Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The coffee industry remains highly prosperous with brands actively opening new stores, although there is a caution regarding short-term data volatility due to the seasonal downturn [3] - The tea beverage sector is under slight pressure as it enters the off-season, with a trend of subsidy reductions expected despite the government's investigation into delivery platforms [3] - The e-commerce sector continues to face challenges, showing lackluster performance due to the domestic consumption environment [3] - Music streaming platforms are highlighted as valuable internet assets driven by domestic demand, with a recommendation to focus on music subscription platforms [3] - The virtual asset and trading platform market is experiencing limited catalysts, with cryptocurrency prices remaining volatile [3] - The automotive service sector is seeing expansion, with new 4S stores being opened by Zhongsheng Group in various cities [3] - The internet healthcare sector is gaining attention with OpenAI's launch of "ChatGPT Health," suggesting a focus on this area [3] - The AI and cloud sectors are viewed positively, with recommendations to monitor leading tech companies with strong cash flows [3] - The media sector is showing signs of recovery, with new games performing well and user growth in the gaming segment [3] Summary by Sections 1.1 Consumer & Internet - The Hang Seng non-essential consumer index decreased by 0.98%, underperforming the Hang Seng index by 0.57 percentage points [8] - Notable stock performances include: Gu Ming (+8.72%), Ba Wang Tea (+6.99%), and Luckin Coffee (-6.47%) [8][10] 1.2 Platform & Technology 1.2.1 Streaming Platforms - The Hang Seng media index increased by 3.22%, outperforming both the Hang Seng index and the technology index [15] - Key stock performances include: iQIYI (+0.99%) and Tencent Music (-2.86%) [15] 1.2.2 Virtual Assets & Internet Brokers - As of January 9, the global cryptocurrency market cap reached $319.54 billion, up 3.40% [22] - Bitcoin and Ethereum prices were $90,505 and $3,083.14, reflecting changes of +0.6% and -1.2% respectively [22] 1.2.3 Automotive Services - The Hang Seng composite index rose by 0.38%, with notable stock performances including Advance Auto Parts (+12.73%) [31] 1.2.4 O2O - The Hang Seng internet technology index decreased by 0.27%, with key stock performances such as JD Health (+13.31%) and Didi Global (-7.19%) [37] 1.2.5 AI & Cloud - The Nasdaq internet index increased by 1.59%, with Amazon (+9.22%) and Google (+4.26%) showing strong performances [39] 1.3 Media - The Shenwan一级传媒 index increased by 13.14%, with the advertising and marketing sector showing the largest gains [46] - Key stock performances include: Xindong Company (+18.50%) and Tencent Holdings (-1.93%) [46]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The competition in AI entry points is intensifying, with major companies increasing their investments. China's AI presence globally has significantly improved, with domestic large models continuously iterating. Despite GPT-5.2 and Gemini 3 Pro leading, Chinese models have effectively altered the North American dominance in the competitive landscape. In the global Top 10, three positions are held by Chinese models, and in the Top 15, there are six Chinese companies. By 2025, China's open-source AI model usage is expected to account for over 70% of the global market [2][11][19] - The demand for inference has surged, with the emergence of o1 class inference models unlocking approximately 10 times the potential of traditional models in terms of inference-time compute. The demand for computing power has shifted from being solely "training-driven" to a dual focus on "training + inference" [2][5][37] - The battle for entry points has evolved beyond mobile devices to OS-level intelligent agents and super apps. By December 24, 2025, ByteDance's AI application Doubao announced daily active users (DAU) exceeding 100 million, while Qianwen App reached over 30 million monthly active users within 23 days of public testing, becoming the fastest-growing AI application globally. Doubao bypasses traditional interfaces, creating an "AI operating system" that directly interacts with super apps like WeChat and Alipay, challenging the rules of the traditional app era [2][44][45] Summary by Sections