金融工程 | 证券研究报告 — 周报 2026 年 3 月 13 日 中银量化多策略行业轮动 周报 – 20260313 当前（2026 年 3 月 12 日）中银多策略行业配置系统仓位：基础化工 （15.3%）、农林牧渔（14.1%）、电力设备及新能源（13.9%）、非银行 金融（10.5%）、医药（7.6%）、通信（7.0%）、电子（4.1%）、计算机 （4.1%）、消费者服务（3.7%）、综合（3.7%）、电力及公用事业 （3.3%）、建筑（3.2%）、银行（3.2%）、家电（3.1%）、交通运输 （3.1%）。 相关研究报告 《中银证券量化行业轮动系列（七）：如何把 握市场"未证伪情绪"构建行业动量策略》 20220917 《中银证券量化行业轮动系列（八）："估值泡 沫保护"的高景气行业轮动策略》20221018 《中银证券宏观基本面行业轮动新框架：对传 统自上而下资产配置困境的破局》20230518 《中银证券量化行业轮动系列（九）：长期反 转-中期动量-低拥挤"行业轮动策略》20240914 《中银证券量化行业轮动系列（十）：如何基 于资金流构建行业轮动策略？》20250326 《中银证券量化行业 ...

ETF 基金资金跟踪 目前周期板块资金热度较高 2026 年 3 月 13 日 投资要点 分析师：包冬青 SAC 执业证书编号： S0340526020001 基金从业资格证书编号： A20230314011388 电话：0769-26628039 邮箱：baodongqing@dgzq.com.cn ◼ 后市策略展望： 本报告的风险等级为中高风险。 本报告的信息均来自已公开信息，关于信息的准确性与完整性，建议投资者谨慎判断，据此入市，风险自担。 请务必阅读末页声明。 研 究 ETF 基 金 证 券 研 究 报 告 策 略 ◼ 市场表现回顾：（1）权益市场方面，今年以来中证2000、中证1000及 中证红利表现相对较强；风格方面周期风格领涨，其次是成长风格、 稳定风格；行业中表现相对较强的有煤炭、石油石化、有色金属、建 材、电力设备及新能源等。（2）商品市场方面：今年以来南华原油表 现相对较强。（3）基金市场方面：今年以来商品型基金指数表现相对 较强，主要受益于原油、黄金等上涨的拉动。 ◼ ETF基金资金跟踪：截止目前周期板块（地产、石油、煤炭、有色、钢 铁、建材、化工）的资金热度相对较高。主要ETF基金资金 ...

上周美元有所回升，中美利差"倒挂"程度有所加深。10Y 美债名义/实际利率均回升，通胀预期同样有所回升。离岸 美元流动性边际有所收紧，国内银行间资金面均衡偏松，期限利差(10Y-1Y)走阔。 交易热度、波动与流动性： 市场交易热度继续回升，主要指数的波动率均回升。石油石化、军工、电力及公用事业、钢铁、通信等板块的交易热 度均处于 90%分位数以上。钢铁、军工、石油石化、有色板块的波动率处于 80%分位数以上。 机构调研： 银行、电子、计算机、电新、医药等板块调研热度居前，建材、计算机、传媒、医药、纺服等板块的调研热度环比仍 在上升。 分析师预测： 全 A 的 26/27 年净利润预测同时被下调。行业上，计算机、交运、机械、电力及公用事业、建筑、金融等 26/27 年净 利润预测均被上调。指数上，中证 500、创业板指的 26/27 年净利润预测均被上调，沪深 300、上证 50 则均被下调。 风格上，中盘/小盘成长、大盘/中盘/小盘价值的 26/27 年的净利润预测均被上调，大盘成长则均被下调。 北上活跃度有所回升，继续小幅净卖出 A 股 基于前 10 大活跃股口径，北上在通信、电新、汽车等板块的买卖总额之比 ...

宏观流动性： 上周美元有所回升，中美利差"倒挂"程度有所加深。10Y 美债名义/实际利率均回升，通胀预期同样有所回升。离岸 美元流动性边际有所收紧，国内银行间资金面均衡偏松，期限利差(10Y-1Y)走阔。 交易热度、波动与流动性： 市场交易热度继续回升，主要指数的波动率均回升。石油石化、军工、电力及公用事业、钢铁、通信等板块的交易热 度均处于 90%分位数以上。钢铁、军工、石油石化、有色板块的波动率处于 80%分位数以上。 机构调研： 银行、电子、计算机、电新、医药等板块调研热度居前，建材、计算机、传媒、医药、纺服等板块的调研热度环比仍 在上升。 分析师预测： 全 A 的 26/27 年净利润预测同时被下调。行业上，计算机、交运、机械、电力及公用事业、建筑、金融等 26/27 年净 利润预测均被上调。指数上，中证 500、创业板指的 26/27 年净利润预测均被上调，沪深 300、上证 50 则均被下调。 风格上，中盘/小盘成长、大盘/中盘/小盘价值的 26/27 年的净利润预测均被上调，大盘成长则均被下调。 北上活跃度有所回升，继续小幅净卖出 A 股 基于前 10 大活跃股口径，北上在通信、电新、汽车等板块 ...

