Investment Rating - The report maintains a "Buy" rating for key companies in the electric power and new energy sectors, including Ningde Times, Mingyang Electric, and Sunshine Power [6]. Core Insights - The focus on affordability in the U.S. AI power sector is expected to influence the mid-term elections in 2026, with significant price increases in wholesale electricity driven by rising gas prices and capacity costs [2][15]. - The report highlights the potential for a significant increase in capacity prices due to the growing demand from data centers, which are projected to account for 95% of the incremental capacity [2][23]. - The "Energy as a Service" (EAAS) model is identified as a viable solution for data centers to achieve rapid power access while internalizing costs, with an estimated annual installation demand of 29-45 GW from 2026 to 2030 [4][31]. Summary by Sections Section 1: Price Increases and Capacity Demand - The PJM wholesale electricity price increased by 43.7% year-on-year, with gas prices contributing 66% and capacity price increases contributing 30% [2][15]. - The report estimates that capacity prices could rise by 100% to 300% from current levels due to the demand from data centers [2][15]. Section 2: Energy as a Service (EAAS) Model - The EAAS model is projected to maintain a demand of 29-45 GW per year from 2026 to 2030, with small gas turbines being economically advantageous [4][31]. - This model allows data centers to meet their urgent power needs while minimizing the impact on overall electricity costs [4][31]. Section 3: Electric Grid and Regulatory Changes - The report emphasizes the need for accelerated approval processes for electric grid and power sources to reduce electricity costs through economies of scale [3][31]. - Recent regulatory changes, including FERC's proposals, aim to streamline the approval process for large loads and enhance the capacity of the electric grid [31][40]. Section 4: Recommendations for Key Companies - The report recommends several companies for investment, including Ningde Times, Mingyang Electric, and Sunshine Power, all rated as "Buy" [9]. - Other recommended companies include Guodian NARI, Dongfang Electric, and Harbin Electric, which are positioned to benefit from the rising demand for electric power equipment [9]. Section 5: Market Dynamics and Future Outlook - The report anticipates a recovery in household solar storage demand driven by rising electricity prices, with potential for significant market growth [5][10]. - The overall electric power market is expected to experience a structural shift due to the increasing load from data centers, necessitating a reevaluation of investment strategies in the sector [31][35].

Group 1: Macro Insights - The report emphasizes the long-term value of precious metals like gold and silver due to geopolitical restructuring and deteriorating fiscal sustainability, suggesting a need to adjust pricing systems for scarce real assets and core equity assets [2][3] - It highlights that the global investment cycle post-2026 will be more "material-intensive," with demand for key commodities in AI and defense sectors remaining price-sensitive [2] Group 2: Market Strategy - The report suggests a focus on technology and cyclical "material" sectors to lead the rebound in the Hong Kong stock market, with a positive outlook for the first quarter [3] - It notes that the sentiment index has returned to neutral, indicating a potential for continued market recovery, particularly in AI chains and innovative pharmaceuticals [3] Group 3: Company-Specific Insights - Botai Carlink is highlighted as a leading player in smart cockpit solutions, with a target price of 286.67 HKD and expected revenue CAGR of 43.8% from 2025 to 2027, driven by its competitive advantages in high-end SoC platforms and the Harmony ecosystem [11] - Chuanheng Co., a leader in the phosphate chemical industry, is rated "Buy" with a target price of 50.73 CNY, benefiting from a tight global phosphate supply-demand situation and expected growth in its phosphate-related businesses [11] - Laopu Gold is expected to perform well during the Spring Festival sales season, with ongoing expansion and strong sales performance, maintaining a "Buy" rating [14] Group 4: Financial Sector Insights - The report indicates that financial stocks have seen increased positions, with banks and brokers experiencing slight increases in fund holdings, suggesting a positive outlook for the sector [9] - China Merchants Bank shows marginal improvement in operating performance, with a stable asset quality and a "Buy" rating maintained for both A and H shares [17] Group 5: Industry Trends - The report notes that the small-cap stocks have shown high crowding, particularly in sectors like building materials and textiles, suggesting limited upside potential in the short term [5] - The report also discusses the strong demand for wind power in the domestic market, with New Strong Union expected to benefit from increased TRB bearing penetration, maintaining an "Increase" rating [16]