AI Entry Point Competition - China's AI global presence has significantly improved, with domestic large models continuously iterating. In the global Top 10, three positions are held by Chinese models, and in the Top 15, there are six Chinese companies. By 2025, China's open-source AI model usage is expected to account for over 70% of the global market [2][11][19] - The competition for entry points has evolved beyond mobile devices to OS-level intelligent agents and super apps, with significant user engagement reported for new AI applications [2][44][45] Domestic Chip Breakthroughs - The smart computing center in China is expanding, with a projected compound annual growth rate (CAGR) of 57% from 2020 to 2028, reaching 2,781.9 EFLOPS by 2028. Domestic chip technology is steadily improving, with local cloud service providers accelerating the construction of heterogeneous environments [5][50] - Domestic general-purpose GPUs are upgrading from "usable" to "good," with performance metrics approaching those of leading international models. The production capacity of domestic chip manufacturers like SMIC is continuously increasing, providing solid support for domestic AI chip production [5][53][54] Supply and Demand Dynamics - The demand side is characterized by a surge in inference demand as AI applications become more prevalent, while the supply side sees continuous improvements in domestic GPU performance and accelerated adaptation by cloud service providers [5][59] - The AI server market is expected to see a shift towards inference servers becoming the mainstream, with a projected market size of approximately $39.3 billion in 2024, reflecting a year-on-year growth of 49.7% [5][64]

Investment Rating - The report does not explicitly state an investment rating for the industry or specific companies. Core Insights - Li Ning is accelerating the rollout of its "Dragon Store" pop-up shops, with the first opening in Beijing on December 14, 2025, and plans for additional locations in major cities [1][11] - Natural Hall has become the top brand in the cosmetics collection store category for the first ten months of 2025, indicating strong operational capabilities from the company [2][16] - The overall retail sales of cosmetics in China for 2025 are projected to reach 822.53 billion yuan, with a year-on-year growth of 6.18% [2][16] - The apparel retail sector showed a year-on-year growth of 3.5% in November, although the growth rate has slowed compared to October [3][24] Summary by Sections Li Ning's Dragon Store Launch - Li Ning's first Dragon Store opened in Beijing's Sanlitun area, themed "Dragon Glory," showcasing a new product line [1][11] - The company plans to open 18 additional stores, integrating cultural elements into the store design to enhance brand experience [11][12] Natural Hall's Market Position - Natural Hall leads the cosmetics collection store rankings, with a market share of 57.03% for domestic brands, reflecting a recovery in the cosmetics market [2][16] - The online retail channel for cosmetics grew by 9.36%, while offline sales increased by 2.38% [2][16] Industry Data Tracking - Apparel retail sales in November grew by 3.5%, but the growth rate decreased compared to October, indicating potential volatility in recovery [3][24] - Jewelry retail sales saw a decline of 8.5% year-on-year, but there is a recovery in regional consumption [3][24] Investment Recommendations - The report suggests that Haizhu's business transformation aligns with consumer trends, indicating potential for future growth [4] - In the beauty sector, companies like Juzhi Biological and Jinbo Biological are recommended due to their strong operational resilience and product launches [4] - The jewelry sector remains attractive with rising gold prices, recommending brands like Laopu Gold for investment [4] Market Performance Overview - The report notes that the Shanghai Composite Index and Shenzhen Component Index experienced gains of 2.79% and 4.40%, respectively, with the textile and apparel sector rising by 2.65% [5][39] - Specific companies like Yingfeng and Jiumuwang showed significant stock price increases, while others like Lianfa and Wanlima faced declines [5][39]

