Investment Rating - The report maintains a "Buy" rating for H&H International Holdings and By-Health Co., Ltd. [8][10] Core Insights - The AKK probiotic market is expected to grow significantly, with the global market projected to reach approximately 2.35 billion RMB in 2024 and 4.46 billion RMB by 2031, reflecting a CAGR of 9.6% [3] - The Chinese probiotic market is anticipated to exceed 137.7 billion RMB by 2026, driven by a growth rate of 11-12% in recent years [2] - The AKK probiotic has shown promising effects in improving obesity, regulating blood sugar and lipids, and has potential anti-aging benefits [1][2] Summary by Sections Market Potential - The Chinese probiotic market is rapidly expanding, with significant growth in various application sectors, including food and beverages (67%), dietary supplements (19%), healthcare (6%), animal nutrition (5%), and personal care (3%) [2] - The demand for AKK probiotics is expected to accelerate due to its clear efficacy and technological advancements in industrial production [2][4] Technological Advancements - Recent breakthroughs in the cultivation of AKK probiotics have improved supply stability, allowing for industrial-scale production [3][4] - The approval of AKK as a new food ingredient in regions like the EU and Australia indicates its safety and potential for broader market acceptance [4] Investment Opportunities - The report highlights investment opportunities in the AKK probiotic supply chain, recommending companies like H&H International Holdings and By-Health Co., Ltd. for their proactive engagement in this emerging market [5][8] - H&H International Holdings is noted for its product reserves related to AKK, while By-Health is expanding its product range to meet diverse consumer needs [5][11]

Investment Rating - The investment rating for Intel (INTC US) is maintained as "Buy" with a target price of $71.50 [6][4]. Core Views - Intel's Q4 2025 performance exceeded expectations, but the guidance for Q1 2026 is cautious, leading to a stock price drop of over 12% [1]. - The focus should be on the progress of the Foundry transformation and the advancement of foundry orders, rather than short-term financial results [1]. - The company is optimistic about the yield and customer progress for the 18A process node and the demand for Panther Lake [1]. Summary by Sections Financial Performance - Q4 2025 revenue was $13.7 billion, down 4.1% year-over-year but exceeded expectations by 2.1% [1]. - Non-GAAP gross margin and EPS were 37.9% and $0.15, respectively, surpassing expectations [1]. - Q1 2026 revenue guidance is between $11.7 billion and $12.7 billion, with a Non-GAAP gross margin of 34.5% and EPS of $0.00, indicating ongoing cost challenges [1]. Foundry Business - Foundry revenue for Q4 2025 was $4.5 billion, above the expected $4.4 billion, reflecting a year-over-year increase of 3.8% [2]. - The introduction of the 18A process node, featuring RibbonFET and PowerVia technologies, positions Intel to compete with TSMC [2]. - The company is expected to receive support from the U.S. government and industry partners to secure foundry orders [2]. Market Dynamics - CCG revenue was $8.2 billion, slightly below expectations, while DCAI revenue was $4.7 billion, reflecting strong data center demand [3]. - Intel's market share in the x86 CPU market remains strong, with a reported 72% share in the server segment [3]. - The company plans to enhance its CPU offerings through integration with NVIDIA's GPU technology [3]. Valuation and Forecast - Revenue forecasts for 2026 and 2027 have been raised by 2.6% and 4.2% to $57.5 billion and $61.0 billion, respectively [4]. - The valuation method has been adjusted to a price-to-book (PB) basis, with a target PB of 2.5x for 2026 [4]. - The target price has been increased to $71.50, reflecting confidence in the company's operational improvements and market positioning [4].