Market and Industry Overview - In the past month, major domestic market indices showed mixed performance, with the Guozheng 2000, Zhongzheng 1000, Zhongzheng 500, and Shanghai-Shenzhen 300 rising by 4.07%, 3.71%, 3.44%, and 0.09% respectively, while the Shanghai 50 fell by -0.88% [10] - Among the CITIC first-level industry indices, 22 sectors experienced gains, with the steel, building materials, machinery, coal, and defense industries leading the way. The steel industry had the highest monthly increase at 9.52%. Conversely, consumer services, non-bank financials, and media sectors lagged behind, with monthly declines of -3.37%, -3.48%, and -4.22% respectively [10][11] Industry Rotation Strategy Performance - In February, factor performance varied, with profitability and valuation momentum factors continuing to perform well, achieving IC values of 15.81% and 30.64% respectively. The analyst expectation factor had an IC value of 5.47% [18] - The long-short returns for profitability and valuation momentum were 2.74% and 9.53% respectively. For the year-to-date, the average IC values for profitability and valuation momentum factors were 33.94% and 36.14%, with long positions yielding returns of 1.91% and 6.63% respectively [18] - The February performance of the supernormal enhancement industry rotation strategy yielded a return of 6.86%, while the equal-weight benchmark return was 4.83%, resulting in an excess return of 2.03% [32] Current Industry Recommendations - The supernormal enhancement industry rotation strategy for March recommends the non-ferrous metals, basic chemicals, telecommunications, electronics, and machinery sectors. The strategy has removed media and defense industries from its holdings and added telecommunications and machinery [46] - The basic chemicals sector saw an increase in analyst expectation scores, ranking second among all sectors, while the telecommunications sector's supernormal factor score significantly improved, elevating its total ranking to third [46] - The basic chemicals, non-ferrous metals, steel, basic chemicals, and real estate sectors were recommended by the valuation industry rotation strategy, with the defense, steel, and real estate sectors not included in the supernormal enhancement strategy [46] Research Industry Selection Strategy - The research industry selection strategy for March includes telecommunications, home appliances, non-bank financials, electric power and public utilities, and electric equipment and new energy sectors. The research heat for telecommunications, home appliances, electric power and public utilities, and new energy sectors has increased, while the research crowding for telecommunications, home appliances, non-bank financials, and electric power and public utilities has decreased, leading to their recommendation [46][50]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance Model - **Model Construction Idea**: This model tracks the distance of stock prices or indices from their 250-day high to identify market trends and hotspots. It is based on the momentum and trend-following strategy, which has been proven effective in various studies[11][18]. - **Model Construction Process**: The formula for calculating the 250-day new high distance is as follows: $ 250 \text{-day new high distance} = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ Where: - $ Close_t $ represents the latest closing price - $ ts\_max(Close, 250) $ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value, indicating the degree of decline[11]. - **Model Evaluation**: The model effectively captures market momentum and highlights leading stocks or indices that are driving market trends[11][18]. 2. Model Name: Stable New High Stock Screening Model - **Model Construction Idea**: This model focuses on identifying stocks with stable price paths and consistent momentum. It incorporates factors such as analyst attention, relative strength, and price stability to select stocks with strong and sustained performance[23][26]. - **Model Construction Process**: The screening process involves the following steps: 1. **Analyst Attention**: Stocks must have at least five "Buy" or "Overweight" ratings in the past three months[26]. 2. **Relative Strength**: Stocks must rank in the top 20% of the market in terms of 250-day price performance[26]. 3. **Price Stability**: Stocks are scored based on two indicators: - **Price Path Smoothness**: Measured by the ratio of price displacement to the total price path length over the past 120 days[23]. - **Sustained New Highs**: The average 250-day new high distance over the past 120 days[23]. 4. **Trend Continuation**: Stocks with the lowest average 250-day new high distance over the past five days are selected, with the top 50 stocks chosen for further analysis[26]. - **Model Evaluation**: The model emphasizes the importance of smooth price paths and sustained momentum, which are less likely to attract excessive attention and thus generate stronger momentum effects[23][26]. --- Model Backtesting Results 1. 250-Day New High Distance Model - **Indices' 250-Day New High Distance**: - Shanghai Composite Index: 1.40% - Shenzhen Component Index: 2.28% - CSI 300: 2.72% - CSI 500: 3.44% - CSI 1000: 3.64% - CSI 2000: 3.00% - ChiNext Index: 4.69% - STAR 50 Index: 9.10%[2][12][30] 2. Stable New High Stock Screening Model - **Selected Stocks**: 27 stocks were identified as stable new high stocks, including Zhongwu Gaoxin, Baiwei Storage, and Jereh Co. - **Sector Distribution**: - Manufacturing: 9 stocks (e.g., machinery sector) - Cyclical: 9 stocks (e.g., basic chemicals sector)[27][31] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: This factor measures the relative position of a stock's or index's price compared to its 250-day high, serving as an indicator of momentum and trend strength[11]. - **Factor Construction Process**: The calculation is the same as the 250-day new high distance model: $ 250 \text{-day new high distance} = 1 - \frac{Close_t}{ts\_max(Close, 250)} $ - $ Close_t $: Latest closing price - $ ts\_max(Close, 250) $: Maximum closing price over the past 250 trading days[11]. - **Factor Evaluation**: The factor effectively identifies stocks or indices with strong momentum, which are often market leaders[11][18]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: This factor evaluates the stability of a stock's price movement, with smoother paths indicating stronger and more sustainable momentum[23]. - **Factor Construction Process**: The smoothness is calculated as the ratio of price displacement to the total price path length over the past 120 days[23]. - **Factor Evaluation**: Stocks with smoother price paths tend to exhibit stronger momentum effects due to reduced investor attention[23]. 3. Factor Name: Sustained New Highs - **Factor Construction Idea**: This factor measures the consistency of a stock's ability to reach new highs over time, reflecting its trend strength[23]. - **Factor Construction Process**: The average 250-day new high distance is calculated over the past 120 days to assess the stock's sustained performance[23]. - **Factor Evaluation**: This factor highlights stocks with strong and consistent upward trends, making them attractive for momentum-based strategies[23]. --- Factor Backtesting Results 1. 250-Day New High Distance Factor - **Indices' 250-Day New High Distance**: - Shanghai Composite Index: 1.40% - Shenzhen Component Index: 2.28% - CSI 300: 2.72% - CSI 500: 3.44% - CSI 1000: 3.64% - CSI 2000: 3.00% - ChiNext Index: 4.69% - STAR 50 Index: 9.10%[2][12][30] 2. Price Path Smoothness Factor - **Selected Stocks**: Stocks with smoother price paths were identified, including Zhongwu Gaoxin, Baiwei Storage, and Jereh Co[27][31]. 3. Sustained New Highs Factor - **Selected Stocks**: Stocks with consistent new highs over the past 120 days were identified, with the top performers being from the manufacturing and cyclical sectors[27][31].

