Investment Rating - The report maintains an "Overweight" rating for the real estate development and real estate services sectors [5]. Core Views - The "Good House" concept is expected to reshape the supply structure in the real estate market, emphasizing product quality as a core competitive advantage for real estate companies [1][4]. - The demand for housing is shifting from "having" to "quality," indicating a transition to a quality-driven market [2][14]. - The "Good House" initiative is a systematic project aimed at improving housing quality, with significant implications for both supply and demand sides [9][10]. Summary by Sections Investment Rating - The report recommends a continued focus on "three good" real estate stocks characterized by good credit, good cities, and good products, specifically highlighting companies like China Overseas Development and China Resources Land [1][7]. Housing Quality and Market Dynamics - The "Good House" concept was introduced in 2022 and officially implemented in 2025, marking a shift towards a quality-oriented housing market [2][9]. - There is a notable improvement in buyer preferences, with larger units (over 120 square meters) seeing increased sales, indicating a trend towards higher-quality living spaces [20][21]. Government Policies and Standards - The government has outlined four key characteristics of a "Good House": safety, comfort, green standards, and smart technology, which are now integral to new housing project regulations [3][30]. - The report details the evolution of housing standards in China, culminating in the 2025 release of the "Residential Project Standards," which aims to enhance overall housing quality [40]. Market Performance and Company Recommendations - Companies that have adopted the "Good House" framework have shown better sales performance, with many achieving sales rates above 70% for new projects [25][21]. - The report emphasizes the importance of product iteration in the real estate sector, drawing parallels with the smartphone and automotive industries, where product quality drives market dynamics [4][36]. Regional Policy Implementation - Various cities have begun implementing local policies aligned with the "Good House" standards, focusing on aspects like building quality, community amenities, and environmental considerations [12][13].

Investment Rating - The report maintains an "Overweight" rating for the real estate development and real estate services sectors [6]. Core Insights - The industry is still in a bottoming and stabilizing phase, with a more optimistic view on real estate companies in core cities with abundant resources. The report highlights that while the marginal improvement in construction and sales is noted, housing prices still face downward pressure [1][2]. - The report recommends real estate stocks that align with the "good credit, good city, good product" logic, as well as leading property management companies with stable dividends and performance [1]. Summary by Sections Real Estate Development - In September, real estate development investment saw a year-on-year decline of 21%, which is a 1.8 percentage point increase in the decline compared to August. Cumulatively from January to September, the year-on-year decline is 14% [2]. - The land market showed marginal improvement in September, with transaction area and transaction amount down by 1% and 7% year-on-year, respectively, compared to declines of 25% and 31% in August [2]. - New construction in September decreased by 14% year-on-year, but the decline narrowed by 6 percentage points compared to August [2]. Sales Performance - The sales amount in September saw a year-on-year decline of 12%, which is a 2 percentage point narrowing from August. Cumulatively, from January to September, the year-on-year decline is 8% [3]. - The new housing price index in 70 cities decreased by 2.7% year-on-year in September, with a 0.3 percentage point narrowing from August [3]. Cash Flow Situation - In September, the cash inflow for real estate companies decreased by 12% year-on-year, although the decline narrowed by 0.4 percentage points compared to August. Specifically, deposits and prepayments fell by 9% year-on-year, while personal mortgage loans decreased by 11% [4]. - The overall cash flow situation for real estate companies remains to be improved, as domestic loans saw a significant decline of 15% year-on-year in September [4].