Investment Rating - The report maintains an "Overweight" rating for the company with a target price of RMB 62.00 [5][4]. Core Insights - The company is expected to achieve a net profit attributable to shareholders of RMB 7.8-9.2 billion for 2025, representing a year-on-year increase of 1093%-1307%. The non-recurring profit is projected to be RMB 6.8-8.2 billion, an increase of 356%-450%, aligning with expectations due to industry demand recovery and market share expansion [1][2]. - The penetration rate of TRB bearings is anticipated to increase significantly, with projections of 50% in 2025, 80% in 2026, and 90% in 2027 for onshore wind turbines. The company is leveraging its technological advantages and increasing production capacity to support this growth [2][3]. - The company is actively expanding into the gearbox bearing market, which has a low domestic production rate. It has completed prototype validations and small-scale supplies to several gearbox manufacturers, with expectations for large-scale supply in 2026 [3]. Summary by Sections Performance Forecast - The company expects to see a continuous increase in TRB main bearing penetration and market share, leading to an upward revision of revenue forecasts for 2026-2027. The projected net profits for 2025, 2026, and 2027 are RMB 8.77 billion, RMB 12.82 billion, and RMB 14.99 billion, respectively, with corresponding EPS of RMB 2.12, RMB 3.10, and RMB 3.62 [4][8]. Valuation - The report assigns a P/E ratio of 20x for 2026, resulting in a target price of RMB 62, reflecting the company's significant first-mover advantage in TRB bearings and leading position in gearbox bearing domestic substitution [4][5].

Quantitative Models and Construction Methods Model: AI CSI 1000 Enhanced Portfolio - **Construction Idea**: The model is based on the full-spectrum fusion factor, which integrates both high-frequency and low-frequency price-volume data using deep learning and multi-task learning techniques[6][7] - **Construction Process**: 1. Train 27 high-frequency factors using a deep learning model to obtain high-frequency deep learning factors 2. Use multi-task learning to extract end-to-end features from low-frequency price-volume data, resulting in low-frequency multi-task factors 3. Combine the high-frequency and low-frequency factors to form the full-spectrum fusion factor[6] - **Evaluation**: The model shows significant excess returns and a high information ratio, indicating strong performance and effective risk management[1][7] - **Backtest Results**: - Annualized excess return: 21.60% - Annualized tracking error: 6.06% - Information ratio (IR): 3.57 - Maximum drawdown of excess return: 7.55% - Calmar ratio of excess return: 2.86[1][7] Model: LLM-FADT Text Stock Selection Strategy - **Construction Idea**: The model enhances the BERT-FADT strategy by incorporating additional interpretations from a large language model (LLM), including new title interpretations, market catalysts, implied meanings, potential risks, and return guidance[2][14][17] - **Construction Process**: 1. Input six types of text into a fine-tuned FinBERT model: original text, new title interpretations, market catalysts, implied meanings, potential risks, and return guidance 2. Convert these texts into text feature vectors 3. Train an XGBoost model using these enriched text features[17] - **Evaluation**: The LLM-FADT strategy is more stable and has smaller excess drawdowns compared to the BERT-FADT strategy, showing better performance in extreme market conditions[2][14][20] - **Backtest Results**: - Annualized return: 30.10% - Annualized excess return: 25.52% - Sharpe ratio: 1.18 - Information ratio (IR): 2.00[2][20][24] Model: AI Industry Rotation Model - **Construction Idea**: The model uses the full-spectrum fusion factor to score 32 primary industries and constructs a weekly rebalancing strategy by equally weighting the top 5 industries[3][38] - **Construction Process**: 1. Score each industry using the full-spectrum fusion factor based on the industry component stocks 2. Select the top 5 industries with the highest scores 3. Equally weight these industries and rebalance weekly[38][43] - **Evaluation**: The model effectively utilizes AI's feature extraction capabilities to capture patterns in multi-frequency price-volume data, complementing top-down strategies[3][38] - **Backtest Results**: - Annualized return: 26.87% - Annualized excess return: 19.02% - Maximum drawdown of excess return: 12.43% - Sharpe ratio of excess return: 1.85[3][41] Model: AI Thematic Index Rotation Model - **Construction Idea**: The model scores 133 thematic indices using the full-spectrum fusion factor and constructs a weekly rebalancing strategy by equally weighting the top 10 thematic indices[4][28] - **Construction Process**: 1. Score each thematic index using the full-spectrum fusion factor based on the index component stocks 2. Select the top 10 thematic indices with the highest scores 3. Equally weight these indices and rebalance weekly[28][31] - **Evaluation**: The model leverages AI to identify and capitalize on trends in thematic indices, providing a diversified and dynamic investment approach[4][28] - **Backtest Results**: - Annualized return: 16.92% - Annualized excess return: 9.37% - Maximum drawdown of excess return: 20.79% - Sharpe ratio of excess return: 0.73[4][30] Model Backtest Performance AI CSI 1000 Enhanced Portfolio - Annualized excess return: 21.60% - Annualized tracking error: 6.06% - Information ratio (IR): 3.57 - Maximum drawdown of excess return: 7.55% - Calmar ratio of excess return: 2.86[1][7] LLM-FADT Text Stock Selection Strategy - Annualized return: 30.10% - Annualized excess return: 25.52% - Sharpe ratio: 1.18 - Information ratio (IR): 2.00[2][20][24] AI Industry Rotation Model - Annualized return: 26.87% - Annualized excess return: 19.02% - Maximum drawdown of excess return: 12.43% - Sharpe ratio of excess return: 1.85[3][41] AI Thematic Index Rotation Model - Annualized return: 16.92% - Annualized excess return: 9.37% - Maximum drawdown of excess return: 20.79% - Sharpe ratio of excess return: 0.73[4][30]

