Quantitative Models and Construction Methods 1. Model Name: "百元转股溢价率" (Premium Rate per 100 Yuan Conversion) - **Model Construction Idea**: This model compares the valuation of convertible bonds and their underlying stocks by calculating a time-series comparable valuation metric, "百元转股溢价率" (Premium Rate per 100 Yuan Conversion), and evaluates the relative allocation value using rolling historical percentiles[3][14] - **Model Construction Process**: - Fit the relationship curve between the conversion premium rate and conversion value in the cross-sectional space at each time point - Substitute a conversion value of 100 into the fitted formula to obtain the "百元转股溢价率" - Formula: $$ y_{i} = \alpha_{0} + \alpha_{1} \cdot \frac{1}{x_{i}} + \epsilon_{i} $$ where \( y_{i} \) is the conversion premium rate of the \( i \)-th bond, \( x_{i} \) is the conversion value of the \( i \)-th bond, and \( \epsilon_{i} \) is the error term[46][47] - **Model Evaluation**: The rolling three-year and five-year percentiles of this metric are at 99.30% and 99.60%, respectively, indicating that convertible bonds are relatively expensive compared to their underlying stocks[3][14] 2. Model Name: "修正 YTM – 信用债 YTM" (Adjusted YTM Minus Credit Bond YTM) - **Model Construction Idea**: This model evaluates the relative allocation value between debt-heavy convertible bonds and credit bonds by isolating the impact of conversion terms on the convertible bond's yield-to-maturity (YTM)[4][14] - **Model Construction Process**: - Adjust the YTM of debt-heavy convertible bonds using the following formula: $$ \text{Adjusted YTM} = \text{Convertible Bond YTM} \times (1 - \text{Conversion Probability}) + \text{Expected Annualized Return from Conversion} \times \text{Conversion Probability} $$ - The conversion probability is calculated using the Black-Scholes (BS) model, incorporating stock price, strike price, stock volatility, remaining term, and discount rate - The difference between the adjusted YTM and the YTM of credit bonds of the same rating and maturity is calculated for each bond, and the median value is taken as the metric: $$ \text{"修正 YTM – 信用债 YTM" Median} = \text{median}\{X_1, X_2, ..., X_n\} $$ where \( X_i \) represents the difference for the \( i \)-th bond[48] - **Model Evaluation**: The current median value of this metric is -5.00%, indicating that the overall allocation cost-effectiveness of debt-heavy convertible bonds is relatively low[4][14] --- Model Backtesting Results 1. "百元转股溢价率" Model - Rolling three-year percentile: 99.30%[3][14] - Rolling five-year percentile: 99.60%[3][14] 2. "修正 YTM – 信用债 YTM" Model - Median value: -5.00%[4][14] --- Quantitative Factors and Construction Methods 1. Factor Name: 转股溢价率偏离度 (Conversion Premium Deviation) - **Factor Construction Idea**: Measures the deviation of the conversion premium rate from its fitted value, enabling comparability across different parities[20] - **Factor Construction Process**: $$ \text{Conversion Premium Deviation} = \text{Conversion Premium Rate} - \text{Fitted Conversion Premium Rate} $$ The fitted value is determined by the relationship curve between conversion premium rate and conversion value, as described in the "百元转股溢价率" model[20][46] - **Factor Evaluation**: The quality of the fit depends on the number of convertible bonds, and this factor is effective in identifying valuation deviations[20] 2. Factor Name: 理论价值偏离度 (Theoretical Value Deviation, Monte Carlo Model) - **Factor Construction Idea**: Measures the price expectation difference by comparing the closing price of a convertible bond to its theoretical value, which is calculated using Monte Carlo simulation[20] - **Factor Construction Process**: $$ \text{Theoretical Value Deviation} = \frac{\text{Convertible Bond Closing Price}}{\text{Theoretical Value}} - 1 $$ The theoretical value is derived by simulating 10,000 paths for each time point, considering conversion, redemption, downward revision, and resale terms, and using the discount rate of bonds with the same credit rating and maturity[20] - **Factor Evaluation**: This factor fully accounts for the complex terms of convertible bonds and is particularly effective in identifying valuation discrepancies[20] 3. Factor Name: 转债综合估值因子 (Comprehensive Convertible Bond Valuation Factor) - **Factor Construction Idea**: Combines the rankings of the above two factors to create a comprehensive valuation metric for convertible bonds[20] - **Factor Construction Process**: $$ \text{Comprehensive Convertible Bond Valuation Factor} = \text{Rank}(\text{Conversion Premium Deviation}) + \text{Rank}(\text{Theoretical Value Deviation}) $$ This factor is used to construct low-valuation indices for different convertible bond styles (equity-heavy, balanced, and debt-heavy)[20][21] - **Factor Evaluation**: The comprehensive factor performs well across all styles, while the theoretical value deviation factor is particularly effective for equity-heavy convertible bonds[19][20] --- Factor Backtesting Results 1. Conversion Premium Deviation Factor - No specific backtesting results provided 2. Theoretical Value Deviation Factor - No specific backtesting results provided 3. Comprehensive Convertible Bond Valuation Factor - **Equity-heavy Convertible Bond Low-Valuation Index**: - Annualized return: 26.97% - Annualized volatility: 20.65% - Maximum drawdown: 22.94% - IR: 1.31 - Calmar ratio: 1.18[23] - **Balanced Convertible Bond Low-Valuation Index**: - Annualized return: 16.04% - Annualized volatility: 11.99% - Maximum drawdown: 15.95% - IR: 1.34 - Calmar ratio: 1.01[23] - **Debt-heavy Convertible Bond Low-Valuation Index**: - Annualized return: 12.43% - Annualized volatility: 9.80% - Maximum drawdown: 17.78% - IR: 1.27 - Calmar ratio: 0.70[23] --- Style Rotation Model and Construction Methods 1. Model Name: 转债风格轮动 (Convertible Bond Style Rotation) - **Model Construction Idea**: Captures market sentiment using momentum and volatility deviation factors to rotate among low-valuation style indices (equity-heavy, balanced, and debt-heavy)[27] - **Model Construction Process**: - Calculate the following sentiment capture metric: $$ \text{Sentiment Capture Metric} = \text{Rank}(\text{20-day Momentum}) + \text{Rank}(\text{Volatility Deviation}) $$ - Rank the indices based on this metric and allocate weights accordingly. If all three styles are selected, allocate 100% to the balanced style[27][28] - Rebalance every two weeks[27] - **Model Evaluation**: The style rotation model effectively captures market sentiment and enhances returns compared to equal-weight indices[27][32] --- Style Rotation Model Backtesting Results 1. Convertible Bond Style Rotation Model - Annualized return: 25.65% - Annualized volatility: 16.82% - Maximum drawdown: 15.89% - IR: 1.52 - Calmar ratio: 1.61[32]

