Group 1: U.S. Economic Impact - The ongoing U.S. government shutdown has created a data vacuum, making it difficult to confirm or refute market views on the U.S. economy[2] - Key economic indicators such as U.S. inventory and import data are only updated until July, while personal consumption data is available only until August[2] - The Congressional Budget Office (CBO) estimates that if the shutdown continues, it could impact Q4 GDP growth by up to 2 percentage points[14] Group 2: China-U.S. Trade Dynamics - In October, China's exports to the U.S. fell by 25.1% year-on-year, but the decline narrowed by 1.8 percentage points compared to previous months[4] - Exports of intermediate goods from China to the U.S. showed resilience, with a year-on-year decline of 18.5% in September, better than the 24.1% drop in consumer goods[4] - Taiwan's exports to the U.S. surged by 49.7% in October, driven by a 138.2% increase in information and communication products[5] Group 3: Global Export Trends - Vietnam's exports to the U.S. decreased from a year-on-year growth of 38.2% in September to 17.5% in October, with a 15.2% drop in mobile phone exports[6] - Mexico's automotive production and exports fell by 4% and 5.5% respectively in October due to U.S. tariffs[6] - South Korea's overall exports to the U.S. declined by 15.1% in October, excluding semiconductors[6] Group 4: Consumer Confidence and Employment - The University of Michigan's consumer confidence index dropped to 50.3 in November, the lowest since June 2022[15] - The current economic conditions index fell by 6.3 points to 52.3, while the consumer expectations index hit a six-month low at 49[15] - Job vacancies in the U.S. decreased by 2.2 percentage points from September, indicating pressure on consumer spending[15]

Investment Rating - The report suggests a positive outlook for the engineering machinery sector, indicating a potential profit release for domestic manufacturers [5][11]. Core Insights - The report highlights significant advancements in humanoid robotics by companies like Xiaopeng and Tesla, with a projected mass production target set for 2026, which is expected to catalyze market growth [5]. - The engineering machinery sector is experiencing a recovery, with excavator sales in October 2025 reaching 18,096 units, a year-on-year increase of 7.77% [5][33]. - The report emphasizes the robust growth in gas turbine orders, particularly for Mitsubishi Heavy Industries, which saw a significant increase in new orders, reflecting a high industry demand [5][33]. Summary by Sections Market Review - The SW Machinery Equipment Index fell by 0.15% in the week of November 3-7, 2025, ranking 22nd among 31 primary industry categories, while the CSI 300 Index rose by 0.82% [14][17]. Key Data Tracking General Machinery - The manufacturing PMI for October was 49.0%, indicating continued pressure in the general machinery sector [24]. - Forklift sales in September 2025 reached 130,380 units, a year-on-year increase of 23.0% [24]. Engineering Machinery - The engineering machinery sector is on an upward trend, with excavator sales in October 2025 showing a 7.8% increase year-on-year [33]. - Domestic sales of excavators reached 8,468 units, up 2.4%, while exports totaled 9,628 units, up 12.9% [33]. Railway Equipment - The railway equipment sector is experiencing steady growth, with fixed asset investment maintaining a growth rate of around 6% [46]. Gas Turbines - The gas turbine sector is robust, with GEV reporting a 39% year-on-year increase in new orders for the first three quarters of 2025 [56]. Industry Dynamics - The report notes that the engineering machinery market is expected to benefit from a recovery in North America and Europe, with companies like XCMG, SANY, and LiuGong highlighted as key players to watch [5][11].

