金融工程| 证券研究报告 —周报 2025 年 9 月 29 日 中银量化大类资产跟踪 微盘股回撤，拥挤度下行，处于历较低位置 本周 A 股上涨，港股下跌，美股下跌，其他海外权益市场走势分化。 微盘股 vs 基金重仓：微盘股拥挤度下降至历史低位；基金重仓超额 累计净值持续处于历史低位，拥挤度近期上行至历史高位。 A 股行情及成交热度 本周领涨的行业为电子、有色金属、机械；领跌的行业为消费者服务、 纺织服装、建材。本周成交热度最高的行业为国防军工、家电、电子； 成交热度最低的行业为农林牧渔、食品饮料、石油石化。 A 股估值与股债性价比 A 股资金面 A 股风格与拥挤度 成长 vs 红利：成长风格拥挤度及超额净值持续处于历史低位；红利 风格拥挤度近期处于历史较低位置。 股票市场概览 小盘 vs 大盘：小盘风格拥挤度近期上升至历史均衡位置，大盘风格 拥挤度近期上升至历史高位。 机构调研活跃度 当前机构调研活跃度历史分位居前的行业为房地产、通信、有色金 属，居后的行业为银行、医药、机械。 利率市场 本周中国国债利率下跌，美国国债利率上涨，中美利差处于历史高位。 汇率市场 近一周在岸人民币较美元贬值，离岸人民币较美元贬 ...

- The report does not contain any specific quantitative models or factors for analysis[1][2][3] - The report primarily focuses on market trends, style performance, valuation metrics, and fund flows without detailing quantitative models or factor construction[4][5][6] - Key metrics such as PE_TTM, ERP, and fund issuance are discussed, but these are general market indicators rather than specific quantitative factors or models[10][11][12]

金融工程| 证券研究报告 —周报 2025 年 9 月 1 日 中银量化大类资产跟踪 A 股持续放量，微盘股进入回调区间 股票市场概览 本周 A 股上涨，港股下跌，美股下跌，其他海外权益市场走势分化。 A 股风格与拥挤度 成长 vs 红利：成长风格拥挤度及超额净值持续处于历史低位；红利 风格拥挤度近期处于历史较低位置。 小盘 vs 大盘：小盘风格超额净值及拥挤度均处于历史低位，大盘风 格拥挤度近期上升至历史均衡位置。 微盘股 vs 基金重仓：微盘股拥挤度下降至历史均衡位置；基金重仓 超额累计净值持续处于历史低位，拥挤度近期上行至历史较高位置。 A 股行情及成交热度 本周领涨的行业为通信、有色金属、电子；领跌的行业为煤炭、家电、 综合金融。本周成交热度最高的行业为计算机、电子、国防军工；成交 热度最低的行业为煤炭、交通运输、石油石化。 A 股估值与股债性价比 A 股资金面 机构调研活跃度 当前机构调研活跃度历史分位居前的行业为有色金属、房地产、通 信，居后的行业为银行、机械、电子。 利率市场 本周中国国债利率上涨，美国国债利率下跌，中美利差处于历史高位。 汇率市场 近一周在岸人民币较美元升值，离岸人民币较美元升 ...

- The report does not contain any specific quantitative models or factors for analysis[1][2][3]

Quantitative Models and Construction Methods 1. Model Name: Changjiang Momentum Index - **Model Construction Idea**: The index uses the momentum effect in the A-share market, selecting stocks with strong momentum characteristics and relatively high liquidity[26][27] - **Model Construction Process**: - Momentum indicator = (1-year stock return) - (1-month stock return, excluding stocks with price limits)[26][27] - Select the top 100 stocks in the A-share market with the strongest momentum characteristics and relatively high liquidity as index constituents[26][27] - **Model Evaluation**: The index effectively represents the overall trend of stocks with the strongest momentum characteristics in the A-share market[26][27] 2. Model Name: Changjiang Reversal Index - **Model Construction Idea**: The index captures the reversal effect in the A-share market, selecting stocks with strong reversal characteristics and good liquidity[28] - **Model Construction Process**: - Screening indicator = 1-month stock return[28] - Select the top 100 stocks in the A-share market with the strongest reversal characteristics and good liquidity as index constituents[28] - Weight the constituents based on their average daily trading volume over the past three months[28] - **Model Evaluation**: The index aims to accurately represent the overall performance of stocks with high reversal characteristics in the A-share market during different phases[28] --- Model Backtesting Results 1. Changjiang Momentum Index - **Relative Return (Momentum vs. Reversal)**: - 1 week: -0.2% - 1 month: 5.5% - Year-to-date: 8.5%[26][27] 2. Changjiang Reversal Index - **Relative Return (Reversal vs. Momentum)**: - 1 week: 0.2% - 1 month: -5.5% - Year-to-date: -8.5%[26][27] --- Quantitative Factors and Construction Methods 1. Factor Name: Style Crowdedness - **Factor Construction Idea**: Measures the crowdedness of different investment styles (e.g., growth, dividend, small-cap, large-cap) based on turnover rates[34][120] - **Factor Construction Process**: - Calculate the z-score standardized turnover rate of each style index over the past n trading days[120] - Subtract the turnover rate of the Wind All A Index from the style index turnover rate[120] - Compute the rolling y-year percentile of the difference[120] - Parameters: - 6-month crowdedness: n = 126, rolling window = 3 years - 1-year crowdedness: n = 252, rolling window = 6 years[120] - **Factor Evaluation**: Provides insights into the relative popularity and valuation of different investment styles over time[34][120] 2. Factor Name: Style Excess Cumulative Net Value - **Factor Construction Idea**: Measures the relative performance of style indices compared to the Wind All A Index[121] - **Factor Construction Process**: - Base date: January 4, 2016[121] - Daily cumulative net value = (style index closing value) / (base date closing value)[121] - Excess cumulative net value = (style index cumulative net value) / (Wind All A cumulative net value)[121] - **Factor Evaluation**: Tracks the relative performance trends of different styles over time[121] --- Factor Backtesting Results 1. Style Crowdedness - **Growth vs. Dividend**: - Growth crowdedness: 0% (1-year percentile), unchanged from last week[34] - Dividend crowdedness: 16% (1-year percentile), down from 22% last week[34] - **Small-cap vs. Large-cap**: - Small-cap crowdedness: 0% (1-year percentile), down from 1% last week[38] - Large-cap crowdedness: 29% (1-year percentile), down from 32% last week[38] - **Micro-cap vs. Fund-heavy**: - Micro-cap crowdedness: 6% (1-year percentile), unchanged from last week[40] - Fund-heavy crowdedness: 6% (6-month percentile), unchanged from last week[40] 2. Style Excess Cumulative Net Value - **Growth vs. Dividend**: - 1 week: +0.4% - 1 month: +2.3% - Year-to-date: +0.6%[26][34] - **Small-cap vs. Large-cap**: - 1 week: -1.4% - 1 month: -0.5% - Year-to-date: +1.5%[26][38] - **Micro-cap vs. Fund-heavy**: - 1 week: +1.3% - 1 month: +10.8% - Year-to-date: +26.5%[26][40]