一是全面排查摸底，摸清风险底数。统一法人改革启动前，需联合监管部门、第三方机构，按照审慎原 则对全省范围内参与整合的农村中小银行开展全面彻底的资产清查，重点排查房地产贷款、地方融资平 台贷款、大额贷款、涉农贷款、小微企业贷款和逾期90天以内贷款中的不良资产，明确不良资产的金 额、成因、担保方式及处置潜力，建立"一户一档、分类管理"的不良资产台账，杜绝隐性不良"藏 账""瞒账"，确保风险底数清晰可控。 十五五时期是我国全面推进乡村振兴、加快农业农村现代化的关键阶段，也是农村中小银行破解"小、 散、弱"发展困境、实现高质量发展的攻坚期。全省统一法人改革作为农村中小银行整合区域资源、强 化风险抵御能力、提升服务效能的核心路径，本质是通过打破县域法人壁垒、重构治理体系、优化发展 模式，实现从"分散经营"向"集中赋能"的转型。结合当前农村中小银行改革实践与十五五时期政策导 向，全省统一法人改革过程中需重点把握六大关键事项。 一、不良资产处置要彻底：筑牢改革的风险底线 不良资产是农村中小银行全省统一法人改革的"绊脚石"，也是制约改革成效的核心瓶颈。农村中小银行 不良资产区域分布不均、成因复杂多元、处置难度较大，部分县域机 ...

- The report discusses multiple quantitative models for market timing, including short-term, medium-term, and long-term models. These models are constructed based on price-volume, acceleration and trend, momentum, and limit-up/down perspectives. The report emphasizes the importance of combining signals from different models and periods to achieve a balanced strategy[9][11][12] - The short-term models include the "Volume Model" (neutral), "Feature Institutional Model" (neutral), "Feature Volume Model" (bearish), "Smart Algorithm CSI 300 Model" (bullish), and "Smart Algorithm CSI 500 Model" (bearish)[11][70] - Medium-term models include the "Limit-Up/Down Model" (neutral), "Up-Down Return Difference Model" (bullish for some broad-based indices), and "Calendar Effect Model" (bullish)[12][71] - The long-term model is the "Long-Term Momentum Model," which is neutral[72] - Comprehensive models such as the "A-Share Comprehensive Weapon V3 Model" and "A-Share Comprehensive CSI 2000 Model" are neutral[73] - For Hong Kong stocks, the medium-term models include the "Turnover-to-Volatility Model" (bearish), "Hang Seng Index Up-Down Return Difference Model" (neutral), and "Up-Down Return Similarity Model" (bullish)[13][74] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly decline of -0.44%, outperforming the Shanghai Composite Index by 0.83%. Since December 31, 2020, the cumulative return of this pattern is 19.67%, exceeding the Shanghai Composite Index by 2.61%[43][44] - Backtesting results for the "Double-Bottom Pattern" show a weekly decline of -0.88%, outperforming the Shanghai Composite Index by 0.39%. Since December 31, 2020, the cumulative return of this pattern is 23.45%, exceeding the Shanghai Composite Index by 6.39%[43][50]

- The short-term trading volume model is neutral[2][11] - The characteristic institutional model based on the Dragon and Tiger list is neutral[2][11] - The characteristic trading volume model is bearish[2][11] - The intelligent algorithm model for the CSI 300 is bullish[2][11] - The intelligent algorithm model for the CSI 500 is bearish[2][11] - The mid-term limit-up and limit-down model is neutral[2][12] - The mid-term up-down return difference model is bullish for some broad-based indices[2][12] - The mid-term calendar effect model is bullish[2][12] - The long-term momentum model is neutral[2][12] - The comprehensive A-share V3 model is neutral[2][13] - The comprehensive A-share Guozheng 2000 model is neutral[2][13] - The mid-term trading volume to volatility model for Hong Kong stocks is bearish[2][13] - The Hang Seng Index up-down return difference model is neutral[2][13] - The Hang Seng Index up-down return similarity model is bullish[2][13]

