文 | 银行螺丝钉 （转载请注明出处） 今天大盘略微下跌，波动不大，截止到收盘，还在3.8星。 沪深300等大盘股微涨，小盘股下跌多一些。 前几天小盘股上涨比较多，这两天回调也大，盈亏同源。 价值风格波动不大，现金流等指数微涨。 成长风格微涨。 港股今天也下跌。 港股红利类品种波动不大，港股科技股下跌多一些。 不过几个组合表现稳定，今天整体上涨。 随着市场上涨，低估的品种也不太多了。 当时还没有现在的组合形态。都是一只只的低估指数基金定投。 从2018年至今，这个指数从底部上涨比较多。 估值上，目前在29倍市盈率上下（30倍到高估）。估值表里也有这个指数的。 这几年也一直持有下来，这个指数从18年5点几星到现在，也上涨了80%左右。 类似的还有中证红利、300价值等。 例如2018年定投的中证红利，到现在也有60%多的收益。 2. 低波动跟红利策略类似，也是每次调仓的时候会有低买高卖的特点，让指数的估值降低一些。 红利类指数还有一些低估品种。 螺丝钉汇总了关于红利指数的估值，供参考，见文章下方图片。 1. 有朋友问，以前定投的500低波动到高估了么？ 500低波动是2018年那轮5点几星，咱们定投比较多的指数 ...

Core Viewpoint - In a low-interest-rate environment, bank stocks are seen as representatives of dividend style investments, offering medium to long-term allocation value due to their high dividend characteristics [1] Group 1: Bank Indices Performance - The main bank indices in A-share and Hong Kong markets include the China Securities Bank Index, the China Securities Hong Kong Bank Index, and the China Securities AH Bank Index [2] - The China Securities Hong Kong Bank Index is expected to significantly outperform the China Securities Bank Index in 2025 [3] - From January 1 to December 31, 2025, the Hong Kong Bank Index recorded a growth of 28.94%, which is substantially higher than the 6.79% increase of the China Securities Bank Index and the 9.60% of the AH Bank Index [4] Group 2: Investment Trends and Fund Flows - In Q3 2025, the Hong Kong banking sector saw significant buying from insurance capital, while the A-share banking sector experienced substantial selling by public funds [10] - The proportion of active equity funds' holdings in A-share banks dropped from 3.91% to 1.49%, with the market value decreasing from 64.1 billion yuan to 30.8 billion yuan [10] - In contrast, insurance capital made 41 significant investments in banks throughout 2025, with 33 of these targeting H-share banks [10] Group 3: Stock Performance and Dividend Yields - The strong performance of the Hong Kong Bank Index is attributed to the notable gains of key stocks such as HSBC Holdings, which rose by 25.53%, and Standard Chartered, which increased by 30.66% [11] - The dividend yield for Hong Kong banks remains above 6%, making them attractive to insurance capital, while A-share banks generally have yields between 3% and 4% [13] - Historical data indicates that buying bank stocks before the Spring Festival has an 80% success rate, with quality joint-stock banks and city commercial banks often yielding excess returns [13] Group 4: Future Outlook - Looking ahead to 2026, insurance capital is expected to remain a significant source of funding, but bank stocks may underperform the broader market in the first quarter due to a growth-oriented market style [15] - The return on tangible equity (ROTE) for Hong Kong banks is projected to slightly decline in 2026, yet the combined return from dividends and buybacks is still expected to exceed 7%, maintaining their investment appeal [15]

2026.01. 15 本文字数：2237，阅读时长大约4分钟 行体系的影响路径更具针对性。 多位市场分析人士指出，结构性货币政策工具利率下调，主要作用于银行负债端，有助于降低银行从 央行获取中长期资金的成本，并未直接压低存贷款基准利率或LPR，对银行净息差的直接冲击相对有 限。从这一角度看，该政策对银行盈利的边际影响偏中性，但在当前息差已接近底部的背景下，具有 一定稳定预期的积极意义。 从中期看，结构性工具"降息+扩容"的组合效应更值得关注。再贷款、再贴现工具重点投向支农支 小、科技创新、民营经济等领域，配合风险分担和考核机制优化，有助于提升银行在重点领域投放的 积极性，同时缓释资本占用和信用风险压力，优化资产结构和资产质量预期。 不过，也有分析人士指出，结构性工具更强调定向支持和结构优化，其对银行盈利改善的效果仍需实 体融资需求回暖配合。短期内，银行股更多体现为"风险下行空间收敛"，而非趋势性上行。 在政策落地背景下，银行板块年初以来表现仍然偏弱。据Wind数据统计，今年以来的9个交易日 中，银行板块有6个交易日下跌，且近期出现一定放量下行迹象。个股方面，浦发银行年内累计跌幅 超过10%，南京银行下跌7. ...