Group 1 - The report highlights that the bond market experienced a significant decline at the beginning of the year due to several new changes compared to the end of the previous year, including dual pressure from equity and commodity markets, validated supply concerns, and amplified market volatility from institutional behavior [2][3][25] - The equity market saw a strong upward trend, with indices such as the Shanghai Composite and the ChiNext Index showing notable gains, which historically correlates with rising interest rates in the bond market [7][8] - Commodity prices also surged, with significant increases in old-cycle commodities like rebar and glass, indicating a shift in market dynamics that further pressured the bond market [8][15] Group 2 - Supply concerns were validated as the issuance of government bonds increased significantly, with a net supply expected to reach approximately 1.1 trillion yuan in January, higher than the same period last year [15][27] - The central bank's bond purchase announcements fell short of market expectations, leading to a "bullish news falling flat" scenario, which contributed to the bond market's downward adjustment [22][25] - Institutional behavior exacerbated market volatility, with a stark contrast in bond buying and selling activities between the end of the previous year and the beginning of the new year, indicating extreme market conditions [3][22] Group 3 - The report suggests that while there may be a temporary release of market pressure due to emotional factors, the ongoing supply and liquidity variables remain uncertain, necessitating cautious evaluation of the downward potential for interest rates [5][27] - The bond market is expected to maintain a strategy favoring short-duration bonds, with low spreads, while long-duration bonds lack systematic opportunities, even if a rebound occurs [5][27] - The report emphasizes the importance of monitoring the central bank's liquidity management and the issuance pace of government bonds, as these factors will significantly influence the bond market's trajectory [4][26]

Investment Rating - The SW Machinery Equipment Index increased by 5.39% during the week of January 5 to January 9, 2026, ranking 10th among 31 primary industry categories [12][14]. Core Insights - The report anticipates a significant increase in domestic rocket launches in 2026, driven by the urgent demand for satellite deployment [21]. - The robotics sector is expected to experience a strong market trend in Q1 2026, with advancements in humanoid robots [21]. - The nuclear fusion energy sector is highlighted as a potential investment opportunity during the 14th Five-Year Plan period, with significant technological breakthroughs reported [22]. - The global shipbuilding industry is showing signs of recovery, with new ship prices increasing and order volumes significantly improving [31]. - The engineering machinery sector is entering an upward cycle, with robust domestic and export sales of excavators and loaders [35]. - The report indicates varying degrees of industry performance, with general machinery under pressure, while engineering machinery and railway equipment show positive trends [46][45]. Summary by Sections 1. Stock Portfolio - Recommended stocks include Chaojie Co., Feiwo Technology, Guanglian Aviation, Hengli Hydraulic, Lianchuang Optoelectronics, XCMG, SANY Heavy Industry, Zoomlion, LiuGong, and China Shipbuilding [10]. 2. Market Review - The SW Machinery Equipment Index rose by 5.39% in the first week of 2026, outperforming the CSI 300 Index, which increased by 2.79% [12][14]. 3. Key Data Tracking 3.1 General Machinery - The manufacturing PMI was reported at 50.1% in December, indicating a slight recovery [23]. 3.2 Engineering Machinery - Excavator sales reached 23,095 units in December, marking a year-on-year increase of 17.6% [35]. 3.3 Railway Equipment - Railway fixed asset investment has maintained a steady growth rate of around 6% since 2025 [45]. 3.4 Shipbuilding - The global new ship price index reached 184.65 in December, with a month-on-month increase of 0.17% [46]. 3.5 Oil Service Equipment - The oil service equipment sector is stabilizing, with high demand in the Middle East [49]. 3.6 Industrial Gases - A decrease in raw material prices is expected to improve profitability in the steel sector, boosting demand for industrial gases [55]. 3.7 Gas Turbines - GEV's new gas turbine orders grew by 39% year-on-year in the first three quarters of 2025, indicating a robust market [57].

