作为投资者，谁不希望买到牛股。 事实上，每一年都会有不少牛股出现，即便是在赚钱效应欠佳的2022年，A股百大牛股的年化收益门槛 值，依然超过了62%。 每一年的牛股长啥样？借助兴业证券对近五年来年度百大牛股画像的整理，我们得以窥见这期间每一年的 牛股面孔。 2021年，新能源产业链是孕育牛股的沃土。中游制造和中游材料板块诞生的牛股数量最多，分别贡献了占 比达29%和24%的牛股。机械、电力设备及新能源以及基础化工的"牛股辈出"。 但市值特点又有新变化。这些标的中，2024年年初在行业市值分位数排名在前20%的就有21只，较2023年 的4只大幅增加。上一年非常明显的小微盘风格不再明显。 与此同时，市场审美从大龙头转向"小高新"的现象初现端倪。比如，有21只年初时市值还在板块后20%、 但最终进入了年度的百大牛股排行榜的标的。 那一年的"市场审美"与前两年相比也有变化。2021年的百大牛股ROE虽仍高于A股整体，但领先幅度较 2019、2020年已有所收窄。但2021年A股整体前三季度归母净利润增速的中位数为20.4%，而百大牛股则 为132.6%，高出A股整体112.2%，领先幅度远大于2019年的47.6% ...

2025 年 12 月 6 日 总量研究 市场大市值风格显著，机构调研组合超额收益显著 ——量化组合跟踪周报 20251206 要点 量化市场跟踪 大类因子表现：本周全市场股票池中，盈利因子获取正收益 0.61%；市值因子、 非线性市值因子、动量因子分别获取正收益 0.25%、0.24%、0.23%，市场表现 为大市值风格；残差波动率因子获取负收益-0.59%；其余风格因子表现一般。 单因子表现：沪深 300 股票池中，本周表现较好的因子有单季度 ROA (1.43%)、 市销率 TTM 倒数 (1.39%)、日内波动率与成交金额的相关性 (1.36%)。表现较 差的因子有对数市值因子(-1.70%)、单季度净利润同比增长率 (-1.25%)、5 日反 转(-1.25%)。 中证 500 股票池中，本周表现较好的因子有 5 日平均换手率(1.68%)、日内波动 率与成交金额的相关性(1.66%)、6 日成交金额的移动平均值 (1.30%)。表现较 差的因子有对数市值因子(-1.21%)、单季度 ROE 同比(-1.14%)、单季度 ROA 同 比(-0.87%)。 流动性 1500 股票池中，本周表现较好的因 ...

Core Insights - The overall market showed a significant positive return from the market capitalization factor at 0.99%, while other factors like leverage, liquidity, residual volatility, and valuation factors yielded negative returns of -0.41%, -0.43%, -0.50%, and -0.68% respectively [4] Factor Performance - In the CSI 300 stock pool, the best-performing factors included the correlation between intraday volatility and trading volume (1.23%), ROE stability (1.14%), and the proportion of downside volatility (1.13%). Conversely, the worst-performing factors were early morning return factor (-2.46%), momentum spring factor (-2.21%), and net profit gap (-1.72%) [5] - In the CSI 500 stock pool, the top factors were quarterly gross margin on total assets (1.82%), momentum-adjusted large orders (1.66%), and TTM gross margin on total assets (1.63%). The underperforming factors included year-on-year quarterly ROA (-0.66%), year-on-year quarterly ROE (-0.55%), and ROIC enhancement factor (-0.53%) [5] - In the liquidity 1500 stock pool, the leading factors were TTM net profit margin (1.82%), TTM operating profit margin (1.44%), and ROA stability (1.38%). The lagging factors were inverse TTM price-to-sales ratio (-1.31%), logarithmic market capitalization factor (-1.07%), and net profit gap (-0.95%) [5] Industry Factor Performance - Fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and TTM operating profit per share yielding consistent positive returns in the textile and apparel, and steel industries. The EP factor performed well among valuation factors, showing significant positive returns in coal, beauty care, and textile and apparel industries. Residual volatility and liquidity factors also showed notable positive returns in the media industry [6] PB-ROE-50 Combination Tracking - The PB-ROE-50 combination recorded negative excess returns across all stock pools, with the CSI 500 pool showing an excess return of -1.30%, the CSI 800 pool at -2.09%, and the overall market stock pool at -1.46% [7] Institutional Research Combination Tracking - Both public and private fund research selection strategies yielded negative excess returns, with the public fund strategy showing an excess return of -1.91% relative to the CSI 800, and the private fund strategy at -3.65% [8] Block Trade Combination Tracking - The block trade combination recorded negative excess returns relative to the CSI All Index, with an excess return of -2.84% [9] Directed Issuance Combination Tracking - The directed issuance combination also showed negative excess returns relative to the CSI All Index, with an excess return of -1.42% [10]