2026年开年，存款市场迎来重要变化。曾作为银行"揽储利器"的大额存单，利率持续下行， 部分中小银行3个月期产品利率首次跌破1%， 正式进入"0 字头"区间。 大额存单短期限产品利率跌破1%，成为近年来首次出现的市场现象。 这一变化，不仅正在改写储户对于"高息存款"的旧有认知，更推动整个理财市场迎来资产配置的重构浪潮。 根据中国货币网公开信息，今年已有超40家银行发布2026年第一期大额存单发行公告。在"期限"和"利率"两个方面都有明显不同于往年的变化。 在期限方面，"短期化"特征明显： 多数银行主打一年期及以下品种，三年期产品发行量锐减，五年期产品近乎绝迹。 在利率方面，"下行态势"突 出， 三年期产品利率普遍不超过2%，一年期利率多不足1.5%，一年期以下产品利率已跌破1%。 值得关注的是，随着开年大额存单市场出现变化，近期，在社交平台上求购高息存单的信息日渐增多，一种由买卖双方私下议价并定向转让的模式随之兴 起。 市场普遍关注， 未来大额存单市场走势如何？ "大额存单利率仍将延续下行趋势，尤其是3个月、1个月等短期限产品利率或进一步逼近'0字头'。"娄飞鹏表示。 以往相较于国有大行在利率上有所优势的中小 ...

来源：第一财经 【时政新闻】 ②欧洲主要股指涨跌不一，截至发稿，英国富时100指数涨0.48%，法国CAC40指数跌0.18%，德国 DAX30指数涨0.06%； 【公司新闻】 ①贝莱德与微软的AI合作项目已筹集125亿美元； ②摩根士丹利：2025年第四季度净营收178.9亿美元； ③高盛：2025年第四季度净营收134.5亿美元； ①英国通信管理局称将继续调查X平台伪造图像事件； ②美驻卡塔尔空军基地警戒级别已降低； ③美国上周初请失业金人数为19.8万人； 【市场动态】 ①美股三大股指期货涨跌不一，截至发稿，道指期货跌0.05%，标普500指数期货涨0.39%，纳指期货涨 0.9%； ④台积电：2025年第四季度净利润同比增35%，该美股盘前涨逾6%。 (本文来自第一财经) ...

Core Insights - Goldman Sachs reported a record stock trading revenue of $4.31 billion in Q4, surpassing market expectations and setting a new Wall Street record [1] - The company's quarterly revenue was $13.45 billion, a 3% year-over-year decline, and $400 million below market expectations; Non-GAAP EPS was $14.01, exceeding expectations by $2.25 [1] - The revenue decline was primarily due to losses in the platform solutions business, linked to the transfer of Apple credit card loans to held-for-sale categories, resulting in a $2.26 billion revenue reduction [1] - However, a reduction in credit loss provisions by $2.48 billion offset the negative impact, and global banking and market revenues significantly increased [1] - For 2025, Goldman Sachs achieved a net revenue of $58.3 billion, the second-highest on record, with potential for a historical record if not for the sale of the Apple credit card portfolio to JPMorgan [1] Financial Performance - The average return on common equity (ROE) for Goldman Sachs in 2025 was 15.0%, with an annualized ROE of 16.0% for Q4 [2] - The book value per common share increased by 6.2% for the year and by 1.1% in Q4, reaching $357.60 at the end of the quarter [2] Dividend and Strategic Focus - The company announced an increase in its dividend to $4.50 per share [3] - Under CEO David Solomon's leadership, Goldman Sachs has refocused on core businesses and improved its trading engine while expanding its investment banking market share [3] - The firm has raised its targets for the asset and wealth management business, aiming to increase the pre-tax profit margin from 24%-26% to 30% and the return rate from 14%-16% to 17%-19% [3] Investment Banking and Expansion - Investment banking fees for the quarter reached $2.58 billion, marking a historical high for Q4 and exceeding analyst expectations [4] - The wealth and asset management division, led by Marc Nachmann, is expanding through acquisitions, including ETF issuer Innovator Capital and venture capital firm Industry Ventures [4] - This division is positioned as a reliable revenue source to balance the volatility of the company's core businesses [4]

得益于交易活动给投行业务带来的丰厚收益，摩根士丹利(MS.US)2025年第四季度业绩超出市场预期。 财报显示，大摩Q4营收同比增长10%至178.9亿美元，好于市场预期的177.5亿美元;净利润为44.0亿美 元，较上年同期的37.1亿美元增长18%;摊薄后每股收益为2.68美元，好于市场预期的2.45美元。 大摩的第四季度业绩与花旗集团(C.US)等华尔街竞争对手的表现相呼应，这些银行也从并购和首次公开 募股(IPO)活动的激增中受益。据悉，大摩是第四季度末几宗大型IPO的联合簿记管理人之一，包括电动 飞机制造商BETA Technologies、税务咨询公司Andersen Group以及医疗用品巨头Medline。该行还在第 四季度的多个标志性交易中担任关键角色，包括为数据基础设施公司Confluent提供咨询、助其达成被 IBM以110亿美元收购的交易。 此外，咨询费用为11.33亿美元，同比增长45%。股票交易收入达36.7亿美元。财富管理业务的净新增资 产达1223亿美元，远高于市场预期。 去年一系列大型交易推动全球并购总额突破5.1万亿美元，对人工智能的热忱以及美联储降息预期鼓舞 了企业首席执 ...

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） ...