Core Insights - The forum on "Cross-Border RMB and Local Currency Transaction Cooperation Potential under the LCT Framework" was held in Indonesia, attracting nearly 300 participants from various sectors, highlighting the growing financial collaboration between China and Indonesia [1][2] Group 1: Economic and Trade Relations - China has been Indonesia's largest trading partner for 12 consecutive years and among the top three sources of investment for 9 years [1] - The cross-border RMB and local currency settlement mechanism is a natural outcome of the comprehensive strategic partnership between China and Indonesia, contributing to the diversification of Indonesia's foreign exchange reserves and enhancing macroeconomic resilience [1][2] Group 2: Local Currency Transaction (LCT) Mechanism - Indonesia has established LCT cooperation mechanisms with multiple countries, including China, with the local currency settlement scale projected to reach $25.66 billion by 2025, with an average monthly user count of 7,568 [2] - The local currency settlement between Indonesia and China is expected to grow significantly from $4.9 billion in 2024 to $13.19 billion in 2025, reflecting strong recognition of the mechanism by enterprises [2] Group 3: Financial Infrastructure and Support - The Indonesian central bank emphasizes cooperation with Chinese financial institutions in local currency settlement and cross-border payment, aiming to enhance transaction efficiency and reduce costs [3] - The LCT mechanism has proven effective in promoting international trade and investment, with local industries in Central Java benefiting significantly from this framework [3] Group 4: Industry and Economic Development - The industrial agglomeration effect in regions like Semarang is accelerating, with increasing demand for financial support in manufacturing and related industries [4] - The China Banking Corporation in Jakarta is a leading player in cross-border RMB clearing, having maintained the top market position for 12 years and supporting cross-border QR payment connectivity [4]

前瞻 | 上市银行 2025 年年报： 业绩增速有望稳中向好，资产质量持续优化 评级： 增持（维持） 执业证书编号：S0740517030004 Email：daizf@zts.com.cn 执业证书编号：S0740519050002 Email：dengmj@zts.com.cn 分析师：杨超伦 执业证书编号：S0740524090004 Email：yangcl@zts.com.cn 3、《详解基金 4Q25 银行持仓：板块 提升 0.04pcts 至 2.08%》2026-01-25 银行 证券研究报告/行业专题报告 2026 年 02 月 07 日 风险提示：经济下滑超预期；研报信息更新不及时；政策落地不及预期。 请务必阅读正文之后的重要声明部分 | 上市公司数 | 42 | | --- | --- | | 行业总市值(亿元) | 146,116.35 | | 行业流通市值(亿元) | 139,898.72 | 1、《1 月金融数据前瞻： 预计新增 贷款 5.1-5.25 万亿元，社融增速为 8.3%》2026-02-07 2026-01-31 分析师：戴志锋 报告摘要 核心观点：1、十一家银行业绩快 ...

一小时驰援！进 出口银行驻村团 队破解德庆贡柑 采摘难题_南方 +_南方plus 近日，德庆县凤 村镇禄村村贡柑 出口示范基地 （下称"基地"） 接到企业大额集 采订单，面临采 摘人力短缺困 境。作为驻镇帮 镇扶村单位，中 国进出口银行驻 村团队快速响 应，一小时内组 建专业采摘队 伍，既保障了贡 柑新鲜交付，也 带动村民就近创 收。 德庆贡柑素 有"中国柑王"的 美誉，其独特的 品质和悠久的历 本赶不上交付期 限，不仅会影响 客户体验，辛苦 种植的贡柑也可 能错过最佳采摘 期。"基地负责 人表示。参与采 摘的村民感慨 道，在家门口就 能就业创收，既 兼顾了家庭，又 增加了收入，真 切感受到了帮扶 中国进出口银行 驻村团队长期扎 根禄村村，坚守 政策性金融使 命，聚焦本地贡 柑高标准示范产 业发展的短板与 难点，精准对接 需求、高效破解 难题，为产业发 展保驾护航。在 此次采摘人力短 缺难题面前，中 国进出口银行广 东省分行公司业 务一处副处长、 驻德庆县凤村镇 禄村村第一书记 杜翊含牵头行 动，驻村团队仅 用一小时便确定 解决方案，成功 对接本村摘果领 队，快速组织起 一支几十人的摘 果队伍，同步推 进采摘 ...

