2.24%, and the excess return relative to the CSI 300 ETF was about -1.01%. From October 14, 2024, the out - of - sample cumulative return of the strategy was about 35.44%, and the cumulative excess relative to the CSI 300 ETF was about 11.95% [3] - In the week of March 9, 2026, the model recommends allocating sectors such as joint - stock banks, power, and securities. The strategy will newly hold products like Bank ETF, Green Power ETF, etc., and continue to hold products like Coal ETF [12] Group 3: Summary by Relevant Catalogs ETF Strategy Portfolio Information - The strategy portfolio includes multiple ETFs, such as Bank ETF (market value: 14.61 billion yuan), Green Power ETF (market value: 5.13 billion yuan), etc. The holding situation includes调入 (newly included) and 继续持有 (continue to hold). Different ETFs have different heavy - position Shenwan industries and corresponding weights, as well as weekly and daily timing signals [3] Performance Tracking - During 20260302 - 20260306, the strategy's cumulative net return was about -2.24%, and the excess return relative to the CSI 300 ETF was about -1.01%. From October 14, 2024, the out - of - sample cumulative return of the strategy was about 35.44%, and the cumulative excess relative to the CSI 300 ETF was about 11.95% [3] Future Recommended Allocation - In the week of March 9, 2026, the model recommends allocating sectors such as joint - stock banks, power, and securities. The strategy will newly hold products like Bank ETF, Green Power ETF, Financial Real Estate ETF Guotou Ruixin, Grid Equipment ETF, Central Enterprise ETF ICBC, etc., and continue to hold products like Coal ETF [12]

Investment Rating - The industry rating is "Increase" with an expectation of a growth rate exceeding 10% relative to the benchmark index over the next 6 to 12 months [19]. Core Insights - The market has entered a consolidation phase, with the core observation variable being changes in risk appetite. The upcoming end of the Two Sessions may lead to a decrease in risk appetite, compounded by ongoing conflicts in the Middle East and rising oil prices [3][11]. - The Wind All A Index has seen a decline of 2.3% over the past week, with small-cap stocks represented by the CSI 1000 dropping 3.64% and mid-cap stocks by the CSI 500 falling 3.44% [3][9]. - The short-term market environment is characterized by a narrowing distance between the 20-day and 120-day moving averages, indicating a potential continuation of the downward trend [8][10]. Summary by Sections Market Overview - The Wind All A Index is currently in a consolidation phase, with a PE ratio at the 90th percentile, indicating a high valuation level, while the PB ratio is at the 50th percentile, suggesting a moderate valuation level [12][14]. - The market trend line is positioned around 6790 points, with a negative profit effect recently recorded at -0.1% [10][17]. Sector Allocation - The mid-term industry allocation model continues to recommend the technology sector, particularly focusing on commercial aerospace (satellite ETF 563230.SH) for rebound opportunities. The performance trend model highlights the importance of the computing-related industry chain (semiconductor equipment ETF 159516.SZ, communication ETF 515880.SH) and cyclical sectors (oil and gas ETF 159309.SZ, energy and chemicals ETF 159981.SH), as well as agriculture (agriculture ETF 562900.SH) [7][9][11]. - Additionally, a defensive strategy suggests short-term attention on bank ETFs [9][11]. Trading Strategy - The report suggests maintaining a 60% position in absolute return products based on the Wind All A Index, reflecting the current market conditions and valuation metrics [12][19]. - The market is expected to remain in a consolidation phase, with potential adjustments still in play, and a wait for trading volume to drop below 2 trillion is advised for a possible effective rebound [3][11].

- The report defines a 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[3][8][10] - The latest data shows the 20-day moving average at 6784 points and the 120-day moving average at 6432 points, with the short-term moving average still above the long-term moving average[3][8][10] - The difference between the two moving averages is 5.47%, with the absolute value of the distance continuing to be greater than 3%[3][8][10] - The market trend line is around 6790 points, and the profitability effect has just turned negative at -0.1%, indicating the market has entered a volatile pattern[3][8][10] - The core observation variable in a volatile market pattern is the change in risk appetite[3][8][10] - The report suggests that the risk appetite may decrease as the Two Sessions come to an end next week, and the ongoing war in the Middle East and the sharp rise in oil prices will suppress risk appetite[3][8][10] - The market rebounded on Thursday and Friday with reduced volume, but the reduction in trading volume is still below the critical value of the model, indicating the possibility of a continuation of the decline[3][8][10] - The report recommends waiting for the trading volume to shrink below 2 trillion yuan to expect an effective rebound[3][8][10] - The mid-term industry allocation model, TWO BETA, continues to recommend the technology sector, focusing on the oversold rebound opportunities in commercial aerospace (satellite ETF 563230.SH)[7][9][11] - The performance trend model suggests focusing on the computing power-related industrial chain (semiconductor equipment ETF 159516.SZ, communication ETF 515880.SH), as well as the cyclical (oil and gas ETF 159309.SZ, energy and chemical ETF 159981.SH) and agricultural (agriculture ETF 562900.SH) sectors[7][9][11] - In addition, the report suggests paying attention to the banking ETF in the short term under a defensive strategy[7][9][11] - The valuation indicators show that the PE of the Wind All A Index is near the 90th percentile, indicating a relatively high level, and the PB is near the 50th percentile, indicating a medium level[12] - Based on the short-term trend judgment, the report suggests an absolute return product with the Wind All A Index as the main stock allocation subject to maintain a 60% position[12]