Quantitative Models and Construction Methods - **Model Name**: Index Enhancement Strategy (80% Component Constraint) **Model Construction Idea**: The model aims to replicate the performance of typical index enhancement products by adjusting the distribution of components across different market capitalization domains[4][33][34] **Model Construction Process**: 1. The model constrains the component weight to 80% while adjusting the allocation in micro-cap stocks. 2. Specific constraints include: - Industry exposure: 0.1% - Weight cap for CSI 2000 components: 0.2% - Weight cap for micro-cap stocks: 0.1% - Monthly rebalancing frequency 3. Performance metrics such as excess return, tracking error, IR, and maximum drawdown are calculated for different micro-cap allocations (0%, 5%, 10%)[34][35] **Model Evaluation**: The model demonstrates that higher micro-cap allocations can enhance excess returns, albeit with slightly increased tracking error and drawdown[35] - **Model Name**: Index Enhancement Strategy (Relaxed Component Constraint) **Model Construction Idea**: This model explores the impact of relaxing the component weight constraint to 40% while varying micro-cap allocations and market capitalization exposures[33][39] **Model Construction Process**: 1. The component weight constraint is relaxed to 40%, and micro-cap allocations are adjusted (0%, 10%, 20%). 2. Additional constraints include: - Industry exposure: 0.1% - Weight cap for CSI 2000 components: 0.2% - Weight cap for micro-cap stocks: 0.1% - Monthly rebalancing frequency 3. Performance metrics such as excess return, tracking error, IR, and maximum drawdown are calculated for different scenarios[39][40] **Model Evaluation**: Relaxing the component constraint significantly improves excess returns, especially with higher micro-cap allocations, though it introduces higher tracking error and drawdown risks[40] Model Backtesting Results - **Index Enhancement Strategy (80% Component Constraint)**: - CSI 300 (0% micro-cap): Excess Return: 7.97%, Tracking Error: 3.34%, IR: 5.38, Max Drawdown: -1.16% - CSI 300 (5% micro-cap): Excess Return: 8.52%, Tracking Error: 3.45%, IR: 5.58, Max Drawdown: -1.19% - CSI 300 (10% micro-cap): Excess Return: 8.70%, Tracking Error: 3.57%, IR: 5.51, Max Drawdown: -1.33% - CSI 500 (0% micro-cap): Excess Return: 7.55%, Tracking Error: 3.87%, IR: 4.38, Max Drawdown: -1.52% - CSI 500 (5% micro-cap): Excess Return: 8.23%, Tracking Error: 3.88%, IR: 4.78, Max Drawdown: -1.38% - CSI 500 (10% micro-cap): Excess Return: 9.20%, Tracking Error: 3.98%, IR: 5.24, Max Drawdown: -1.39% - CSI 1000 (0% micro-cap): Excess Return: 10.12%, Tracking Error: 4.28%, IR: 5.40, Max Drawdown: -1.50% - CSI 1000 (5% micro-cap): Excess Return: 9.76%, Tracking Error: 4.31%, IR: 5.16, Max Drawdown: -1.69% - CSI 1000 (10% micro-cap): Excess Return: 9.76%, Tracking Error: 4.31%, IR: 5.16, Max Drawdown: -1.69%[35] - **Index Enhancement Strategy (Relaxed Component Constraint)**: - CSI 300 (0% micro-cap): Excess Return: 10.87%, Tracking Error: 4.35%, IR: 5.73, Max Drawdown: -1.29% - CSI 300 (10% micro-cap): Excess Return: 13.96%, Tracking Error: 7.01%, IR: 4.64, Max Drawdown: -3.02% - CSI 500 (0% micro-cap): Excess Return: 10.25%, Tracking Error: 6.65%, IR: 3.52, Max Drawdown: -2.19% - CSI 500 (20% micro-cap): Excess Return: 17.08%, Tracking Error: 7.98%, IR: 5.07, Max Drawdown: -2.43% - CSI 1000 (0% micro-cap): Excess Return: 10.84%, Tracking Error: 6.24%, IR: 3.98, Max Drawdown: -1.54% - CSI 1000 (20% micro-cap): Excess Return: 16.81%, Tracking Error: 7.38%, IR: 5.38, Max Drawdown: -2.04%[40] Quantitative Factors and Construction Methods - **Factor Name**: Market Capitalization (Size) **Factor Construction Idea**: Market capitalization is used as a linear factor to segment stocks into deciles, with smaller-cap stocks expected to deliver higher excess returns[19][22] **Factor Construction Process**: 1. Divide the market into 10 deciles based on market capitalization. 2. Calculate the excess return for each decile. 3. Analyze the trend of excess returns across deciles[22] **Factor Evaluation**: The smallest decile (G01) delivers the highest excess return (22.4%), while returns decrease progressively with increasing market capitalization[22] - **Factor Name**: Mid-Cap (Nonlinear Size) **Factor Construction Idea**: Mid-cap is modeled as a cubic function to capture the performance of stocks outside the large-cap and small-cap domains[2][25] **Factor Construction Process**: 1. Define mid-cap stocks using a cubic function of market capitalization. 2. Analyze the overlap between mid-cap and market capitalization groups. 3. Evaluate the excess return of mid-cap groups[25][26] **Factor Evaluation**: Mid-cap stocks exhibit significant overlap with small-cap stocks, and the smallest mid-cap group (G01) delivers high excess returns (21.6%)[22][25] Factor Backtesting Results - **Market Capitalization (Size)**: - G01: 22.4%, G02: 15.0%, G03: 22.6%, G04: 20.4%, G05: 13.6%, G06: 13.2%, G07: 10.9%, G08: 6.9%, G09: 3.9%, G10: -5.6%[22] - **Mid-Cap (Nonlinear Size)**: - G01: 21.6%, G02: 13.7%, G03: -0.5%, G04: 0.0%, G05: 1.5%, G06: 0.8%, G07: 0.5%, G08: -2.1%, G09: -0.2%, G10: -2.7%[22]