Investment Rating - The report maintains an "Overweight" rating for the construction and building materials industry [6]. Core Views - The funding environment is gradually improving, with expectations for increased fiscal support in the fourth quarter, particularly benefiting the real estate sector [1]. - The report highlights a potential turning point for consumer building materials revenue due to improving demand and a decrease in price pressures in 2025 [2]. - The cement industry is experiencing a push for price increases, but demand support remains weak, leading to price fluctuations [3]. - The flat glass market shows signs of price stabilization, but supply-side improvements are still needed [4]. Summary by Sections Investment Environment - Infrastructure, real estate, and manufacturing investments in China showed mixed results, with infrastructure investment up by 1.1% year-on-year, real estate down by 13.9%, and manufacturing up by 4.0% [1]. - The central government has allocated an additional 500 billion yuan to local governments, indicating a proactive fiscal approach [1]. Real Estate Market - From January to September 2025, real estate sales, new starts, and completion areas decreased by 5.5%, 18.9%, and 15.3% year-on-year, respectively [2]. - September saw a positive turn in monthly housing completion area, suggesting a potential recovery in the sector [2]. Cement Industry - Cement production from January to September 2025 was 1.259 billion tons, down 5.2% year-on-year, with a notable price increase in September [3]. - The average cement price in September was 351 yuan per ton, reflecting a 1.4% month-on-month increase [3]. Glass Industry - The flat glass production for the first nine months of 2025 was 729 million weight cases, down 5.2% year-on-year, with prices stabilizing in September [4]. - The photovoltaic glass market showed better performance with a price increase of 19% month-on-month [4]. Recommended Stocks - The report recommends several stocks with a "Buy" rating, including China Liansu (2128 HK), Sichuan Road and Bridge (600039 CH), Yaxiang Integration (603929 CH), Sankeshu (603737 CH), Tubaobao (002043 CH), and Dongfang Yuhong (002271 CH) [7][29].

Investment Rating - The report maintains an "Overweight" rating for the industry and "Buy" for the specific company, Diwei [7][9]. Core Insights - The multi-directional forging market is expected to grow from $1.205 billion in 2024 to $1.576 billion by 2031, with a CAGR of 3.9% [2][41]. - Domestic companies, particularly Diwei, have surpassed international competitors in technology, with their 350MN multi-directional forging hydraulic press achieving superior performance metrics [2][5]. - The global forging market is projected to grow from $95.02 billion in 2024 to $199.69 billion by 2034, with a CAGR of 7.7%, and the Chinese market is expected to grow at a CAGR of 11.3% from 2025 to 2035 [3][34]. Summary by Sections Market Expansion - The demand from high-end manufacturing sectors such as aerospace, deep-sea equipment, and new energy is driving the global multi-directional forging market expansion [2][3]. - Multi-directional forging is widely used in high-performance applications, including aerospace landing gear and complex structural components, enhancing material utilization and product reliability [3][12]. Technical Barriers - Multi-directional forging faces significant barriers in equipment, processes, and materials, creating a strong competitive moat [4][12]. - The technology requires high-precision multi-cylinder synchronous control and complex mold design, which are challenging to achieve [4][12]. Domestic Technological Breakthroughs - The domestic multi-directional forging industry has made significant technological advancements since around 2010, with companies like Diwei leading the way [5][13]. - Diwei's 350MN hydraulic press has entered the global supply chain for top oil service companies, showcasing China's capabilities in high-end forging equipment [5][13]. Market Growth and Demand - The global forging market is expected to grow steadily, with multi-directional forging benefiting from the increasing demand for high-performance components [33][34]. - The multi-directional forging hydraulic press market is projected to grow from $1.205 billion in 2024 to $1.576 billion by 2031, driven by aerospace and automotive lightweighting needs [41][43]. Application and Performance - Multi-directional forging is recognized for its ability to produce complex parts in a single forming process, significantly improving material utilization rates to 70%-85% [11][31]. - The technology is particularly advantageous in high-stress applications, such as aerospace and nuclear power, where performance and reliability are critical [42][44].

Investment Rating - The investment rating for the company is maintained at "Hold" [6] Core Views - The company is a rare iron ore producer in the A-share market, focusing on increasing production and quality to maintain profitability stability while actively seeking opportunities in non-ferrous metals [1][3] - In Q1-Q3 2025, the company achieved revenue of 1.247 billion yuan, a year-on-year increase of 12.98%, and a net profit attributable to the parent company of 220 million yuan, a year-on-year increase of 47.09% [1] - The company emphasizes investor returns, with a cumulative dividend rate of approximately 27% as of Q3 2025 [2] Financial Performance - In Q3 2025, the company reported revenue of 479 million yuan, a year-on-year increase of 17.78% and a quarter-on-quarter increase of 16.18%, while the net profit attributable to the parent company was 70 million yuan, showing a year-on-year increase of 0.25% but a quarter-on-quarter decrease of 30.66% [1][2] - The sales gross margin decreased from 30.8% in Q2 2025 to 22.7% in Q3 2025, indicating a potential rebound in iron ore costs [2] Market Outlook - Short-term iron ore prices are expected to stabilize, while medium-term supply pressures are anticipated due to a shift in the iron ore supply-demand balance towards loosening in 2024 [3] - The average price of iron concentrate in Q3 2025 was reported at 941 yuan, remaining stable quarter-on-quarter [2] Valuation - The target price for the company is set at 10.18 yuan, reflecting an increase from the previous target of 9.42 yuan, based on updated iron concentrate price assumptions [4] - The estimated net profits for 2025-2027 are projected to be 274 million yuan, 281 million yuan, and 281 million yuan, respectively, with a significant upward revision of 24%-25% compared to previous estimates [4]