Quantitative Models and Construction Methods 1. Model Name: Fund Selection Strategy Based on Trading Motivation Factor and Stock Spread Income Factor - **Model Construction Idea**: This strategy combines the trading motivation factor and the stock spread income factor to select funds with high stock spread income, active trading motivation, and low likelihood of performance manipulation[2][24] - **Model Construction Process**: - The **trading motivation factor** is derived from fund report data, including fund flows, stock buy/sell amounts, and the proportion of top 20 stocks traded[47] - The **stock spread income factor** is calculated from the stock spread income in the fund's profit statement[47] - The strategy adopts a semi-annual rebalancing approach, adjusting positions at the end of March and August each year, and selects funds from active equity funds after deducting transaction costs[24] - **Model Evaluation**: The strategy has shown long-term outperformance against the Wind Active Equity Hybrid Fund Index, with a fee-adjusted annualized excess return of 3.64% since March 2011[24][28] 2. Model Name: Fund Selection Strategy Based on Fund Manager Trading Uniqueness - **Model Construction Idea**: This strategy evaluates the uniqueness of fund managers' trading behaviors by constructing a network based on their holdings and transactions, aiming to identify funds with distinctive trading styles[3][32] - **Model Construction Process**: - A network is built using detailed fund manager holdings and transaction data - A metric is calculated to measure the uniqueness of each fund manager's trading behavior compared to their peers[48] - The strategy adopts a semi-annual rebalancing approach, adjusting positions in early April and September each year, and selects funds from active equity funds, general stock funds, and flexible allocation funds after deducting transaction costs[32] - **Model Evaluation**: The strategy has demonstrated significant outperformance, achieving a fee-adjusted annualized excess return of 5.66% since its inception[32][36] 3. Model Name: Industry-Themed ETF Selection Strategy Based on Filing Information - **Model Construction Idea**: This strategy leverages the forward-looking information from the public disclosure stage of ETF filing materials to construct an industry-themed filing similarity factor (T+1), aiming to capture market investment hotspots[4][39] - **Model Construction Process**: - The T+1 factor is constructed by calculating the similarity between the indices tracked by newly filed ETFs and existing market indices[48] - The strategy adopts a monthly rebalancing approach, selecting ETFs from industry-themed ETFs with a transaction fee rate of 0.1% per side, using the CSI 800 Index as the benchmark[39] - **Model Evaluation**: The strategy has consistently outperformed the CSI 800 Index since December 2018, with a fee-adjusted annualized excess return of 11.33%[39][44] --- Model Backtesting Results 1. Fund Selection Strategy Based on Trading Motivation Factor and Stock Spread Income Factor - **December 2025 Return**: 1.56% (vs. 3.06% for the benchmark)[28] - **Annualized Return**: 10.85% (vs. 7.33% for the benchmark)[28] - **Annualized Volatility**: 21.62% (vs. 19.97% for the benchmark)[28] - **Sharpe Ratio**: 0.50 (vs. 0.37 for the benchmark)[28] - **Maximum Drawdown**: 48.39% (vs. 45.42% for the benchmark)[28] - **Annualized Excess Return**: 3.64%[28] - **IR**: 0.61[28] - **Excess Maximum Drawdown**: 19.22%[28] - **December 2025 Excess Return**: -1.54%[28] 2. Fund Selection Strategy Based on Fund Manager Trading Uniqueness - **December 2025 Return**: 5.36% (vs. 3.06% for the benchmark)[36] - **Annualized Return**: 13.40% (vs. 7.87% for the benchmark)[36] - **Annualized Volatility**: 19.52% (vs. 18.30% for the benchmark)[36] - **Sharpe Ratio**: 0.69 (vs. 0.43 for the benchmark)[36] - **Maximum Drawdown**: 37.26% (vs. 45.42% for the benchmark)[36] - **Annualized Excess Return**: 5.66%[36] - **IR**: 1.09[36] - **Excess Maximum Drawdown**: 10.84%[36] - **December 2025 Excess Return**: 2.27%[36] 3. Industry-Themed ETF Selection Strategy Based on Filing Information - **December 2025 Return**: 5.84% (vs. 3.31% for the benchmark)[43] - **Annualized Return**: 19.22% (vs. 6.90% for the benchmark)[43] - **Annualized Volatility**: 21.05% (vs. 18.85% for the benchmark)[43] - **Sharpe Ratio**: 0.91 (vs. 0.37 for the benchmark)[43] - **Maximum Drawdown**: 34.89% (vs. 42.96% for the benchmark)[44] - **Annualized Excess Return**: 11.33%[44] - **IR**: 0.64[44] - **Excess Maximum Drawdown**: 19.07%[44] - **December 2025 Excess Return**: 2.53%[44]