Overview - The A-share market experienced fluctuations in the first half of 2025, with the overall trend being a recovery after a rapid decline at the beginning of the year. The market indices showed positive performance, with the Wind All A, Shanghai Composite Index, and CSI 300 increasing by 5.83%, 2.76%, and 0.03% respectively [1][10][15]. Style - In the first half of 2025, the market style was relatively stable, with micro-cap stocks outperforming small-cap, which in turn outperformed mid-cap and large-cap stocks. The micro-cap index rose by 36.41%, while the large-cap index only increased by 0.36% [2][22][23]. Industry & Theme - The industry rotation speed in the A-share market showed a fluctuating trend, with a peak in rotation intensity in March. Out of 31 primary industries, 20 experienced gains, with notable increases in non-ferrous metals (up 18.12%), banking (up 13.10%), defense and military (up 12.99%), and media (up 12.77%). Conversely, coal, food and beverage, real estate, and oil and petrochemicals performed poorly [3][31][30][31]. - The second-tier industry of ground weaponry saw a rise of over 60%, while sectors like coal mining, photovoltaic equipment, liquor, and hotel catering underperformed [3][36]. Monthly Performance - Monthly performance showed that no industry recorded gains in all six months. Non-ferrous metals performed well in January, March, and June, with a notable 9.3% increase in June. The banking sector remained stable with minimal drawdowns, while the defense and military sector showed significant volatility [31][32]. Themes - Excluding certain speculative themes, 15 thematic concepts achieved over 40% growth, with servers, stock trading software, GPUs, electric vehicles, and equipment upgrades leading the way [37].

以下文章来源于二鸟说 ，作者二鸟说 二鸟说 . A股中市场风格的划分方法很多，如市值风格（大盘/中盘/小盘）、投资风格（成长/价值）、估值风格（高估值/低估值）、价格风格（高价股/低 价股）等，其中应用最广泛的是按照市值和投资风格属性划分，下面重点为大家分析一下。 1、大盘/中盘/小盘市值风格 专注于基金投资，秉承长期投资，价值投资，稳健投资的原则，合理进行大类资产配置，科学的择基，适当择时，实现资产长期稳健增值。 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者： 二鸟说 来源：雪球 A股市场每隔一段时间，就会上演价值与成长风格的切换，比如在2025年1季度内，科技成长风格在1月和2月领涨市场，3月开始调整；价值风格前 期涨幅较小，在3月份表现相对抗跌。 在这个快速切换的过程中，有些投资者刚刚参与到科技股行情中，结果不小心高位站岗，有些投资者则因为将资金从科技板块撤出或配置到其他低 位潜力板块之后，在调整中避免了较大的损失。这说明，市场风格的切换会导致某种风格的资产在一段时间内表现优异，而在另一段时间内表现较 差，对投资收益有显著影响。 那么市场风格切换背后的原因是 ...