1. Report Industry Investment Rating No relevant content provided. 2. Core Views of the Report - Overseas asset fluctuations have been repaired. Although the fourth - quarter reports of tech giants show that cloud - computing revenue and 2026 capex expenditure guidance exceed expectations, market divergence is rising, and the previous structured market is undergoing "destructuring". At least in the first half of 2026, tech growth will maintain its momentum due to factors such as the decrease in the expectation of the Fed's marginal monetary policy easing and the potential IPO of OpenAI in the third or fourth quarter of 2026 [1][37]. - In the domestic equity market, defensive sectors like food and beverage performed well last week, followed by pro - cyclical sectors, while tech growth sectors generally showed high volatility. For convertible bonds, due to the priority of winning rate over odds, high - volatility targets with tech themes and mostly being new - issue targets make it difficult to control drawdowns and increase the difficulty of speculation. Before the holiday, it is recommended to balance risks. High - position funds should actively switch from high - to low - risk assets, and low - position funds should seize the opportunity to invest in targets with clear performance inflection points and high visibility of upward trends in 2026 [1][37][39]. 3. Summary by Relevant Catalogs 3.1. Weekly Market Review 3.1.1. Overall Decline in the Equity Market - From February 2nd to February 6th, the equity market declined overall. The Shanghai Composite Index fell 1.27% to 4065.58 points, the Shenzhen Component Index dropped 2.11% to 13906.73 points, the ChiNext Index decreased 3.28% to 3236.46 points, and the CSI 300 fell 1.33% to 4643.60 points. The average daily trading volume of the two markets decreased by 21.36% week - on - week to 23879.96 billion yuan [6][10]. - Among the 31 Shenwan primary industries, 16 industries closed up, with 3 industries rising more than 2%. Food and beverage, beauty care, power equipment, transportation, and banking led the gains, rising 4.31%, 3.69%, 2.20%, 1.90%, and 1.70% respectively. Non - ferrous metals, communication, electronics, steel, and computer led the losses, with declines of - 8.51%, - 6.95%, - 5.23%, - 3.35%, and - 3.27% respectively [16]. 3.1.2. Overall Rise in the Convertible Bond Market - From February 2nd to February 6th, the CSI Convertible Bond Index rose 0.05% to 520.79 points. Among the 29 Shenwan primary industries, 22 industries closed up, with 2 industries rising more than 2%. Social services, power equipment, transportation, national defense and military industry, and petroleum and petrochemicals led the gains, rising 4.95%, 2.95%, 1.85%, 1.76%, and 1.42% respectively. Computer, electronics, communication, non - bank finance, and non - ferrous metals led the losses, falling 4.85%, 3.06%, 2.22%, 2.13%, and 1.94% respectively [19]. - The average daily trading volume of the convertible bond market was 902.09 billion yuan, a significant decrease of 30.87 billion yuan, with a month - on - month change of - 3.31%. The top ten convertible bonds in terms of trading volume were Shangtai Convertible Bond, Naipu Convertible Bond 02, Dongshi Convertible Bond, Yanpai Convertible Bond, Shuangliang Convertible Bond, Jize Convertible Bond, Yongji Convertible Bond, Jiemei Convertible Bond, Tairui Convertible Bond, and Jialian Convertible Bond. The average trading volume of the top ten convertible bonds reached 116.84 billion yuan, and the trading volume of the top - ranked bond was 335.59 billion yuan [19]. - Approximately 54.71% of individual convertible bonds rose, about 21.73% of them had a gain in the range of 0 - 1%, and 17.54% of them had a gain of more than 2% [19]. - The overall market conversion premium rate increased, with the average daily conversion premium rate this week being 44.31%, a 1.56 - percentage - point increase from last week. By price range, except for the convertible bonds in the price range below 90 yuan, the average daily conversion premium rate quantiles of convertible bonds in other price ranges narrowed. The narrowing amplitude was the largest in the 110 - 120 yuan price range, reaching 30.31 percentage points. By parity range, except for the convertible bonds in the parity range below 90 yuan, the average daily conversion