证券研究报告 中国人寿 (601628 CH/2628 HK) 业绩预增：前三季度归母净利润同比 增长 50%~70% 我们预计，中国人寿三季度 NBV 有望保持增长。一方面，在预定利率下调 和报行合一的推动下，NBV 利润率或同比有所改善。继 2024 年 9 月 1 日/10 月 1 日传统险/分红险预定利率下调为 2.5%/2.0%后，今年 9 月 1 日传统险/ 分红险预定利率下调为 2.0%/1.75%，预定利率连续下调有助于降低负债成 本，抬高 NBV 利润率。另一方面，2024 年 4 月监管取消了一家银行网点只 能销售三家保险公司产品的"一对三"限制，大公司得以迅速扩张银行网点。 今年上半年，国寿包含银保在内的其他渠道的 NBV 同比增速高达 179%左 右（可比口径），我们预计银保高增长趋势在三季度有望延续。全渠道看， 公司三季度 NBV 有望稳健增长。 盈利预测与估值 华泰研究 公告点评 2025 年 10 月 20 日│中国内地/中国香港 保险 公司发布 3Q25 业绩预增公告：预计公司 2025 年前三季度归母净利润同比 增长 50%~70%。我们估算，三季度单季度归母净利润或同比增长 ...

证券研究报告 策略视角 近期美国信贷风险的影响和应对 华泰研究 2025 年 10 月 20 日│中国内地 | 易峘 | 李雨婕 | 何康，PhD | | --- | --- | --- | | 研究员 | 研究员 | 研究员 | | SAC No. S0570520100005 | SAC No. S0570525050001 | SAC No. S0570520080004 | | SFC No. AMH263 | SFC No. BRG962 | SFC No. BRB318 | | evayi@htsc.com | liyujie@htsc.com | hekang@htsc.com | | +(852) 3658 6000 | +(852) 3658 6000 | +(86) 21 2897 2202 | 短期波动加剧，系统性风险可控，中期流动性宽松渐明 自 9 月以来，美国私人信贷和区域银行风险持续发酵并集中暴露，包括 Tricolor Holdings 和 First Brands Group 的破 产，以及 Zions Bancorp 和 Western Alliance Bancorp 因欺 ...

- **A-share multi-dimensional timing model**: The model evaluates the overall directional judgment of the A-share market using four dimensions: valuation, sentiment, funds, and technical indicators. Each dimension provides daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. Valuation and sentiment dimensions adopt a mean-reversion logic, while funds and technical dimensions use trend-following logic. The final market view is determined by the sum of the scores across all dimensions [9][15][16] - **Style timing model for dividend style**: The model uses three indicators to time the dividend style relative to the CSI Dividend Index and CSI All Share Index. The indicators include relative momentum, 10Y-1Y term spread, and interbank pledged repo transaction volume. Each indicator provides daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. The final view is based on the sum of the scores across all dimensions. When the model favors the dividend style, it fully allocates to the CSI Dividend Index; otherwise, it allocates to the CSI All Share Index [17][21] - **Style timing model for large-cap and small-cap styles**: The model uses momentum difference and turnover ratio difference between the CSI 300 Index and Wind Micro Cap Index to calculate the crowding scores for large-cap and small-cap styles. The model operates in two crowding zones: high crowding and low crowding. In high crowding zones, it uses a small-parameter dual moving average model to address potential style reversals. In low crowding zones, it uses a large-parameter dual moving average model to capture medium- to long-term trends [22][24][26] - **Sector rotation model**: The genetic programming-based sector rotation model selects the top five sectors with the highest multi-factor composite scores from 32 CITIC industry indices for equal-weight allocation. The model updates its factor library quarterly and rebalances weekly. The factors are derived using NSGA-II algorithm, which evaluates factor monotonicity and performance of long positions using |IC| and NDCG@5 metrics. The model combines multiple factors with weak collinearity into sector scores using greedy strategy and variance inflation factor [29][32][33][36] - **China domestic all-weather enhanced portfolio**: The portfolio is constructed using a macro factor risk parity framework, which emphasizes risk diversification across underlying macro risk sources rather than asset classes. The strategy involves three steps: macro quadrant classification and asset selection, quadrant portfolio construction and risk measurement, and risk budgeting to determine quadrant weights. The active allocation is based on macro expectation momentum indicators, which consider buy-side expectation momentum and sell-side expectation deviation momentum [38][41] --- Model Backtesting Results - **A-share multi-dimensional timing model**: Annualized return 24.97%, maximum drawdown -28.46%, Sharpe ratio 1.16, Calmar ratio 0.88, YTD return 37.73%, weekly return 0.00% [14] - **Dividend style timing model**: Annualized return 15.71%, maximum drawdown -25.52%, Sharpe ratio 0.85, Calmar ratio 0.62, YTD return 19.53%, weekly return -3.43% [20] - **Large-cap vs. small-cap style timing model**: Annualized return 26.01%, maximum drawdown -30.86%, Sharpe ratio 1.08, Calmar ratio 0.84, YTD return 64.58%, weekly return -2.22% [27] - **Sector rotation model**: Annualized return 33.33%, annualized volatility 17.89%, Sharpe ratio 1.86, maximum drawdown -19.63%, Calmar ratio 1.70, weekly return 0.14%, YTD return 39.41% [32] - **China domestic all-weather enhanced portfolio**: Annualized return 11.66%, annualized volatility 6.18%, Sharpe ratio 1.89, maximum drawdown -6.30%, Calmar ratio 1.85, weekly return 0.38%, YTD return 10.74% [42]

