朋友们早上好，我是帮主郑重。周二，大洋彼岸的美股市场，悄悄上演了一出"冰与火"的戏码。一边是 道琼斯指数连续三天创下历史新高，站上了5万点大关；另一边，却是标普500指数震荡走低，纳指也跌 了近0.6%。同一个市场，为何出现如此分裂的走势？这背后传递的信号，对我们节后的投资又有何启 发？ 首先，给道指"火上浇油"的，是市场对美联储政策的新预期。有策略师开始讨论，在新主席可能上任的 背景下，美联储今年有降息三次的空间，这比当前市场预期的更加宽松。这种预期一旦升温，首先利好 的就是金融、工业等对利率敏感的传统价值股，它们恰恰是道指的核心成分。这告诉我们，市场的目光 已经开始超越近期的数据波动，博弈更长远的政策路径。 那么，这种"分裂"意味着什么？对我们又有什么实操的借鉴呢？ 第一，它明确提示我们，美股内部的风格轮动正在剧烈发生。资金从一部分过热或面临冲击的科技、金 融板块，流向更稳健的价值板块和对AI颠覆"免疫"的领域。这绝不是牛市终结的信号，而是一次健康的 内部结构调整。它告诉我们，即便在指数创新高的牛市里，"精挑细选"也比"闭眼买入"重要得多。 第二，它把所有人的目光都聚焦在了接下来几天的关键经济数据上，特别 ...

2026 年 02 月 10 日 金 融 工 程 高维宏观周期驱动风格、行业月报(2026/2):经济 景气下行、通胀细分项下行看好小盘红利风格 投资要点： 传统宏观因子、宏观周期的高维度体系构建 金 融 工 程 定 期 报 告 宏观因子变量的构建：将宏观指数分别对宽基指数、代理宏观变 量做回归，选取 t 值显著的细分宏观变量，用过去一年标准差倒数加权 构建宏观因子变量。采用单边 HP 滤波器对宏观经济数据进行调整，消 除短期波动对长期趋势判断的影响。基于滤波变量，分别用因子动量 划分宏观趋势（上行、下行）和用时序百分位划分宏观状态（高、中、 低位）。 宏观因子升维的必要性：宏观因子 A 对宽基、风格和行业的价格 传导在 A 的不同边际变化不一致，且宏观因子 A 在宏观因子 B 的不同 状态下驱动宽基、风格和行业的收益方向也不同。同一状态及其边际 变化所对应的周期混乱，我们需要将宏观变量的边际与状态结合，综 合考虑宏观变量的变化趋势和所处的时序排位。 多信号驱动下的指数择时、风格轮动 小盘全指择时：在库存处于中等向上水平时预测值最高，因此推 荐配置中证全指。 2012 年 1 月末起至 2026 年 1 月 ...