Investment Rating - The report initiates coverage on Chuanheng Co., Ltd. with a "Buy" rating, assigning a target price of RMB 50.73 based on a 19x PE for 2026 [1][8][6]. Core Insights - Chuanheng Co., Ltd. is a leading player in the domestic phosphate chemical industry, possessing a nominal phosphate rock capacity of 3.3 million tons. The company's self-owned phosphate mines contribute to its high gross margin in the industry. The global phosphate supply-demand balance is expected to remain tight in the next 1-2 years, benefiting the company. Additionally, the anticipated growth in new energy demand is expected to enhance the company's business in iron phosphate, ammonium phosphate, and phosphoric acid [1][15][19]. - The company is projected to achieve a net profit of RMB 1.31 billion, RMB 1.62 billion, and RMB 1.8 billion for the years 2025-2027, representing year-on-year growth of 37%, 24%, and 11%, respectively [6][12]. Summary by Sections Phosphate Supply and Demand - The global expansion of phosphate rock production is slow due to limited new supply overseas and regulatory constraints in China. The demand for phosphate fertilizers is expected to grow by 3% annually starting in 2024, driven by the expansion of arable land and increasing new energy needs. The projected global phosphate rock supply-demand gap is estimated to reach 178,000 tons, 95,000 tons, and 121,000 tons from 2025 to 2027 [2][14]. New Energy Demand - The demand for phosphate chemical products has significantly increased due to the rise in lithium battery materials. Although there was a notable price drop in 2023-2024 due to concentrated production, the demand for energy storage and power batteries is expected to improve, leading to a recovery in the industry. The company's iron phosphate and industrial-grade ammonium phosphate are anticipated to benefit from this trend [3][16]. Profitability and Dividend Policy - The company has entered a growth realization phase, with a significant reduction in its debt ratio and an attractive dividend yield. The dividend payout ratio has increased to around 70%, with expected dividend yields of 3.7%, 4.5%, and 5.0% for 2025-2027 [17][33]. The company’s net profit for 2024 is projected to be RMB 956.48 million, reflecting a compound annual growth rate (CAGR) of approximately 37% since 2021 [17][12]. Market Perspective - The report contrasts with market concerns regarding the sustainability of tight phosphate supply. It argues that the demand for phosphate fertilizers remains robust, and the growth in iron phosphate production capacity will support continued tightness in supply. The company is expected to benefit from high phosphate prices and a favorable cost structure due to its significant export share [5][18]. Company Overview - Chuanheng Co., Ltd. is recognized as a leading integrated phosphate chemical enterprise in China, with a comprehensive production base from phosphate mining to fine phosphate chemicals. The company has a total phosphate rock capacity of 3.3 million tons and has been expanding its production capabilities [19][24].

证券研究报告 川恒股份 (002895 CH) 受益于行业高景气的磷化工一体化企业 2026 年 1 月 25 日│中国内地 农用化工 首次覆盖川恒股份给予"买入"评级，予以公司 26 年 19xPE，目标价 50.73 元。公司是国内磷化工的龙头企业，拥有磷矿名义产能 330 万吨，自有磷 矿优势使公司磷化工毛利率处于行业前列。受益于未来 1-2 年的全球磷矿供 需紧平衡局势，我们认为公司磷矿仍有望延续景气，且在新能源需求增长预 期下，公司的磷酸铁、工铵、磷酸等业务亦有望贡献弹性。公司股息拥有较 高吸引力，我们假设公司在 70%股利支付率水平下，25-27 年股息率达 3.7%/4.5%/5.0%。 全球磷矿扩产速度较慢，化肥和新能源需求支撑行业供需趋紧 由于海外磷矿新增有限，中国磷矿受制于超采监管、安全生产和环保要求， 小磷矿持续退出且新产能投产流程延长，综合导致全球磷矿扩产较慢，我们 预计 25-27 年磷矿新增产能约 401/947/658 万吨，主要集中于中国。而需求 端由于全球耕地面积扩张，24 年起磷肥需求每年呈 3%增长，21 年后新能 源需求进一步加剧供需紧张。我们测算 25-27 年全球磷 ...