Quantitative Models and Construction Methods - **Model Name**: AI Thematic Index Rotation Model **Model Construction Idea**: The model utilizes a full-spectrum price-volume fusion factor to score 133 thematic indices and constructs a weekly rebalancing strategy by equally allocating the top 10 thematic indices based on their scores [3][9][6] **Model Construction Process**: 1. **Thematic Index Pool**: Select thematic indices tracked by ETF funds classified by Wind, resulting in a pool of 133 thematic indices [9] 2. **Factor**: Full-spectrum price-volume fusion factor, which scores each thematic index based on the factor scores of its constituent stocks [9] 3. **Strategy Rules**: - On the last trading day of each week, select the top 10 thematic indices with the highest model scores - Allocate equally among the selected indices - Buy at the opening price of the first trading day of the following week - Weekly rebalancing with a transaction cost of 0.04% on both sides [9] **Model Evaluation**: The model demonstrates effective thematic index rotation and generates significant excess returns compared to the equal-weight benchmark [3][9] - **Model Name**: AI Concept Index Rotation Model **Model Construction Idea**: The model uses a full-spectrum price-volume fusion factor to score 72 concept indices and constructs a weekly rebalancing strategy by equally allocating the top 10 concept indices based on their scores [15][11][19] **Model Construction Process**: 1. **Concept Index Pool**: Select 72 popular concept indices from Wind [15] 2. **Factor**: Full-spectrum price-volume fusion factor, which scores each concept index based on the factor scores of its constituent stocks [15] 3. **Strategy Rules**: - On the last trading day of each week, select the top 10 concept indices with the highest model scores - Allocate equally among the selected indices - Buy at the opening price of the first trading day of the following week - Weekly rebalancing with a transaction cost of 0.04% on both sides [15] **Model Evaluation**: The model effectively identifies high-performing concept indices and generates consistent excess returns compared to the equal-weight benchmark [15][19] - **Model Name**: AI Industry Rotation Model **Model Construction Idea**: The model uses deep learning to extract information from full-spectrum price-volume data, scoring 32 primary industries and constructing a weekly rebalancing strategy by equally allocating the top 5 industries based on their scores [16][19][23] **Model Construction Process**: 1. **Industry Pool**: Includes 32 primary industries, with certain industries split into subcategories (e.g., food and beverage into food, beverages, and alcohol) [23] 2. **Factor**: Full-spectrum price-volume fusion factor, which scores each industry based on the factor scores of its constituent stocks [23] 3. **Strategy Rules**: - On the last trading day of each week, select the top 5 industries with the highest model scores - Allocate equally among the selected industries - Buy at the closing price of the first trading day of the following week - Weekly rebalancing without considering transaction costs [23] **Model Evaluation**: The model complements top-down strategies by leveraging AI's ability to extract patterns from multi-frequency price-volume data, achieving strong excess returns [16][23] - **Model Name**: AI CSI 1000 Enhanced Portfolio **Model Construction Idea**: The portfolio is constructed using the full-spectrum fusion factor to enhance the CSI 1000 index, aiming to achieve higher excess returns [27][29] **Model Construction Process**: 1. **Factor**: Full-spectrum fusion factor [29] 2. **Portfolio Construction Rules**: - Constituent stock weight must not be less than 80% - Individual stock weight deviation capped at 0.8% - Barra exposure limited to 0.3% - Weekly turnover rate controlled at 30% - Weekly rebalancing with a transaction cost of 0.4% on both sides [29] **Model Evaluation**: The portfolio demonstrates strong excess returns, high information ratio, and controlled tracking error [27][29] - **Model Name**: Text FADT_BERT Stock Selection Portfolio **Model Construction Idea**: The portfolio is based on the forecast_adjust_txt_bert factor, which is derived from upgraded text factors in earnings forecast adjustment scenarios, and selects the top 25 stocks for active quantitative enhancement [32] **Model Construction Process**: 1. **Factor**: Forecast_adjust_txt_bert factor, developed using text data related to earnings forecast adjustments [32] 2. **Portfolio Construction Rules**: - Select the top 25 stocks from the long side of the base stock pool - Active quantitative enhancement applied to the selected stocks [32] **Model Evaluation**: The portfolio achieves high annualized returns and excess returns relative to the CSI 500 index, with a strong Sharpe ratio [32] --- Model Backtesting Results - **AI Thematic Index Rotation Model** - Annualized return: 16.76% - Annualized excess return: 10.61% - Maximum drawdown of excess return: 20.79% - Excess Sharpe ratio: 0.82 - Year-to-date return: 24.22% [8] - **AI Concept Index Rotation Model** - Annualized return: 23.06% - Annualized excess return: 10.78% - Maximum drawdown of excess return: 19.48% - Excess Sharpe ratio: 0.91 - Year-to-date return: 25.27% - Year-to-date excess return: -0.98% [13] - **AI Industry Rotation Model** - Annualized return: 26.55% - Annualized excess return: 20.18% - Maximum drawdown of excess return: 12.43% - Excess Sharpe ratio: 1.96 - Year-to-date return: 23.70% - Year-to-date excess return: 1.52% [22] - **AI CSI 1000 Enhanced Portfolio** - Annualized return: 20.19% - Annualized excess return: 22.09% - Annualized tracking error: 6.07% - Maximum drawdown of excess return: 7.55% - Information ratio: 3.64 - Calmar ratio: 2.92 - Year-to-date excess return: 19.74% [27][30] - **Text FADT_BERT Stock Selection Portfolio** - Annualized return since inception: 39.96% - Annualized excess return since inception: 30.76% - Sharpe ratio: 1.39 - Year-to-date absolute return: 20.49% - Year-to-date excess return: -2.04% [32][37]