1. Report Industry Investment Rating No relevant content provided. 2. Core View of the Report The "Guojin Securities Fixed Income - Bond Market Micro - trading Thermometer" rose 4 percentage points to 50%. The proportion of indicators in the over - heated range increased to 35%. The trading heat index mean rose significantly, while the institutional behavior, spread, and price - ratio index means changed with different trends [3][4][15]. 3. Summary According to Related Catalogs 3.1. This period, the micro - trading thermometer reading rebounded to 50% - The "Guojin Securities Fixed Income - Bond Market Micro - trading Thermometer" continued to rise 4 percentage points to 50%. The trading heat index mean rebounded 20 percentage points, with TL/T long - short ratio, market turnover rate, and institutional leverage percentile values rising 14, 47, and 53 percentage points respectively. Meanwhile, the percentile values of money tightness expectation, stock - bond ratio, and policy spread decreased 22, 17, and 13 percentage points respectively. High - congestion indicators include 30/10Y Treasury turnover rate, institutional leverage, fund duration, and fund ultra - long bond buying volume [3][15]. 3.2. The proportion of indicators in the over - heated range increased to 35% - Among 20 micro - indicators, the number of indicators in the over - heated range increased to 7 (35%), in the neutral range decreased to 6 (30%), and in the cold range decreased to 7 (35%). The market turnover rate moved from the cold to the neutral range, and institutional leverage moved from the neutral to the over - heated range [4][20]. - **Trading heat**: The 30/10Y Treasury turnover rate remained at 100%. The 1/10Y Treasury turnover rate, TL/T long - short ratio, market turnover rate, and institutional leverage percentile values rebounded 5, 14, 47, and 53 percentage points respectively, and the trading heat index mean rose 20 percentage points [5][20]. - **Institutional behavior**: The percentile values of fund duration, allocation strength, and fund - rural commercial bank buying volume rebounded slightly, while those of fund divergence and money tightness expectation decreased 4 and 22 percentage points respectively. The institutional behavior index mean decreased 1 percentage point [5][20]. - **Spread**: The market spread rebounded 4 percentage points, and the policy spread dropped 13 percentage points. The spread index mean decreased 4 percentage points [5][20]. - **Price - ratio**: The percentile values of stock - bond, commodity, and real - estate price - ratios decreased 17, 5, and 6 percentage points respectively, and the price - ratio index mean decreased 7 percentage points [5][20]. 3.2.1. The institutional leverage percentile value rebounded 53 percentage points - In trading heat indicators, the proportion of over - heated indicators rose to 67%, neutral to 38%, and cold decreased to 25%. The market turnover rate percentile value rose 47 percentage points to 55% and moved to the neutral range, and institutional leverage rose 53 percentage points to 96% and moved to the over - heated range [6][21]. 3.2.2. The money tightness expectation percentile value decreased 22 percentage points - In institutional behavior indicators, the proportion of over - heated indicators rose to 38%, neutral decreased to 38%, and cold decreased to 25%. The percentile values of fund divergence, money tightness expectation, and listed company wealth - management buying volume decreased 4, 22, and 4 percentage points respectively, while those of allocation strength and fund - rural commercial bank buying volume rebounded 10 and 8 percentage points respectively [7][25]. 3.2.3. The policy spread percentile value dropped 13 percentage points - The 3 - year Treasury yield rose slightly, the policy spread widened from 1bp to 4bp, and the percentile value dropped 13 percentage points to 41%, remaining in the neutral range. The credit spread narrowed 4bp to 51bp, the agricultural development - state - owned development spread was flat at 0bp, and the IRS - SHIBOR 3M spread widened 2bp. The average spread narrowed slightly from 18bp to 17bp, and its percentile value rose 4 percentage points to 60%, still in the neutral range [8][31]. 3.2.4. All price - ratio percentile values declined - The proportion of price - ratio indicators in the cold range remained 100%. The stock - bond, commodity, and real - estate price - ratio percentile values declined 17, 5, and 6 percentage points respectively [9][35].