证券研究报告 市场或逐步切向业绩修复方向 策略 逐步切换向绩优方向 2026 年 1 月 25 日│中国内地 国内周报 核心观点 上周 A 股市场在资金情绪分化下，小盘股涨幅居前，行业轮动持续，向后 看，资金面的弹性及后续轮动方向是市场主要关注点。我们认为 1 月中旬以 来，宽基 ETF 流出金额虽偏高，但考虑到险资等仍有增量、投资者存在套 利需求，市场持续放量下，资金侧仍有接力动能。轮动方向或由主题逐步切 往存在业绩印证的方向，历史复盘来看，业绩预告披露期内的，景气具备持 续修复能力的行业通常具备一定超额收益，具体到本轮，景气修复的线索或 主要集中在涨价链、高端制造及 AI 链中，结合拥挤度进一步考量，配置上 可关注电力设备、基础化工、半导体设备等，适度增配周期型红利。 资金侧仍有接力动能 1 月中旬以来，市场主要的宽基 ETF 净流出超 5000 亿元，挂钩沪深 300 的 ETF 是主要流出项，剔除涨跌幅以赎回份额统计，沪深 300ETF、中证 500ETF、中证 1000ETF 份额分别减少 29%、16%、45%。市场关心在 ETF 赎回下，资金后续的承接力量如何，我们认为：1）市场成交额仍在高位区 ...

证券研究报告 必选消费 看好 AKK 菌产业链投资机会 华泰研究 2026 年 1 月 24 日│中国内地 动态点评 AKK 益生菌——嗜粘蛋白阿克曼氏菌（Akkermansia muciniphila，简称 AKK）是一种独特的肠道共生菌，据上海交通大学医学院附属瑞金医院公众 号数据，AKK 益生菌占肠道菌群的 1%-5%；另据中国食品学报，AKK 益生 菌在改善肥胖、调节血糖血脂及代谢、抗炎等方面有积极作用，并具有潜在 的抗衰研究价值。 中国益生菌市场潜力较大，AKK 菌商业化落地加速 功效明显与技术进步共促中国市场 AKK 菌的成长势头可期 据仙乐健康公众号数据，2024 年全球 AKK 菌市场规模约 23.5 亿元人民币， 预计将在 2031 年快速增长至 44.6 亿元，对应期间 CAGR 为 9.6%，而中国 市场的势头或将更为强劲，预计在 2031 年市场规模有望突破 10 亿元，市 场渗透率约 20%；在中国，AKK 菌相关产品线上销售额从 25 年 1 月的 600 万元冲至 6 月的 2300 万元（来源魔镜洞察数据）。当前 AKK 处于快速发 展阶段，主要的增长驱动因素为较强的功效性与供 ...

证券研究报告 英特尔(INTEL) (INTC US) 制程追赶初见成效,看好 18A 订单落地 华泰研究 2026 年 1 月 23 日 | 美国 年报点评 半导体 英特尔 25Q4 业绩超预期,但 26Q1 指引液软且 18A 无客户更新,盘后股 价下跌超 12%(股价自 12/17 累计上涨 51%,也存在获利了结）。其中 1Q 指引审慎(或受季节性需求下供应约束影响),股价先下跌 6%;业绩会未 对 18A/14A 进展做更多更新,市场随之调整预期。我们认为,短期财报并 非核心，关键在于 Foundry 转型推进节奏,更应关注代工订单进展及制程 领先是否持续兑现。业绩端:Q4 营收 137 亿美元,同比-4.1%,超 VA 预 期（下同）2.1%（全年营收 529 亿美元,同比持平）;Non-GAAP 毛利率 和 EPS 为 37.9%和 0.15 美元，高于预期的 36.3%和 0.08 美元，其中毛利 率高指引 1.4pp。指引端: Q1 营收 117-127 亿美元（预期 125 亿美元）, Non-GAAP 毛利率/EPS 为 34.5%/0.00 美元（预期 36.1%和 0.04 美元）, ...

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]