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

Investment Rating - The report maintains an "Overweight" rating for the real estate development and service sectors [6] Core Insights - The real estate sector is experiencing a significant reduction in holdings, with public funds and northbound capital reaching new lows in their investment proportions. The market is currently stabilizing, with a focus on recovery in core cities, particularly first-tier cities [1][2] - Recommended investment opportunities include companies with strong credit, urban presence, and product quality, as well as those with robust operational capabilities to manage cash flow during market adjustments [1] - The report highlights a shift in holdings concentration, with Beike rising to the top position among public fund holdings, indicating a narrowing of investor divergence in the sector [3] Summary by Sections Public Fund Holdings - As of Q4 2025, the total market value of public fund holdings in the real estate sector was 38.8 billion yuan, a 31% decrease quarter-on-quarter. The sector's holdings accounted for 0.43% of total stock investments, down 0.19 percentage points [2] - The real estate sector index fell by 8.9%, ranking 30th out of 31 sectors, primarily due to declining fundamentals and some companies hitting new stock price lows [2] Northbound Capital - Northbound capital's total holdings in real estate stocks were 11.5 billion yuan, a 17% decrease quarter-on-quarter, representing 0.45% of total northbound holdings [4] - The top five real estate stocks held by northbound capital include China Merchants Shekou, Poly Developments, and others, with notable increases in holdings for companies with "real estate+" attributes [4] Recommended Companies - Key recommended companies include: - Yuexiu Property (123 HK) with a target price of 7.06 HKD and a "Buy" rating [8] - Longfor Group (960 HK) with a target price of 15.21 HKD and a "Buy" rating [8] - Greentown Service (2869 HK) with a target price of 6.56 HKD and a "Buy" rating [8] - China Overseas Development (688 HK) with a target price of 19.08 HKD and a "Buy" rating [8] - China Merchants Shekou (001979 CH) with a target price of 12.79 CNY and a "Buy" rating [8] - CR Land (1109 HK) with a target price of 36.45 HKD and a "Buy" rating [8] - Others include companies like Greenland China, and Hong Kong local firms benefiting from market recovery [1][8] Performance Insights - Beike's market value increased significantly, reflecting a strong investor interest, while other companies like Poly Developments and China Merchants Shekou saw reductions in their holdings [3][4] - The report emphasizes the importance of operational efficiency and cash flow management for companies navigating the current market challenges [1][3]