Group 1 - Recent underperformance of large overseas tech stocks indicates market concerns over the financial cycle and high expectations within AI tech giants, shifting focus towards the revenue generation capabilities of their AI businesses [3][10] - The market is increasingly recognizing the value of China's substantial capacity built for energy transition, particularly in power and manufacturing sectors, leading to a repricing of Chinese assets [4][5] - The ongoing revaluation of the power equipment sector is driven by previous underestimation due to overcapacity, coinciding with a recovery in valuation and performance due to overseas power shortages [18][19] Group 2 - The chemical sector is identified as a key area for potential opportunities, with many companies positioned to benefit from the energy transition and having established significant capacities [27][28] - Specific segments within the power equipment sector, such as electrical instruments and lithium batteries, are highlighted for their high profitability and low trading congestion, suggesting potential for upward price movement [18][19] - The report suggests monitoring industries with high energy consumption, such as non-ferrous metals and textiles, as they may gain competitive advantages due to China's relatively abundant power resources [35][36] Group 3 - The focus has shifted from AI-driven growth in the U.S. to China's foundational strengths in power and manufacturing, creating a basis for the revaluation of previously perceived excess capacities [38][39] - The report emphasizes the importance of real assets and China's manufacturing advantages in the context of global economic recovery and investment expansion [5][39] - Recommendations include focusing on upstream resources and capital goods that benefit from domestic economic recovery and international demand [5][39]

1. Report Industry Investment Rating - Not provided in the given content 2. Core View of the Report - Not explicitly stated in the given content 3. Summary by Relevant Catalogs 3.1 Secondary Market Price and Volume Performance - Data on multiple REITs including fund code, fund name, industry type, listing date, issue price, first - day return, return since listing, trading volume since listing, trading volume this week, trading volume last week, turnover rate this week, turnover rate last week, return this week, return last week, and return since the beginning of this year are presented for different industries such as warehousing logistics, industrial parks, affordable rental housing, consumer infrastructure, data centers, highways, ecological environment protection, water facilities, and energy [10][11] 3.2 Secondary Market Valuation Situation - Valuation data of various REITs are provided, including P/FFO, P/NAV, IRR, PV multiplier, and expected cash distribution rate in 2025, along with comparisons to industry averages and current quantiles [22] 3.3 Market Correlation Statistics - The correlation coefficients between REITs (including different types such as property - based, franchise - based, and by industry) and various asset classes (stock assets, convertible bonds, pure bonds, and commodities) are presented [28] 3.4 Primary Market Tracking - Information on several REITs in the primary market is given, including project nature, project type, stage, acceptance date, original equity holder, underlying projects, and project valuation [30]

Report Industry Investment Rating No relevant content provided. Core Viewpoints of the Report In October, the approval of urban investment bonds showed characteristics of a slight decline in registration quota, a marginal acceleration in the approval rhythm, and a slight decline in the scale of terminated projects, with the overall financing rhythm slowing down. The supply - structure differentiation and the gap between strong and weak urban investment bonds further widened. The approval rhythm showed marginal relaxation, but the approval for urban investment platforms with different qualifications remained significantly differentiated. The establishment of the Debt Management Department by the Ministry of Finance in 2025 indicates stricter debt supervision, and it is recommended to continue to monitor the implementation of policy tools and relevant regulatory dynamics of the Debt Management Department [6][53]. Summary by Catalog 1. Registration Status: Slight Decline in Urban Investment Registration Quota - Overall registration: In October, the registration quota of urban investment platforms decreased slightly. The registration scale of the exchange decreased significantly, from 2338 billion yuan to 1868 billion yuan, while that of DCM increased slightly, from 1886 billion yuan to 2065 billion yuan [12]. - By administrative level: The registration scales of provincial and municipal urban investment platforms decreased. The proposed issuance scale of provincial urban investment registration projects dropped from 945 billion yuan to 631 billion yuan, and that of prefecture - level cities dropped from 1588 billion yuan to 1302 billion yuan. The proposed issuance scale of district - level urban investment registration projects increased from 1691 billion yuan to 2000 billion yuan, and its three - month moving average proportion rose to 42% [15]. - By district and county qualifications: The registration scale of districts and counties with weak qualifications decreased significantly. The registration scale of district - level platform bonds with a budget revenue of less than 5 billion yuan dropped from 675 billion yuan to 408 billion yuan, and the three - month moving average proportion decreased to 39.2% [18]. - By province: The scales in regions such as Zhejiang, Shandong, and Sichuan decreased significantly month - on - month. The scales in Sichuan, Anhui, and Shaanxi continued to decline. The scale growth in Jiangsu was mainly at the district - level, while that in Tianjin increased significantly month - on - month [20]. 