1. Report Industry Investment Rating - No relevant information provided 2. Core Viewpoints of the Report - The strategy is based on the reports "Strategy Portfolio Report under Industry Rotation: Quantitative Analysis from the Perspective of Industry Style Continuity and Switching" (20241007) and "Research on the Overview and Allocation Methods of the Stock - type ETF Market: Taking the ETF Portfolio Based on the Industry Rotation Strategy as an Example" (20241013), constructing a strategy portfolio based on industry and theme ETFs [2] - From 20260224 - 20260227, the cumulative net return of the strategy was about 0.44%, and the excess return relative to the CSI 300 ETF was about - 0.71%. From October 14, 2024 to now, the cumulative out - of - sample return of the strategy was about 38.54%, and the cumulative excess return relative to the CSI 300 ETF was about 13.71% [5] - In the week of 20260302, the model recommends allocating sectors such as real estate development, cement, and batteries. In the next week, the strategy will newly hold products such as Building Materials ETF, Battery ETF Huitianfu, Bank ETF, and Game ETF, and continue to hold products such as Real Estate ETF and Tourism ETF [13] 3. Summary by Relevant Catalogs 3.1 Strategy Update - The strategy constructs a portfolio based on industry and theme ETFs, with reference to two previous research reports [2] 3.2 ETF Portfolio Information | Fund Code | ETF Name | ETF Market Value (billion yuan) | Holding Status | Heavy - held SW Industry and Weights | Weekly Timing Signal | Daily Timing Signal | | --- | --- | --- | --- | --- | --- | --- | | 159707 | Real Estate ETF | 6.65 | Continue to hold | Real estate development (100%) | - 1 | - 1 | | 159745 | Building Materials ETF | 26.61 | Add | Cement (45.24%) | 1 | 1 | | 159796 | Battery ETF Huitianfu | 84.32 | Add | Batteries (64.18%) | - 1 | - 1 | | 512800 | Bank ETF | 114.46 | Add | Joint - stock banks (42.01%) | - 1 | - 1 | | 159869 | Game ETF | 122.27 | Add | Games (83.85%) | 1 | - 1 | | 159766 | Tourism ETF | 78.92 | Continue to hold | Aviation and airports (33.21%) | 0 | 0 | | 515220 | Coal ETF | 94.06 | Add | Coal mining (88.96%) | 1 | 1 | | 159328 | Home Appliance ETF E Fund | 1.21 | Add | White goods (43.01%) | - 1 | - 1 | | 515650 | Consumption 50 ETF | 36.27 | Add | Baijiu (28%) | - 1 | 0 | | 515760 | Zhejiang State - owned Assets ETF Huaxia | 1.44 | Add | City commercial banks (20.13%) | 0 | 0 | [3] 3.3 Performance Tracking - From 20260224 - 20260227, the cumulative net return of the strategy was about 0.44%, and the excess return relative to the CSI 300 ETF was about - 0.71% - From October 14, 2024 to now, the cumulative out - of - sample return of the strategy was about 38.54%, and the cumulative excess return relative to the CSI 300 ETF was about 13.71% [5] 3.4 Portfolio Adjustment and Recommendations - In the week of 20260302, the model recommends allocating sectors such as real estate development, cement, and batteries - In the next week, the strategy will newly hold products such as Building Materials ETF, Battery ETF Huitianfu, Bank ETF, and Game ETF, and continue to hold products such as Real Estate ETF and Tourism ETF [13]

- The report does not contain any quantitative models or factors related to quantitative investment analysis. The content primarily focuses on ETF performance, scale changes, valuation, financing, and other market-related data[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84]