Investment Rating - The report maintains an "Overweight" rating for the banking sector, indicating an expectation that the sector will outperform the benchmark index [1]. Core Insights - The banking index has declined by 8.4% since December, primarily due to concerns over real estate and interest rate cuts, leading to a valuation drop to 0.65x PB, which is at the 65th percentile over the past five years. Some quality stocks are offering a dividend yield close to 6% for 2025 [2][6]. - Despite the market's concerns, the core revenue trends in the banking sector remain positive. Eight banks, including Nanjing and Ningbo, reported improved revenues and profits for 2025, suggesting a favorable outlook for 2026 as net interest margins stabilize and wealth management income contributes positively [7][9]. - The report emphasizes the importance of focusing on high-quality banks with strong fundamentals and earnings elasticity, such as Ningbo, Nanjing, and Chongqing Rural Commercial Bank, as well as those with excellent dividend yield ratios like Shanghai and Chengdu banks [7][9]. Summary by Sections Investment Recommendations - The report highlights specific banks with investment recommendations: - Chengdu Bank (601838 CH): Buy with a target price of 23.25 [5]. - Chongqing Rural Commercial Bank (3618 HK): Buy with a target price of 8.34 [5]. - Nanjing Bank (601009 CH): Buy with a target price of 14.78 [5]. - Shanghai Bank (601229 CH): Buy with a target price of 12.38 [5]. - Ningbo Bank (002142 CH): Buy with a target price of 35.12 [5]. - Chongqing Rural Commercial Bank (601077 CH): Hold with a target price of 8.29 [5]. Market Dynamics - The report notes that the banking sector's credit issuance has been robust at the start of 2026, with a significant portion of new credit issued in January, indicating a shift in lending patterns. The focus remains on sectors like transportation, energy, and manufacturing [9][10]. - The report anticipates a narrowing of the decline in net interest margins for 2026, driven by the optimization of funding costs and stabilization in new loan pricing. The impact of potential interest rate cuts by the central bank is expected to be limited due to the timing of loan repricing [10][21]. Performance Metrics - The report provides performance metrics for various banks, indicating improvements in revenue and profit growth for several institutions. For instance, Nanjing Bank is projected to achieve a net profit of 219 billion yuan in 2025, with a growth rate of 8.5% [26]. - The report also highlights the asset quality of banks, noting that the average non-performing loan ratio for mortgage loans remains below 1%, indicating manageable risk levels [6][9].

证券研究报告 金工 AI 量化的当下与未来 2026 年 1 月 22 日│中国内地 深度研究 人工智能 100：AI 量化的过去、现在与未来 本文是华泰人工智能系列的第 100 篇研究报告。过往的八年半里，我们亲 历了量化投资行业的这场深刻变革：技术路径上，从早期的机器学习，演进 到深度学习，再到如今以大语言模型为代表的新范式。应用场景上，从早期 的因子合成，拓展至因子挖掘与端到端建模，进而渗透到组合优化、行业轮 动、资产配置、流程管理等投资的各个环节。行业认知上，从最初的质疑与 观望，逐渐转向接纳与尝试，直至今日的全面拥抱。第 100 篇研究，既是 对过往足迹的回顾，也是对未来征途的眺望。 AI 量价端到端策略的演进 在量价研究普遍内卷的当下，端到端建模不仅是效率的提升，亦是一种回归 原始数据的研究范式。我们已实现从日频、周频等低频数据到逐笔成交、 level2 高频数据的全面覆盖，通过引入 GRU 及 Transformer 等架构，模型 得以直接在原始数据空间中学习量价数据间的内在联系。展望未来，全频段 融合或是关键，未来的端到端模型或将致力于打破时间尺度与数据形态的边 界，一方面通过对比学习等技术实 ...