2. Approval Feedback: Marginal Acceleration in Urban Investment Bond Approval - By issuance venue: In October, the DCM and exchange approval rhythms for urban investment bonds both accelerated. The number of effective sample bonds registered in DCM was 300, a significant decrease from the previous month, and that in the exchange was 81, also a significant decrease. The average number of feedbacks in DCM increased from 2.4 to 2.5 times, and the feedback time decreased from 41.5 days to 40.6 days. The average number of feedbacks in the exchange decreased from 4.1 to 3.7 times, and the feedback time decreased from 80.6 days to 70 days [28]. - By issuance method and level: The feedback times of public and private urban investment corporate bonds in prefecture - level cities and district - level cities decreased to varying degrees [33]. - By province: The approval rhythms in Jiangxi, Shaanxi, Fujian and other places accelerated significantly. The approval speeds in Zhejiang, Anhui, and Shanxi continued to improve, while the approval feedback days in Sichuan, Hubei, Guangdong and other places were significantly extended [37]. - By district and county qualifications: The approval rhythm of platform bonds in districts and counties with weak qualifications slowed down. The feedback days of district - level platforms with a general budget revenue of less than 5 billion yuan increased from 70.3 days to 72.6 days, lower than the average of last year. The approval rhythms of district - level platforms with a general budget revenue of 5 - 8 billion yuan and 10 - 30 billion yuan accelerated significantly [39]. 3. Terminated Issuance: Slight Decline in the Scale of Terminated Projects - Overall situation: In October, the scale of terminated projects decreased slightly. The proposed issuance scale of terminated urban investment bonds decreased from 13 billion yuan to 12.5 billion yuan, and the number of terminated projects decreased from 9 to 6. The terminated scale of district - level urban investment bonds decreased significantly, and its three - month moving average proportion decreased to 46%. The scale of terminated projects at the municipal level increased significantly, and there were no terminated projects at the provincial level. The three - month moving average proportion of the number of terminated projects in districts and counties with weak qualifications (local budget revenue of less than 5 billion yuan) continued to decline to 30.6% [42]. - By province: Terminated projects of urban investment platforms mainly occurred in Shandong, Henan, and Hebei. The scale of terminated projects in Shandong was mainly at the district - level platform, while those in Henan and Hebei were mainly affected by prefecture - level platforms [50].

Investment Rating - The report maintains a "Buy" rating for the company, expecting a price increase of over 15% in the next 6-12 months [5]. Core Insights - The company reported total revenue of $1.4 billion in Q3 2025, a year-on-year increase of 41%, and achieved a GAAP net profit of $125 million, marking a return to profitability [2]. - The core product, Zebutinib, continues to show rapid growth, with Q3 sales reaching $1.04 billion, up 51% year-on-year and 10% quarter-on-quarter, solidifying its position as a global leader in the BTKi market [3]. - The company has raised its full-year guidance for 2025, projecting total revenue between $5.1 billion and $5.3 billion, with GAAP operating expenses of $4.1 billion to $4.3 billion, and a gross margin in the mid-to-high 80% range [3]. - The research pipeline is advancing efficiently, with several key milestones expected in the near future, including clinical trials for various treatments [4]. - The company has revised its net profit forecasts for 2025-2027 to $312 million, $795 million, and $1.22 billion, respectively, reflecting a positive trend in profitability [5]. Summary by Sections Performance Review - In Q3 2025, the company achieved total revenue of $1.4 billion, a 41% increase year-on-year, and a GAAP net profit of $125 million, indicating a return to profitability [2]. Operational Analysis - Zebutinib sales reached $1.04 billion in Q3 2025, a 51% increase year-on-year, with the U.S. market contributing $739 million (up 47% year-on-year) and Europe showing a 68% increase to $163 million [3]. - The company has updated its full-year guidance for 2025, projecting revenue of $5.1 billion to $5.3 billion and positive GAAP net profit for the year [3]. Research and Development - The company is set to initiate several key clinical trials in 2025 and 2026, including studies for various cancer treatments, indicating a robust R&D pipeline [4]. Profitability Forecast - The company has increased its net profit forecasts for 2025-2027, now expecting $312 million, $795 million, and $1.22 billion, respectively, reflecting a significant improvement in profitability [5].