premium rates of convertible bonds in other parity ranges narrowed, with the largest narrowing amplitude of 15.41 percentage points in the 110 - 120 yuan parity range [24]. - In terms of the premium rate changes of each industry, the conversion premium rates of 12 industries widened, with 3 industries having a widening amplitude of more than 2 percentage points. Social services, household appliances, food and beverage, media, and textile and apparel led the widening, with amplitudes of 9.03, 3.54, 2.90, 1.59, and 1.27 percentage points respectively. Building materials, communication, agriculture, forestry, animal husbandry and fishery, non - bank finance, and electronics led the narrowing, with amplitudes of - 14.89, - 14.64, - 5.78, - 4.62, and - 3.81 percentage points respectively [28]. - In terms of conversion parity, the parity of 4 industries increased, with 1 industry having a widening amplitude of more than 2%. Communication, transportation, banking, and social services led the widening, with amplitudes of 16.51%, 1.19%, 0.61%, and 0.13% respectively. Non - bank finance, non - ferrous metals, building materials, automobiles, and electronics led the narrowing, with amplitudes of - 29.31%, - 15.94%, - 13.22%, - 11.74%, and - 10.64% respectively [30]. 3.1.3. Comparison of Stock and Bond Market Sentiments - From February 2nd to February 6th, the weekly weighted average change of the convertible bond market was negative, and the median was positive. The weekly weighted average change of the underlying stock market was positive, and the median was negative. In terms of trading volume, the trading volume of the convertible bond market decreased by 4.05% month - on - month and was at the 82.40% quantile level since 2022. The trading volume of the underlying stock market decreased by 22.67% month - on - month and was at the 88.20% quantile level since 2022. Both the underlying stocks and convertible bonds had a significant reduction in trading volume, and the underlying stock trading volume was at a higher quantile level. In terms of the proportion of rising and falling stocks and bonds, about 60.00% of convertible bonds closed up, and about 43.85% of underlying stocks closed up. About 64.62% of convertible bonds had a larger change than the underlying stocks. In general, the trading sentiment of the convertible bond market was better this week [34]. 3.2. Outlook and Investment Strategy - Overseas asset fluctuations have been repaired. Although the fourth - quarter reports of tech giants show that cloud - computing revenue and 2026 capex expenditure guidance exceed expectations, market divergence is rising, and the previous structured market is undergoing "destructuring". At least in the first half of 2026, tech growth will maintain its momentum due to factors such as the decrease in the expectation of the Fed's marginal monetary policy easing and the potential IPO of OpenAI in the third or fourth quarter of 2026 [1][37]. - In the domestic equity market, defensive sectors like food and beverage performed well last week, followed by pro - cyclical sectors, while tech growth sectors generally showed high volatility. For convertible bonds, due to the priority of winning rate over odds, high - volatility targets with tech themes and mostly being new - issue targets make it difficult to control drawdowns and increase the difficulty of speculation. Before the holiday, it is recommended to balance risks. High - position funds should actively switch from high - to low - risk assets, and low - position funds should seize the opportunity to invest in targets with clear performance inflection points and high visibility of upward trends in 2026 [1][37][39]. - Specific targets recommended for attention include Bo 25, Baolong, Saite, Huitian, Suli, Jianlong, Tairui, Yongjin, Zhongte, Yongxi, Dinglong, Li'ang, Shenglan Convertible Bond 02, Chaosheng, Lihe, Huachen, Tiannai Convertible Bond, etc. [1][39]. - The top ten high - rated, medium - low - priced convertible bonds with the greatest potential for parity premium rate repair next week are Liqun Convertible Bond, Bengang Convertible Bond, Lutai Convertible Bond, Lianchuang Convertible Bond, Xingye Convertible Bond, Yingfeng Convertible Bond, Guotou Convertible Bond, Nenghua Convertible Bond, Qingnong Convertible Bond, and Ziyin Convertible Bond [1][39].