金融监管总局在1月15日召开的2026年监管工作会议上，统筹安排了5项今年的重点任务。其中，中小金 融机构风险化解仍位列各项任务首位，会议指出，要着力处置存量风险，坚决遏制增量风险，牢牢守住 不"爆雷"底线。 会议指出，过去一年，金融监管总局系统上下围绕防风险、强监管、促高质量发展工作主线，守住不发 生系统性金融风险底线，各项工作取得积极进展。其中，在有力有序防范化解重点风险方面，中小金融 机构改革化险取得重大进展，城市房地产融资协调机制扩围增效，积极支持融资平台经营性金融债务接 续置换重组。防非打非工作机制实现省市县三级全覆盖。同时，强监管严监管氛围逐步形成。在行业改 革转型方面，持续推进保险业"报行合一"和预定利率调整，加力推动银行业提质增效；支持金融机构多 渠道补充资本。此外，出台了超长期贷款相关政策服务"两重"建设、支持小微企业融资协调工作机制走 深走实、科技金融"四项试点"稳步推进、保险经济减震器和社会稳定器功能进一步发挥，由此精准有效 支持了经济稳中向好。 对于今年监管工作的重点任务，会议首先强调，要有力有序有效推进中小金融机构风险化解。着力处置 存量风险，坚决遏制增量风险，牢牢守住不"爆雷"底线 ...