- The report discusses a **quantitative timing model based on volume signals**, which evaluates market sentiment and timing by analyzing volume trends. The model's construction involves assessing the volume levels of major indices (e.g., Shanghai Composite, CSI 300, etc.) and assigning a cautious view when volume signals are weak. As of February 27, 2026, all major indices showed a "cautious" volume timing signal[24][25] - A **momentum sentiment indicator** is introduced, which calculates the proportion of stocks in the CSI 300 index with positive returns over a specific period (N days). The formula is: $ \text{CSI 300 N-day Upward Proportion} = \frac{\text{Number of CSI 300 stocks with positive returns in N days}}{\text{Total number of CSI 300 stocks}} $ This indicator is used to capture market sentiment, with higher values indicating optimism. As of February 27, 2026, the indicator was above 60%, reflecting high market sentiment[25][26][27] - A **momentum sentiment timing strategy** is applied by smoothing the momentum sentiment indicator using two moving averages (short-term and long-term). When the short-term average exceeds the long-term average, the model signals a bullish market view. Parameters used are N=230, N1=50, and N2=35. As of February 27, 2026, the short-term line was above the long-term line, maintaining a bullish outlook[27][29] - The report also introduces an **eight-moving-average sentiment indicator**, which evaluates the CSI 300 index's trend by comparing its closing price against eight moving averages (8, 13, 21, 34, 55, 89, 144, 233). The indicator assigns values based on the number of moving averages the index surpasses. If the index price exceeds more than five moving averages, the model signals a bullish view. As of February 27, 2026, the CSI 300 index was in a "sentiment prosperity zone," indicating positive sentiment[34][38] - **Cross-sectional volatility** is analyzed as a measure of alpha opportunities. The report highlights that cross-sectional volatility for CSI 300, CSI 500, and CSI 1000 stocks increased week-over-week, indicating an improved short-term alpha environment. Over the past quarter, cross-sectional volatility for CSI 300 and CSI 1000 was in the upper-middle range of the past six months, while CSI 500 was in the middle range[39][40] - **Time-series volatility** is also examined, showing a week-over-week decline for CSI 300, CSI 500, and CSI 1000 stocks, suggesting a deteriorating short-term alpha environment. Over the past quarter, time-series volatility for CSI 300 and CSI 500 was in the middle range of the past six months, while CSI 1000 was in the upper-middle range[40][43]