Quantitative Models and Factors Summary Quantitative Models and Construction Methods Model Name: Volume Timing Model - **Model Construction Idea**: The model uses volume signals to determine market timing[12] - **Model Construction Process**: - The model evaluates the volume timing signals for major indices as of February 6, 2026, and maintains a cautious view[24] - **Model Evaluation**: The model is currently signaling a cautious outlook for all major indices[24] Model Name: Momentum Sentiment Indicator - **Model Construction Idea**: The model uses the number of stocks with positive returns within an index to gauge market sentiment[24] - **Model Construction Process**: - Calculate the proportion of stocks in the CSI 300 index with positive returns over the past N days - The formula is: $ \text{CSI 300 Index N-day Upward Stock Proportion} = \frac{\text{Number of stocks with positive returns in the past N days}}{\text{Total number of stocks in the index}} $[24] - **Model Evaluation**: The indicator can quickly capture upward opportunities but may miss out on gains during sustained market exuberance and has limitations in predicting downturns[25] Model Name: Moving Average Sentiment Indicator - **Model Construction Idea**: The model uses the eight moving average system to determine the trend state of the CSI 300 index[32] - **Model Construction Process**: - Calculate the eight moving average values for the CSI 300 index closing prices with parameters 8, 13, 21, 34, 55, 89, 144, 233 - Assign values to the moving average indicator based on the moving average interval values - The formula is: $ \text{Indicator Value} = \begin{cases} -1 & \text{if interval value is 1/2/3} \\ 0 & \text{if interval value is 4/5/6} \\ 1 & \text{if interval value is 7/8/9} \end{cases} $[32] - **Model Evaluation**: The recent CSI 300 index is in a non-prosperous sentiment interval[32] Model Backtesting Results Volume Timing Model - **Signal**: Cautious for all major indices[24] Momentum Sentiment Indicator - **Current Value**: The indicator is above 60%, indicating high market sentiment[25] Moving Average Sentiment Indicator - **Current Value**: The CSI 300 index is in a non-prosperous sentiment interval[32] Quantitative Factors and Construction Methods Factor Name: Cross-sectional Volatility - **Factor Construction Idea**: The factor measures the cross-sectional volatility of index constituent stocks to assess the Alpha environment[36] - **Factor Construction Process**: - Calculate the cross-sectional volatility for the CSI 300, CSI 500, and CSI 1000 index constituent stocks - The formula is: $ \text{Cross-sectional Volatility} = \sqrt{\frac{1}{N-1} \sum_{i=1}^{N} (R_i - \bar{R})^2} $ where $ R_i $ is the return of stock i, and $ \bar{R} $ is the average return[37] - **Factor Evaluation**: The short-term Alpha environment has deteriorated, but the quarterly view shows a good Alpha environment for the CSI 300 and CSI 1000 indices[36] Factor Name: Time-series Volatility - **Factor Construction Idea**: The factor measures the time-series volatility of index constituent stocks to assess the Alpha environment[37] - **Factor Construction Process**: - Calculate the time-series volatility for the CSI 300, CSI 500, and CSI 1000 index constituent stocks - The formula is: $ \text{Time-series Volatility} = \sqrt{\frac{1}{T-1} \sum_{t=1}^{T} (R_t - \bar{R})^2} $ where $ R_t $ is the return at time t, and $ \bar{R} $ is the average return[40] - **Factor Evaluation**: The recent week shows an improvement in the Alpha environment for all indices[37] Factor Backtesting Results Cross-sectional Volatility - **CSI 300**: - Last quarter average: 2.17% - Last quarter percentile (2 years): 70.99% - Last quarter percentile (1 year): 74.07% - Last quarter percentile (6 months): 65.64%[37] - **CSI 500**: - Last quarter average: 2.48% - Last quarter percentile (2 years): 48.41% - Last quarter percentile (1 year): 53.97% - Last quarter percentile (6 months): 56.35%[37] - **CSI 1000**: - Last quarter average: 2.63% - Last quarter percentile (2 years): 66.53% - Last