证券日报网讯 1月15日，苏交科发布公告称，公司使用闲置自有资金9000万元购买华夏银行"人民币单 位结构性存款DWJCNJ26055"，期限364天，预期年化收益率0.30%至2.35%，风险等级R1；至此尚未赎 回理财余额66000万元，未超120000万元授权额度。 （文章来源：证券日报） ...

证券日报网1月15日讯 ，紫金银行在接受调研者提问时表示，2026年，我行仍将坚守服务三农、服务中 小、服务城乡的市场定位，以服务实体经济为根本宗旨，以深化改革创新为强大动力，聚焦主责主业， 持续深化做小做散机制，加强重点领域金融支持，提升风险防控能力，努力实现高质量发展。 （文章来源：证券日报） ...

证券日报网1月15日讯 ，紫金银行在接受调研者提问时表示，我行坚持服务实体经济、坚持做小做散， 围绕金融"五篇大文章"，积极稳妥加大信贷投放力度，持续优化普惠金融产品与服务，为地方经济和社 会发展提供优质的金融服务。 （文章来源：证券日报） ...

智通财经APP获悉，美国银行业巨头第四季度利润大幅增长，这主要得益于借款人需求的持续增长，表 明美国经济形势良好，也预示着贷款机构未来的盈利前景乐观。 美国银行(BAC.US)周三公布的数据显示，其平均贷款额同比增长8%，净利息收入(即贷款收入与存款支 出之间的差额)飙升至创纪录的159亿美元。其竞争对手摩根大通(JPM.US)的平均贷款额增长了9%。投 资者普遍认为，贷款增长是银行业务的积极指标，也是经济整体强劲的体现。 美国银行首席财务官Alastair Borthwick在电话会议上告诉记者："我们看到所有消费贷款类别都实现了 增长。这在第四季度对我们有所帮助，但总体而言，2025年的故事更多地围绕商业借贷展开……我们身 处经济增长环境中的客户持续投资以支持其业务发展。" 美国经济展现韧性 Borthwick表示，美国银行预计2026年贷款增长率将达到中等个位数百分比。尽管特朗普实施了大规模 进口关税，但美国经济和美国消费者依然保持韧性，这部分得益于人工智能的蓬勃发展和美联储的降 息。市场预计今年还将有两次降息。 标普全球市场情报公司的分析师在周二发布的一份报告中写道："他们对2026年经济持续增长的势 ...

Core Viewpoint - The article discusses the recent monetary policy adjustments, particularly structural interest rate cuts, and their implications for the financial market, emphasizing a cautious approach to overall interest rate reductions while focusing on targeted support for specific sectors like technology and small enterprises [4][5][6]. Group 1: Monetary Policy Insights - The recent structural interest rate cuts aim to direct funds towards technology and small enterprises rather than allowing capital to circulate in financial markets [4]. - The decision to lower the rates on structural monetary tools and increase quotas for technology re-loans indicates a continuous policy approach rather than a shift towards broad interest rate cuts [5]. - The central bank is cautious about further lowering the OMO rate, prioritizing structural monetary policy and fiscal measures, with a preference for maintaining bank interest margins [6]. Group 2: Market Reactions - Following the announcement of new financing regulations, the financing balance increased by over 150 billion, indicating strong market activity despite regulatory changes [10]. - A significant drop in daily trading volume was observed, with a decrease of over 1 trillion, marking one of the largest single-day volume reductions historically [12]. - The A-share market showed a mixed performance, with a median decline of only 0.4% across over 5,000 stocks, indicating a selective market reaction [21]. Group 3: Sector Performance - The commercial aerospace sector experienced a sharp decline, with leading stocks like China Satellite facing significant losses, highlighting the volatility in high-valuation sectors [24]. - Despite overall market cooling, sectors such as AI hardware and semiconductor equipment showed resilience, with notable gains following positive earnings reports from major companies like TSMC [27]. Group 4: Investment Strategies - The article suggests that the current low-interest-rate environment in China continues to create opportunities for structural investments in the stock market, despite limited room for significant interest rate reductions [7]. - The analysis of foreign capital flows indicates a strategic approach, with foreign investors adjusting their positions based on fundamental valuations, as seen in the case of Industrial and Commercial Bank of China [42].