- The report primarily focuses on ETF performance, scale changes, and valuation metrics, with no mention of quantitative models or factors[1][2][3][4] - It provides detailed data on ETF weekly returns, net subscriptions/redemptions, and valuation percentiles across various categories like broad-based, sector, and thematic ETFs[2][3][36] - Specific thematic ETFs such as AI, chip, and photovoltaic ETFs are highlighted for their performance and valuation metrics, but no quantitative models or factors are discussed[19][36][45]

1. Report Industry Investment Rating - No information provided regarding the report industry investment rating 2. Core Viewpoints of the Report - Last week (from February 2, 2026, to February 6, 2026), the median weekly return of equity ETFs was -1.71%. Among broad - based ETFs, the Shanghai Stock Exchange 50 ETF had the smallest decline, and among sector ETFs, consumer ETFs had the highest return. Among hot - topic ETFs, photovoltaic ETFs had the highest return. Last week, equity ETFs had a net redemption of 2.056 billion yuan, with the Science and Technology Innovation Board ETF having the highest net subscription among broad - based ETFs, technology ETFs having the highest net subscription among sector ETFs, and AI ETFs having the highest net subscription among topic - based ETFs. As of last Friday, Huaxia, E Fund, and Huatai - Peregrine ranked in the top three in terms of the total scale of listed, non - monetary ETFs [1][2][61] 3. Summary According to Relevant Catalogs ETF Performance - The median weekly return of equity ETFs last week was -1.71%. Among broad - based ETFs, the Shanghai Stock Exchange 50 ETF had a median change of -0.82%, the smallest decline. By sector, consumer ETFs had a median change of 0.10%, the highest return. By topic, photovoltaic ETFs had a median change of 3.09%, the highest return. The median changes of bond, money - market, cross - border, and commodity ETFs were 0.02%, 0.02%, -2.19%, and -6.07% respectively [1][12][18] ETF Scale Changes and Net Subscriptions/Redeemptions - As of last Friday, the scales of equity, cross - border, and bond ETFs were 3.097 trillion yuan, 1.0195 trillion yuan, and 721.3 billion yuan respectively. The scales of commodity and money - market ETFs were relatively small, at 322.9 billion yuan and 160 billion yuan respectively. Among broad - based ETFs, the CSI 300 and A500 ETFs had relatively large scales. Last week, equity ETFs had a net redemption of 2.056 billion yuan and a total scale reduction of 80.468 billion yuan; money - market ETFs had a net subscription of 6.311 billion yuan and a total scale increase of 6.325 billion yuan. Among broad - based ETFs, the Science and Technology Innovation Board ETF had the highest net subscription of 5.501 billion yuan, and its scale decreased by 5.248 billion yuan; the CSI 500 ETF had the highest net redemption of 11.647 billion yuan, and its scale decreased by 15.917 billion yuan. By sector, technology ETFs had the highest net subscription of 10.405 billion yuan, and their scale decreased by 18.479 billion yuan; cyclical ETFs had the highest net redemption of 9.527 billion yuan, and their scale decreased by 21.091 billion yuan. By hot - topic, AI ETFs had the highest net subscription of 4.472 billion yuan, and their scale decreased by 3.271 billion yuan; photovoltaic ETFs had the highest net redemption of 0.8 billion yuan, and their scale decreased by 0.198 billion yuan [2][22][29] ETF Benchmark Index Valuation - As of last Friday, in the broad - based ETFs, the ChiNext and Shanghai Stock Exchange 50 ETFs had relatively low valuation quantiles. By sector, the large - finance and consumer ETFs had relatively moderate valuation quantiles. By sub - topic, the wine and new energy vehicle ETFs had relatively low valuation quantiles. Compared with the previous week, the valuation quantile of the wine ETF increased significantly [3][44][47] ETF Margin Trading - From last Monday to Thursday, the margin trading balance of equity ETFs decreased from 52.343 billion yuan in the previous week to 52.046 billion yuan, and the short - selling volume increased from 2.205 billion shares in the previous week to 2.312 billion shares. Among the top 10 ETFs in terms of average daily margin purchases and short - selling volumes, the securities ETFs and Science and Technology Innovation Board ETFs had relatively high average daily margin purchases, and the CSI 1000 ETFs and CSI 500 ETFs had relatively high average daily short - selling volumes [4][51][54] ETF Managers - As of last Friday, Huaxia Fund ranked first in the total scale of listed, non - monetary ETFs, with a relatively high management scale in multiple sub - fields such as scale - based index ETFs, topic - based, style, and strategy - based index ETFs, and cross - border ETFs. E Fund ranked second, with a relatively high management scale in scale - based index ETFs and cross - border ETFs. Huatai - Peregrine Fund ranked third, with a relatively high management scale in scale - based index ETFs and topic - based, style, and strategy - based index ETFs [55]