1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints of the Report - The report focuses on constructing multiple ETF portfolios, including macro industry, macro + momentum industry, core - satellite, and trinity style rotation portfolios, aiming to find better investment opportunities by combining macro factors, momentum factors, and style rotation [4][5]. - Different industries have different sensitivities to economic, liquidity, and credit factors. For example, traditional cycle industries are sensitive to the economy, TMT is sensitive to liquidity, and consumption is sensitive to credit [4]. 3. Summary According to Relevant Catalogs 3.1 ETF Portfolio Construction Methods 3.1.1 Based on Macro Method - Calculate macro - sensitivities of broad - based, industry - themed, and Smart Beta ETFs based on economic, liquidity, and credit variables. Combine with momentum indicators for complementary analysis [4]. - Traditional cycle industries are suitable for economic up - periods, TMT for weak - economy but loose - liquidity periods, and consumption for credit - expansion periods. State - owned enterprises and ESG - related themes have low sensitivities to liquidity and credit [4]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) and adjust positions monthly [4]. 3.1.2 Trinity Style Rotation ETF Portfolio Construction - Build a medium - to - long - term style rotation model centered on macro - liquidity, and compare it with the CSI 300 index [5]. - Construct three types of models (growth/value rotation, market - cap, and quality models) by screening macro, fundamental, and market - sentiment factors. The model has 8 style - preference results [5]. - Select ETFs with high exposure to the target style, control industry exposure, and set allocation limits to get the final ETF allocation model [5]. 3.2 Macro Industry Portfolio - Select industry - themed ETFs with over 1 - year establishment and over 200 million current scale. Calculate sensitivity scores of economic, liquidity, and credit factors monthly, adjust scores according to the latest indicators, and sum them up. If liquidity and credit deviate significantly, remove the liquidity score. Select the top 6 industry - themed indices and corresponding largest - scale ETFs for equal - weight allocation [6][7]. - Currently, with falling economic leading indicators, loose liquidity, and tightened credit, the portfolio is biased towards TMT and consumption. The February positions are shown in Table 1 [8]. - The portfolio has large fluctuations and outperformed the benchmark significantly in January [11]. 3.3 Macro + Momentum Industry Portfolio - Combine macro and momentum methods to address the left - side nature of macro - based strategies (low win - rate but high odds). Use clustering to group industry - themed indices and select the highest - rising product in each group in the past 6 months for equal - weight allocation [12]. - The momentum - selected industries still have a high proportion of cyclical industries. The February positions are shown in Table 3 [16]. - The portfolio has performed well this year and outperformed the CSI 300 significantly in January [17]. 3.4 Core - Satellite Portfolio - Design a "core - satellite" portfolio with the CSI 300 as the core to address the high volatility and rapid industry rotation of industry - themed ETFs [19]. - Calculate macro - sensitivities for broad - based, industry - themed, and Smart Beta ETFs, construct three stock portfolios, and weight them at 50%, 30%, and 20% respectively [19]. - The current allocation of broad - based ETFs is biased towards the Sci - tech Innovation Board and the ChiNext. The portfolio has performed stably, outperforming the benchmark in most months except December, and had significant excess returns in January 2026 [23][24]. 3.5 Trinity Style Rotation ETF Portfolio - The model currently favors the small - cap growth - high - quality segment. The factor exposures and historical performance are shown in Table 7 [26]. - The February positions are shown in Table 9 [31]. - The portfolio has achieved certain excess returns, especially in some months such as August 2025 and January 2026 [29].

文 | 银行螺丝钉 （转载请注明出处） 时加 正 2015年牛市顶点后），即使股息率稳 定,股价下跌仍然可能导致账面浮亏。 价值、成长策略，在A股长期都有效。 长期收益也差不多，但每隔几年，就会 出现风格轮动,某一种风格会超过另一 种风格。 价值风格的波动,在股票类资产中属于 比较低的，遇到成长风格牛市，价值风 格也不至于大幅暴跌。不过可能会阶段 性的跑输市场，此时就对投资者的耐心 提出了考验。 其实对红利这类品种,如果想要长期坚 持下来,最好不要以短期跑赢跑输市场 来看待。 因为风格轮动的存在，红利肯定会在某 几年跑输市场。这在长达几十年的投资 中，几乎是一定会遇到的。 如果以跑赢跑输看待红利策略，那就会 患得患失。 实际上，能长期坚持投资红利类品种 的，通常是从股息率的角度看待红利。 如果是用短期资金投资,那么为了应对 流动性需求，可能会被迫在低位卖出, 影响我们的投资体验,甚至产生实际的 亏损。 所以，最好也是用3-5年以上长期不用的 闹钱来投资，并做好面对波动风险的心 理准备。 (2) A 股存在显著的成长与价值风格轮 动特征。 红利作为价值风格品种，会在不同阶段 出现跑赢或跑输市场的情况。 风险提示 ...

天风证券表示，根据经济复苏与市场流动性，可以把投资主线降维为三个方向：1、AI产业革命带来的 算力、存力、 电力及应用的科技主线机会，2、内外共振，经济逐步修复，牛市主线风格"强者恒强"， 但周期后半段易有所表现，3、赔率思维，即考虑风格轮动、底部反转的可能性。连续三年跑输但第四 年跑赢概率较大的行业有 食品饮料、农林牧渔、社会服务、 医药生物。AI产业趋势的进展取决于 AI应 用端和消费端的突破，重视AI巨头的布局。牛市初期资金更偏好少数高景气赛道，后期资金抱团聚焦 主线，新增资金获利难度提升，而周期股又具备低估值、高贝塔的属性，易随着基本面回暖的深化而发 挥较好的业绩弹性，获得增量资金青睐。 ...