Report Industry Investment Rating - Not provided in the given content Core Viewpoints - Among the bonds with discounted transactions, "24 Chanrong 08" had a relatively large deviation in bond valuation price. Among the bonds with rising net prices, "22 Vanke 02" had a prominent deviation in valuation price. Among the Tier 2 and perpetual bonds with rising net prices, "25 ABC Tier 2 Capital Bond 04B(BC)" had a relatively large deviation in valuation price; among the senior unsecured bonds with rising net prices, "25 ABC TLAC Non - Capital Bond 02C(BC)" had a prominent deviation in valuation price. Among the bonds with a transaction yield higher than 5%, real - estate bonds ranked high [2]. - The changes in credit bond valuation yields were mainly distributed in the [-5,0) interval. The transaction terms of non - financial credit bonds were mainly distributed within 0.5 years, and the proportion of discounted transactions for bonds with terms between 0.5 and 1 year was the highest; the transaction terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years, and the proportion of discounted transactions for bonds within 1 year was the highest. By industry, the bonds in the light manufacturing industry had the largest average deviation in valuation price [2]. Summary by Relevant Catalogs Discounted Transaction Tracking - Bonds such as "24 Chanrong 08", "24 Chanrong 06", etc. in the non - banking finance industry had relatively large deviations in valuation price, with deviations ranging from - 1.17% to - 0.82%. Bonds like "20 Boshui 02" in the agriculture, forestry, animal husbandry and fishery industry and "25 Qingcheng 09" in the urban investment industry also had certain deviations in valuation price [4]. Tracking of Bonds with Rising Net Prices - Real - estate bonds such as "22 Vanke 02", "22 Vanke 06" etc. had a valuation price deviation of 3.98%. Bonds in the banking industry like "25 ABC Tier 2 Capital Bond 04B(BC)" and "25 ABC TLAC Non - Capital Bond 02C(BC)" also had certain deviations in valuation price [6]. Tracking of Tier 2 and Perpetual Bond Transactions - Bonds such as "25 ABC Tier 2 Capital Bond 04B(BC)", "24 Fudian Bank Tier 2 Capital Bond 01" etc. had different degrees of deviation in valuation yield, with deviations ranging from - 5.35bp to - 0.56bp [8]. Tracking of Senior Unsecured Bond Transactions - Bonds such as "25 ABC TLAC Non - Capital Bond 02C(BC)", "25 CITIC Baixin Bank Small and Micro - enterprise Bond 01" etc. had deviations in valuation yield, with deviations ranging from - 1.46bp to - 0.58bp [10]. Tracking of Bonds with a Transaction Yield Higher than 5% - Real - estate bonds such as "22 Vanke 02", "22 Vanke 06" etc. and non - banking finance bonds like "23 Chanrong 05", "23 AVIC Chanrong MTN001 (Science and Technology Innovation Note)" had a transaction yield higher than 5% [11]. Distribution of Credit Bond Valuation Deviations on the Day - The changes in credit bond valuation yields were mainly distributed in the [-5,0) interval [2]. Distribution of Non - financial Credit Bond Transaction Terms on the Day - The transaction terms of non - financial credit bonds were mainly distributed within 0.5 years, and the proportion of discounted transactions for bonds with terms between 0.5 and 1 year was the highest [2]. Distribution of Tier 2 and Perpetual Bond Transaction Terms on the Day - The transaction terms of Tier 2 and perpetual bonds were mainly distributed between 4 and 5 years, and the proportion of discounted transactions for bonds within 1 year was the highest [2]. Discounted Transaction Proportion and Transaction Scale of Non - financial Credit Bonds by Industry - The bonds in the light manufacturing industry had the largest average deviation in valuation price [2].

Investment Rating - The report maintains a positive outlook on the wind power industry, indicating a long-term growth trend driven by economic factors and increasing demand for renewable energy [6]. Core Insights - Global wind power demand is expected to maintain a long-term boom due to economic drivers and the increasing electrification needs, with projected global new installations of 167GW in 2025, a year-on-year increase of 34%, and 196GW in 2026, a year-on-year increase of 18% [2][13]. - Domestic wind power installations are anticipated to break the five-year planning cycle, with significant contributions from offshore wind, replacement projects, and green electricity connections, leading to continued growth [2][14]. - The overseas wind power market is projected to experience sustained demand growth, with a compound annual growth rate (CAGR) of 14% from 2025 to 2030, particularly in the European offshore wind sector, which is expected to grow at a CAGR of 32% [3][50]. Summary by Sections Economic Drivers of Global Wind Power Demand - The report highlights that the global wind power demand is expected to remain robust due to economic factors and the electrification trend, with specific forecasts for new installations in 2025 and 2026 [2][13]. - Domestic demand is supported by market reforms and initiatives such as "old-for-new" replacements and green electricity connections, with expectations of continued growth in installations [14][19]. Profitability and Investment Recommendations - The report suggests that the profitability of wind turbine manufacturers is set to improve, with a notable increase in the average bidding price for onshore wind turbines in 2025, which is expected to rise by approximately 11% [4][29]. - The report recommends focusing on three main investment lines: turbine manufacturers, offshore cable and foundation suppliers, and component manufacturers benefiting from domestic and international market opportunities [6][51]. Offshore Wind Market Dynamics - The report indicates that the European offshore wind market is poised for significant growth, with a recovery in project bidding expected in 2026 after a period of delays and cancellations [59][67]. - The report emphasizes the importance of policy adjustments in Europe that are likely to enhance project success rates and support continued demand growth in the offshore wind sector [59][61].