- The report focuses on Hong Kong pharmaceutical indices and ETFs, highlighting the significant tracking scale of indices such as Guozheng Hong Kong Stock Connect Innovative Drugs and Hong Kong Innovative Drugs, with tracking ETF scales of 341.3 billion yuan and 226.4 billion yuan respectively [1][6][7] - Since August 2025, Guozheng Hong Kong Stock Connect Innovative Drugs and Hong Kong Innovative Drugs indices have seen net inflows of 145.1 billion yuan and 81.5 billion yuan respectively, ranking among the top two in terms of net inflows [7][10] - Seven indices focus on the innovative drug sector, with one compiled by Guozheng Index, two by China Securities Index, and four by Hang Seng Index. Indices compiled by the same company show similar compilation schemes and performance, while differences exist between companies. Year-to-date (YTD) returns show China Securities > Guozheng ≈ Hang Seng [7][12] - The industry distribution of indices varies: Guozheng and Hang Seng indices have a higher proportion of pharmaceutical industry, while China Securities indices have a higher proportion of biotechnology and life sciences tools and services [7][13] - Guozheng Hong Kong Stock Connect Innovative Drugs index experienced short-term deviation in September due to individual constituent stock adjustments during the sample adjustment period, but the deviation was corrected within two trading days, and the subsequent operation returned to normal [7][14]