Quantitative Models and Construction Methods 1. Growth-Value Rotation Model - **Model Name**: Growth-Value Rotation Model - **Model Construction Idea**: The model suggests overweighting growth stocks based on the current market environment and historical data analysis[4] - **Model Construction Process**: - The model evaluates the macroeconomic environment, valuation signals, short-term momentum signals, style breadth signals, and style congestion signals to determine the allocation between growth and value stocks[22] - The model uses the following signals: - Dynamic macro signal: 0% - Valuation reversion signal: 100% - Short-term momentum signal: 0% - Style breadth signal: 100% - Style congestion signal: 100%[23] - **Model Evaluation**: The model has shown a significant annualized return of 14.47% since 2011, with an annualized excess return of 7.90% over the benchmark[22] - **Model Test Results**: - Annualized return: 14.47% - Annualized volatility: 21.44% - Maximum drawdown: 40.08% - Sharpe ratio: 0.64 - Return-drawdown ratio: 0.36[23] 2. Small-Cap vs. Large-Cap Rotation Model - **Model Name**: Small-Cap vs. Large-Cap Rotation Model - **Model Construction Idea**: The model suggests overweighting large-cap stocks based on liquidity conditions and market trends[4] - **Model Construction Process**: - The model uses 11 effective rotation indicators to construct a comprehensive signal for rotating between small-cap and large-cap stocks[25] - The model evaluates the following indicators: - A-share Dragon Tiger List buying intensity: 0% - R007: 0% - Financing buy balance change: 0% - Thematic investment trading sentiment: 0% - Grade spread: 100% - Option volatility risk premium: 100% - Beta dispersion: 0% - PB differentiation: 0% - Block trading discount/premium rate: 100% - CSI 1000 MACD (10,20,10): 0% - CSI 1000 trading volume: 0%[27] - **Model Evaluation**: The model has consistently generated positive excess returns annually since 2014[26] - **Model Test Results**: - Annualized return: 20.61% - Annualized excess return: 13.18% - Maximum drawdown: 40.70% - Average turnover interval (trading days): 20 - Win rate (by trade): 50.00%[27] Model Backtest Results Growth-Value Rotation Model - Annualized return: 14.47% - Annualized volatility: 21.44% - Maximum drawdown: 40.08% - Sharpe ratio: 0.64 - Return-drawdown ratio: 0.36[23] Small-Cap vs. Large-Cap Rotation Model - Annualized return: 20.61% - Annualized excess return: 13.18% - Maximum drawdown: 40.70% - Average turnover interval (trading days): 20 - Win rate (by trade): 50.00%[27]

文 | 银行螺丝钉 （转载请注明出处） 今天大盘整体微跌，截止到收盘，还在3.9星。 大盘股下跌。 中证500等中小盘股波动不大。 红利等价值风格略微下跌。 创业板等成长风格盘中上涨，到收盘也变成下跌。 昨天海外市场波动比较大。 美股、商品、虚拟货币大幅波动。 受此影响，今天港股也整体下跌。 港股恒生红利低波动波动小一些，港股科技股下跌。 螺丝钉也汇总了港股指数的估值，供参考，见文章下方图片。 1. 有朋友问，港股红利类指数最近表现比较强，接近历史新高的位置。 但同期港股科技类指数下跌，是因为啥呢？ 其实A股和港股，牛市经常出现成长/价值风格轮动。 港股的指数数量比A股少。 A股价值风格，有红利、低波动、价值、自由现金流等指数。 港股主要是红利类指数，例如恒生红利低波动。 A股成长风格，有A系列龙头策略、有成长、质量等指数，也有创业板科创板。 港股主要是科技类指数，例如港股科技、恒生科技。 2. 2024年9月5.9星以来，港股也出现过几波风格切换。 （1）2024年9月到2025年9月，港股成长风格比较强势。 2025年1-2季度，港股科技股的盈利同比翻倍增长，是最近5年增速最快的时间段。 指数点数=估值* ...