- The artificial intelligence global asset allocation model applies machine learning to asset allocation problems, using factor investment ideas to score and rank assets, ultimately constructing a monthly quantitative equal-weighted strategy for global asset allocation[38][39][40] - The dynamic macroeconomic event factor-based stock-bond rotation strategy includes three risk preference models (conservative, balanced, and aggressive), utilizing macro timing modules and risk budgeting frameworks to determine stock and bond weights[43][44][45] - The dividend style timing model uses 10 indicators from economic growth and monetary liquidity dimensions, constructing a timing strategy for the dividend index, which shows significant stability improvement compared to the CSI Dividend Total Return Index[51][54][55] Model Backtesting Results - Artificial intelligence global asset allocation model: annualized return 38.76%, Sharpe ratio 1.07, maximum drawdown -6.56%, year-to-date return 6.81%[39][40][42] - Dynamic macroeconomic event factor-based stock-bond rotation strategy: aggressive model annualized return 20.14%, Sharpe ratio 1.30, maximum drawdown -13.72%, year-to-date return 14.42%; balanced model annualized return 10.92%, Sharpe ratio 1.19, maximum drawdown -6.77%, year-to-date return 4.13%; conservative model annualized return 5.94%, Sharpe ratio 1.50, maximum drawdown -3.55%, year-to-date return 0.97%[43][49][50] - Dividend style timing model: annualized return 16.52%, Sharpe ratio 1.07, maximum drawdown -13.77%, year-to-date return 0%[51][54][55]

Quantitative Models and Construction 国证 2000 Index Enhancement Strategy - **Model Name**: 国证 2000 Index Enhancement Strategy - **Model Construction Idea**: Focused on the small-cap stock rotation phenomenon in A-shares, aiming to select stocks effectively within 国证 2000 index components to enhance returns [11] - **Model Construction Process**: - Selected factors such as technical, reversal, and idiosyncratic volatility, which showed strong performance on 国证 2000 index components [12] - Addressed high correlation among factors by regressing volatility factors on technical and reversal factors to obtain residual volatility factors [12] - Combined all major factors equally and performed industry and market capitalization neutralization to construct the 国证 2000 enhancement factor [12] - Formula: Residual volatility factor = Volatility factor - Regression(Technical factor, Reversal factor) [12] - **Model Evaluation**: Demonstrated strong predictive performance with an IC mean of 12.63% and T-statistic of 12.70 [12] - **Strategy Construction**: - Monthly rebalancing at the end of each month, buying the top 10% ranked stocks based on factor values, constructing an equal-weighted long portfolio [15] - Backtesting period: April 2014 to present, benchmarked against 国证 2000 index, with a transaction fee rate of 0.2% per side [15] Machine Learning Index Enhancement Strategy - **Model Name**: TSGRU+LGBM Machine Learning Index Enhancement Strategy - **Model Construction Idea**: Improved machine learning stock selection model by integrating TimeMixer framework with GRU and LightGBM, leveraging multi-scale mixing and seasonal/trend decomposition mechanisms [21] - **Model Construction Process**: - Original strategy used GBDT and NN models trained on different feature datasets and prediction labels, but showed signs of failure due to market style adjustments [21] - Enhanced model incorporated TimeMixer framework into GRU, combined LightGBM with TSGRU latent vectors and traditional quantitative factors [21] - Optimized portfolio construction by controlling tracking error and individual stock weight deviation to maximize factor exposure [25] - **Model Evaluation**: Improved ability to capture recent market information, showing strong performance [21] Dividend Style Timing + Dividend Stock Selection Strategy - **Model Name**: Dividend Style Timing + Dividend Stock Selection Strategy - **Model Construction Idea**: Leveraged the long-term stability and high dividend characteristics of dividend stocks to reduce risk during weak market conditions [36] - **Model Construction Process**: - Used 10 indicators related to economic growth and monetary liquidity to construct a dynamic event factor system for dividend index timing [36] - Applied AI models to test stock selection within 中证红利 index components, achieving stable excess returns [36] - **Model Evaluation**: Demonstrated significant stability improvement compared to 中证红利 index total return [36] --- Model Backtesting Results 国证 2000 Index Enhancement Strategy - **IC Mean**: 12.63% [12] - **Latest Month IC**: 25.34% [12] - **Annualized Excess Return**: 13.30% [16] - **Information Ratio (IR)**: 1.73 [16] - **Tracking Error**: 7.68% [19] - **October Excess Return**: 2.92% [16] TSGRU+LGBM Machine Learning Index Enhancement Strategy - **沪深 300 Index**: - **Annualized Excess Return**: 6.96% [26] - **Information Ratio (IR)**: 1.40 [26] - **Tracking Error**: 