quarter percentile (1 year): 68.92% - Last quarter percentile (6 months): 66.14%[37] Time-series Volatility - **CSI 300**: - Last quarter average: 0.96% - Last quarter percentile (2 years): 58.02% - Last quarter percentile (1 year): 60.91% - Last quarter percentile (6 months): 47.94%[40] - **CSI 500**: - Last quarter average: 1.27% - Last quarter percentile (2 years): 50.00% - Last quarter percentile (1 year): 57.94% - Last quarter percentile (6 months): 60.32%[40] - **CSI 1000**: - Last quarter average: 1.22% - Last quarter percentile (2 years): 63.35% - Last quarter percentile (1 year): 71.31% - Last quarter percentile (6 months): 66.93%[40]

Global Market Overview - Global markets have shown weakness over the past two weeks, influenced by geopolitical tensions and international market deleveraging[2] - The Iranian situation has escalated tensions in the Middle East, leading to rising oil prices[2] - The US dollar has weakened, with non-US currencies generally appreciating[2] Stock Market Performance - Major indices have experienced declines: the Shanghai Composite Index fell by 1.27%, while the Hang Seng Index dropped by 3.02%[4] - The Nasdaq Composite Index decreased by 1.84%, contrasting with the Dow Jones Industrial Average, which rose by 2.50%[4] - The ChiNext Index saw a significant decline of 3.28% over the two-week period[4] Valuation Metrics - The Price-to-Earnings (PE) ratio for the Shanghai Composite Index is at 90.6, indicating a high valuation compared to historical averages[6] - The Hang Seng Technology Index has a low PE ratio of 46.9, suggesting potential undervaluation relative to other indices[6] - The Price-to-Book (PB) ratio for the Shanghai Composite Index stands at 91.4, reflecting a high valuation compared to its historical performance[8] Bond Market Insights - The yield on 10-year US Treasury bonds is at 4.20%, while China's 10-year government bond yield is significantly lower at 1.80%[12] - The probability of a Federal Reserve rate cut in March-April is below 50%, with expectations for two rate cuts in June and September 2026[15] Commodity Market Trends - Precious metals have seen significant declines, with silver prices dropping by 8.77% recently, while gold prices have remained stable[37] - Oil prices have shown strength, with WTI crude oil priced at $63.55 per barrel, reflecting a robust performance compared to other commodities[36] - Agricultural products have shown mixed results, with soybeans experiencing a notable increase of 4.1%[36]

Core Viewpoint - The recent volatility in gold prices has prompted China Gold Group to adjust its gold buyback business rules, aiming to mitigate operational risks and ensure orderly operations [1][5]. Group 1: Business Adjustments - Starting from February 7, China Gold will suspend gold buyback services on non-trading days, including weekends and public holidays [2]. - The company will implement limit management on buyback transactions, including daily limits for individual customers and total limits for single transactions, along with an appointment system [3]. Group 2: Rationale Behind Adjustments - The adjustments aim to align with market pricing mechanisms, as gold prices are based on real-time quotes from the exchange, avoiding pricing disputes and operational risks on non-trading days [5]. - The changes are designed to control the company's risk exposure during periods of significant price fluctuations, preventing potential losses from acquiring physical gold without market price references [5]. - The adjustments also seek to enhance service consistency by standardizing buyback rules across online and offline channels, improving operational efficiency and reducing consumer misunderstandings [5]. Group 3: Industry Context - Other leading brands, such as Caibai Co., have also announced similar adjustments to their gold buyback services, reflecting a broader trend in the industry [7]. - Major commercial banks have made corresponding adjustments to their gold accumulation services, indicating a collective response to the current market conditions [7].