Core Insights - The dividend index has shown varying performance over the years, outperforming the market from 2016 to 2018 and again from 2022 to 2024, while underperforming from 2019 to 2021 and expected to underperform in 2025 [2]. Group 1 - The A-share market exhibits a characteristic of style rotation, frequently switching between growth and value styles [3]. - A long-term effective strategy may not always yield consistent results, as strategies can become ineffective over two to three years [3]. - Average holding periods for stock funds among general investors are typically only a few weeks, which can lead to suboptimal investment decisions [3].

Core Insights - The "Spring Festival Effect" in the A-share market is not merely about whether the market rises or falls, but rather indicates a shift in market rhythm and style during this time window [2][3]. Group 1: Market Trends - Before the Spring Festival, market sentiment is generally stable, with the Shanghai Composite Index and CSI 300 Index showing average gains of approximately 0.62% and 0.77% respectively in the last five trading days before the festival, and an upward probability of about 63.6% [4][10]. - After the Spring Festival, the market typically enters a sustained upward phase, with the average gains of the three major indices turning positive within the first ten trading days post-festival. The Shanghai Composite Index and CSI 1000 Index have shown post-festival upward probabilities of 72.7% and 81.8% respectively over the past 11 years [9][14]. Group 2: Style Rotation - There is a clear rotation in market style before and after the Spring Festival. Before the festival, larger-cap stocks tend to outperform smaller-cap stocks due to heightened uncertainty and risk aversion, with the CSI 300 Index showing better performance and upward probability compared to the CSI 1000 Index [11][13]. - Post-festival, the market sees a significant shift, with the CSI 1000 Index achieving an average gain of 2.47% and an upward probability of 81.8% in the first five trading days, indicating a preference for smaller-cap growth stocks as liquidity returns and risk appetite increases [14][18]. Group 3: Historical Context - Extreme years such as 2020 and 2016 illustrate how external risks can impact market sentiment. In 2020, the market experienced significant declines due to the COVID-19 pandemic, while in 2016, the aftermath of the "circuit breaker" crisis led to a volatile pre-festival market [15]. Group 4: Future Market Predictions - For the 2026 Spring Festival, there is a high probability of a phase of market recovery post-festival, driven by valuation levels, policy expectations, and capital inflows. However, the market may not simply replicate historical averages due to potential "front-running" behavior by investors [16][17]. - The rotation logic of "defensive large-cap stocks before the festival and active small-cap stocks after" remains relevant, with a focus on sectors that align with current policy support and industry trends, particularly in technology and growth-oriented segments [18][19].

Group 1 - The investment strategy is focused on three main directions: 1) Opportunities in technology related to AI, including computing power, storage, electricity, and applications, 2) Economic recovery leading to a "stronger stronger" market style, with cyclical stocks likely to perform better in the latter half, 3) Considering the potential for style rotation and bottom reversal in sectors like food and beverage, agriculture, social services, and pharmaceuticals, which have underperformed for three consecutive years but have a higher probability of outperforming in the fourth year [1] - The AI industry trend's progress depends on breakthroughs in both application and consumption ends, with a focus on the Hang Seng Internet sector [1] - In the early stages of a bull market, funds prefer high-growth sectors, while in later stages, they concentrate on main lines, making it harder for new funds to profit, whereas cyclical stocks, with low valuations and high beta, are likely to show good performance as fundamentals improve [1] Group 2 - The profitability of bulk chemicals is expected to hit a ten-year low by the second half of 2025 due to weak demand and the end of supply-side increments, with industry-wide losses or minimal profits observed in petrochemical products [3] - The fixed asset completion growth rate in the chemical raw materials and products industry is projected to turn negative starting June 2025, with limited new capacity expected in 2026-2027 [3] - The chemical raw materials and products sector is at a turning point from active destocking to passive restocking, with downstream textile and plastic products experiencing continuous inventory declines [3] Group 3 - The strategic importance of global rare earth resources is increasing, entering a new era of high-quality development, with supply constraints and rising demand from sectors like electric vehicles and robotics expected to drive long-term growth [6] - A significant outflow of funds from bank stocks has been noted, with A-shares and H-shares showing differing performances, indicating that A-share banks are more affected by fund outflows and style influences [6] - The investment value of banks in 2026 is expected to stem from a reassessment of systemic risks and the stable return characteristics of bank equities under the RMB asset allocation framework [6]