4.97% [26] - **October Excess Return**: 2.25% [26] - **中证 500 Index**: - **Annualized Excess Return**: 10.11% [30] - **Information Ratio (IR)**: 1.96 [30] - **Tracking Error**: 5.16% [30] - **October Excess Return**: -0.59% [30] - **中证 1000 Index**: - **Annualized Excess Return**: 13.52% [35] - **Information Ratio (IR)**: 2.37 [35] - **Tracking Error**: 5.70% [35] - **October Excess Return**: 2.63% [35] Dividend Style Timing + Dividend Stock Selection Strategy - **Stock Selection Strategy**: - **Annualized Return**: 18.98% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 2.52% [38] - **Timing Strategy**: - **Annualized Return**: 13.83% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 3.28% [38] - **固收+ Strategy**: - **Annualized Return**: 7.39% [38] - **Sharpe Ratio**: 2.19 [38] - **October Return**: 0.92% [38]

Group 1: Report General Information - Report title: Fund Analysis Special Report (In - Depth) [1] - Report date: November 6, 2025 [1] Group 2: Hong Kong Stock Fund Performance and Scale Development Performance - **Return**: Among different types of Hong Kong stock funds, in the recent quarter, the return of Hong Kong - Stock Connect - Active funds was 20.11%, and that of Hong Kong - Stock QDII - Active funds was 22.43%. In the recent year, the return of Hong Kong - Stock QDII - Active funds reached 55.02%. In the recent 3 - year and 5 - year periods, different types of funds also showed various returns [13]. - **Maximum drawdown**: The maximum drawdown of Hong Kong - Stock Connect - Active funds in the recent quarter was - 4.57%, and that of Hong Kong - Stock QDII - ETF&Passive Index funds in the recent 5 - year period was - 54.98% [13]. - **Annualized Sharpe ratio**: The annualized Sharpe ratio of Hong Kong - Stock Connect - Active funds in the recent quarter was 3.87, and that of Hong Kong - Stock QDII - ETF&Passive Index funds in the recent 5 - year period was 0.11 [13]. Scale and Share - The report presents the scale development and share changes of different types of Hong Kong stock funds through relevant charts [17] New Fund Issuance - The new issuance situation of Hong Kong stock funds in each quarter is shown in the chart [21] Group 3: Hong Kong Stock Fund Positioning Characteristics Stock and Hong Kong Stock Positions - The distribution of stock positions and Hong Kong stock positions of Hong Kong stock funds in different periods is presented. For example, from 2024/12/31 to 2025/9/30, the proportion of different industries in the stock positions showed certain changes [29] Sector and Stock Allocation - **Sector allocation**: In 2025Q3, the top sectors in the heavy - position stocks of Hong Kong stock funds included Media (22.31%), Commerce and Retail (16.99%), and Pharmaceutical Biology (15.52%) [33]. - **Stock allocation**: The top 10 stocks in terms of market - value ratio in 2025Q3 included Alibaba - W (13.87%) and Tencent Holdings (13.00%). The report also shows the top 10 stocks for increased and decreased positions [35]. - **Number of heavy - position funds**: Tencent Holdings had the largest number of holding funds in 2025Q3 (192), and the report also shows the top 10 stocks for increased and decreased positions in terms of the number of holding funds [37]. - **Market - value distribution and concentration**: The market - value distribution and concentration of heavy - position stocks of Hong Kong stock funds are presented [42] Group 4: Hong Kong Stock Fund Company Analysis Fund Company Scale - The top 20 fund companies in terms of Hong Kong stock fund scale in 2025Q3 are listed. For example, E Fund had a scale of 155.06 billion yuan in 2025Q3, with a scale change of 79.91% compared to 2025Q2 [44]. Heavy - Position Industries and Stocks - **Heavy - position industries**: Different fund companies have different first, second, and third heavy - position industries. For example, E Fund's first heavy - position industry in 2025Q3 was Non - Banking Finance (29.53%), with a 14.61% change compared to the previous period [47]. - **Heavy - position stocks**: Each fund company has its own top heavy - position stocks. For example, E Fund's first heavy - position stock was Tencent Holdings (16.52%) [48]. Group 5: High - Performance Hong Kong Stock Fund Positioning Display and Quarterly Report Views Positioning Display - The report shows the heavy - position stocks of some high - performance actively managed Hong Kong stock funds in 25Q3, including their fund codes, names, types, 25Q3 returns, fund managers, total scales, and the proportion of the market value of holding stocks to the fund net value [51][52] Quarterly Report Views - Different high - performance funds have different investment strategies and views. For example, HuaAn Hong Kong - Shanghai - Shenzhen Connect Select A believes that the semiconductor, communication, and new - energy industries have contributed excess returns, and it has increased positions in Hong Kong stock Internet and A - share self - controllable industrial chains [53].