摘要 根据 Wind 数据，折价成交个券中，"24 产融 08"债券估值价格偏离幅度较大。净价上涨成交个券中，"25 盱眙 02" 估值价格偏离程度靠前。净价上涨成交二永债中，"25 贵州银行二级资本债 01"估值价格偏离幅度较大；净价上涨成 交商金债中，"25 农行 TLAC 非资本债 02C(BC)"估值价格偏离幅度靠前。成交收益率高于 5%的个券中，金融债排名靠 前。 信用债估值收益变动主要分布在(0,5]区间。非金信用债成交期限主要分布在 0.5 年内，其中 0.5 年内品种折价成交 占比最高；二永债成交期限主要分布在 4 至 5 年，其中 2 年内品种折价成交占比最高。分行业看，农林牧渔行业的债 券平均估值价格偏离最大。 风险提示 统计数据偏差或遗漏，高估值个券出现信用风险 敬请参阅最后一页特别声明 1 固定收益动态(动态) 图表1：折价成交跟踪 | | | | | 大幅折价个券成交跟踪 | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 简 称 | 剩余期限 | 估值价格偏 | 估值 ...

- The **Artificial Intelligence Global Asset Allocation Model** applies machine learning to asset allocation problems, utilizing factor investment principles to score and rank assets, ultimately constructing a monthly quantitative equal-weighted strategy for global asset allocation[38][39][41] - The model's suggested weights for January include: government bond index (68.27%), SHFE gold (28.55%), Nasdaq (1.02%), ICE Brent oil (1.24%), and CSI 500 (0.92%)[38][41] - Historical performance from January 2021 to December 2025 shows annualized return of 6.78%, Sharpe ratio of 1.04, maximum drawdown of 6.66%, and excess annualized return of -0.38% compared to the benchmark[39][42] - Year-to-date return for the strategy is 7.18%, while the benchmark return is 18.14%[40][42] - The **Dynamic Macro Event Factor-Based Stock-Bond Rotation Strategy** incorporates macro timing modules and risk budgeting frameworks to generate stock-bond allocation weights for three risk profiles: aggressive, balanced, and conservative[43][44][45] - January stock weights are: aggressive (55.00%), balanced (14.60%), and conservative (0.00%)[43][45] - December macro signals include 60% strength for both economic growth and monetary liquidity dimensions[43][45] - Historical performance from January 2005 to December 2025 shows annualized returns of 20.03% (aggressive), 10.84% (balanced), and 5.88% (conservative), outperforming the benchmark's 8.97%[44][49] - Year-to-date returns are 15.77% (aggressive), 4.23% (balanced), and 0.70% (conservative), compared to the benchmark's 15.95%[44][49] - The **Dividend Style Timing Strategy** leverages 10 indicators from economic growth and monetary liquidity dimensions to construct a timing strategy for dividend indices, showing enhanced stability compared to the CSI Dividend Total Return Index[50][51][53] - January recommended allocation for CSI Dividend is 0%, as most signals did not indicate a bullish outlook[50][54] - Historical performance includes annualized return of 16.18%, maximum drawdown of -21.22%, and Sharpe ratio of 0.93, outperforming the CSI Dividend Total Return Index's annualized return of 11.28% and Sharpe ratio of 0.57[50][53]