图里的橙色定级分区数据，把资金参与状态分成了四个等级。一级区代表资金非常活跃，正在积极参与交易；二级区就是资金活跃度降低，进入锁仓状态， 不再频繁操作，只是静静等待合适的时机；三级区和四级区则代表资金活跃度几乎消失，偶尔参与也不会形成持续的推动力量。 二、活跃与锁仓的交替信号 近期监管调整了融资买入的保证金最低比例，从之前的80%提高到100%，只针对新开的融资合约。有人说核心目的是防范过度杠杆化引发的市场波动风 险。同时，市场融资余额已连续多日增加，目前站在2.6万亿元之上，但两融交易额占A股成交额的比例还没到2015年的水平。面对这样的市场变化，普通投 资者最关心的还是怎么看清背后的资金动向，避免被表面的波动影响判断。 | | | 1月14日融资净买入居前个股 | | | | | --- | --- | --- | --- | --- | --- | | 代码 | 简称 | 不承用品 | 最新融资余额 占流通市值 | | 行业 | | | | (亿元) | (亿元) | 比例 (%) | | | 600900 | 长江电力 | 13.83 | 115.22 | 1.79 | 公用事业 | | 600089 ...

本文由 AI算法生成，仅作参考，不涉投资建议，使用风险自担 【1月15日，三菱日联分析师称日本当局难靠干预支撑日元】三菱日联分析师表示，日本当局或难以用 潜在干预措施支撑日元。短期内，市场对财政风险的担忧难减弱，且美联储预计在新主席上任前维持利 率不变。日本财政大臣在日元近期大跌后暗示可能干预，日元跌幅主要受首相高市早苗计划提前大选影 响。投资者押注，若高市早苗权力巩固，可能推动进一步财政刺激，降低加息可能性。 ...

"盼望'老破小'旧貌换新颜，更多的老旧楼原拆原建，老街坊们都能过上舒心的日子"。半个月前，一位 网名叫"北京老兵"的网友在看到北京市西城区三里河一区28号楼"原拆原建"老楼完成封顶消息后，随即 这样留言道。 "北京老兵"的愿望也许会加速实现。2026年1月15日，记者从北京金融监管局获悉，在国家金融监督管 理总局大力支持下，该局争取到全国唯一的金融支持危旧楼改建试点政策，针对居民融资痛点，指导试 点银行在全国首创"个人住房改建贷款"专属产品，并创新"带押改建"模式。 项目实施主体签订改建协议。二是信用状况良好，具有还款能力。三是按合同要求缴纳相应首付款。四 是办理所改建住房的抵押登记。五是其他需要配合的相关材料。 北京银行工作人员告诉记者，未来该行将在监管部门的持续指导下，主动对接全市范围内的危旧楼改建 项目，全面复制推广试点成功经验，以更精准、高效的金融服务助力更多居民实现"安居梦"，为首都城 市更新与民生改善事业注入金融动能。 试点项目中已在原房屋上设立抵押的存量贷款银行，自动成为带押改建试点银行。具体到三里河一区项 目，共涉及北京银行、中国建设银行北京市分行、中国工商银行北京市分行等7家试点银行。 而对 ...