Investment Rating - The industry investment rating is "Overweight" for both the construction and building materials sectors [6]. Core Insights - The report emphasizes the importance of balanced allocation between traditional cyclical building materials and emerging technology growth opportunities, driven by supportive real estate policies aimed at stabilizing the market [1]. - The path for resolving real estate debt is becoming clearer, with significant credit impairment already reflected in the consumption building materials sector, suggesting a narrowing of credit impairment risk exposure [2]. - The decline in real estate construction is expected to slow, with price increases for construction materials continuing, particularly benefiting leading companies in the sector [3]. - The second-hand housing market is showing signs of stabilization, with "stock renovation" expected to be a key theme in 2026, potentially boosting demand for decorative and functional building materials [4]. Summary by Sections Real Estate Policy and Market Outlook - The Ministry of Housing and Urban-Rural Development is focusing on stabilizing the real estate market through targeted policies, which is expected to accelerate the recovery of the sector [1]. - Data indicates that the real estate market is beginning to stabilize, with some companies showing signs of revenue improvement due to increased market share and expansion into overseas markets [1]. Credit Impairment and Debt Resolution - Vanke's recent bondholder meeting approved a significant extension plan, indicating a rational approach to debt resolution within the industry [2]. - Most building materials companies have already accounted for substantial credit impairments, with many reporting over 50% impairment on specific items [2]. Construction Activity and Material Pricing - Real estate sales, new construction, and completion areas have shown declines of 8.7%, 20.4%, and 18.1% year-on-year, respectively, but the rate of decline is expected to slow [3]. - Leading companies in the sector have begun to implement price increases for construction materials, indicating a potential turning point in the market [3]. Second-Hand Housing Market and Renovation Demand - The retail sales of construction and decoration materials reached 167.1 billion yuan in 2025, reflecting a decline of 2.7% year-on-year, primarily due to high base effects from previous quarters [4]. - The report notes a decrease in the listing volume of second-hand homes, suggesting a tightening supply that could lead to price improvements [4]. Recommended Companies - The report recommends several companies for investment, including: - China Liansu (2128 HK) with a target price of 6.35 yuan - Weixing New Materials (002372 CH) with a target price of 14.34 yuan - Rabbit Baby (002043 CH) with a target price of 16.01 yuan - Beixin Building Materials (000786 CH) with a target price of 29.64 yuan - Dongfang Yuhong (002271 CH) with a target price of 17.19 yuan [7][9].

Investment Rating - The investment rating for the company is maintained as "Buy" with a target price of RMB 28.00 [1]. Core Insights - The company is expected to see a significant acceleration in performance in Q4, driven by high demand and improved capacity utilization. The projected revenue for 2025 is RMB 1.38 billion, representing a year-on-year increase of 19.75%, with net profit expected to reach RMB 370 million, up 48.9% [5][6]. - The company is well-positioned to benefit from the growing demand for functional sugars, with new production capacity expected to come online in 2026, further enhancing revenue growth [7][8]. Financial Projections - Revenue projections for the upcoming years are as follows: - 2024: RMB 1.15 billion (+32.64%) - 2025: RMB 1.38 billion (+19.74%) - 2026: RMB 1.74 billion (+25.99%) - 2027: RMB 2.23 billion (+28.52%) [4]. - Net profit forecasts are: - 2024: RMB 246 million (+27.26%) - 2025: RMB 367 million (+49.45%) - 2026: RMB 471 million (+28.27%) - 2027: RMB 620 million (+31.70%) [4]. - The company’s EPS is projected to increase from RMB 0.58 in 2024 to RMB 1.48 in 2027 [4]. Valuation Metrics - The company’s PE ratio is expected to decrease from 38.44 in 2024 to 15.23 in 2027, indicating improving valuation as earnings grow [4]. - The PB ratio is projected to decline from 5.62 in 2024 to 3.37 in 2027, reflecting a more attractive valuation over time [4]. - The EV/EBITDA ratio is expected to fall from 24.75 in 2024 to 9.79 in 2027, suggesting enhanced operational efficiency and profitability [4].