Group 1 - The macro liquidity environment shows a recent decline in the US dollar index, with the degree of inversion in the China-US interest rate spread continuing to narrow. Both nominal and real yields on 10-year US Treasuries have decreased, indicating a drop in inflation expectations [2][15][22]. - Offshore dollar liquidity has tightened marginally, while the domestic interbank funding environment remains balanced and relatively loose, with the term spread (10Y-1Y) narrowing [2][22]. Group 2 - Market trading activity has increased, with trading heat in sectors such as building materials, steel, chemicals, media, and oil & petrochemicals exceeding the 90th percentile. The volatility of major indices has also decreased [3][27][33]. - The volatility of the steel and military sectors remains above the 80th percentile, indicating heightened market activity in these areas [3][33]. Group 3 - Research activity is concentrated in sectors such as banking, electronics, electric new energy, computing, and military, with a notable increase in research heat in the home appliance sector [4][44]. - The research intensity in the top 100 holdings of actively managed equity funds, as well as in the ChiNext Index, CSI 500, and CSI 300, has shown a decline [4][44][50]. Group 4 - Analysts have adjusted net profit forecasts for the entire A-share market for 2026 and 2027, with increases noted in sectors such as oil & petrochemicals, transportation, textiles, machinery, and utilities [5][21][24]. - The proportion of stocks with upward revisions in net profit forecasts for 2026 and 2027 has continued to rise, while the forecasts for the CSI 500 and SSE 50 have been downgraded [5][21][24]. Group 5 - Northbound trading activity has rebounded, with a net sell-off of A-shares observed. The trading volume ratio in sectors like telecommunications, non-ferrous metals, and food & beverages has increased, while net buying has been concentrated in utilities, electronics, and construction [6][31][33]. - The net buying activity in coal, food & beverages, and media sectors contrasts with net selling in electric new energy and chemicals [6][31][33]. Group 6 - The margin financing activity has reached its highest point since late January 2026, with significant net buying in sectors such as electronics, non-ferrous metals, and electric new energy, while net selling occurred in oil & petrochemicals and agriculture [7][35]. - The trading volume on the "Dragon and Tiger List" has increased, particularly in the chemical, light industry, and steel sectors, indicating a resurgence in speculative trading [7][41]. Group 7 - Actively managed equity funds have seen a decrease in positions, with notable increases in allocations to oil & petrochemicals, building materials, and consumer services, while reducing positions in electronics, non-ferrous metals, and computing [8][45]. - The correlation of actively managed equity funds with large/mid/small-cap value stocks has increased, while the correlation with growth stocks has decreased [8][45].

Group 1: Macroeconomic Liquidity - The US dollar index has declined, and the degree of "inversion" in the China-US interest rate spread continues to narrow, with inflation expectations also decreasing [2][16] - Offshore US dollar liquidity has marginally tightened, while the domestic interbank funding situation remains balanced, with the yield spread between 10Y and 1Y bonds narrowing [2][23] Group 2: Market Trading Activity - Market trading activity continues to decline, with most indices experiencing increased volatility; sectors such as media, building materials, light industry, and telecommunications are above the 90th percentile in trading activity [3][29] - The volatility of major indices like the Shanghai Composite, CSI 300, and CSI 500 has increased, while the military industry sector's volatility is above the 80th percentile [3][34] Group 3: Institutional Research - Research activity is high in sectors such as banking, electronics, computing, electric new energy, and military industry, with the textile and apparel sectors showing a month-on-month increase in research activity [4][46] Group 4: Analyst Forecasts - Analysts have adjusted the net profit forecasts for the entire A-share market for 2026 and 2027, with increases in forecasts for sectors including non-ferrous metals, media, building materials, chemicals, and electronics [5][19] - The proportion of stocks with upward adjustments in net profit forecasts for 2026 and 2027 continues to rise across the A-share market [5][18] Group 5: Northbound Trading Activity - Northbound trading activity has slightly decreased, but there has been a net purchase of A-shares; the trading volume ratio in sectors like telecommunications, electronics, and electric new energy has increased [6][31] - Northbound investors primarily net bought in the electronics, telecommunications, and electric new energy sectors, while net selling occurred in media, food and beverage, and utilities sectors [6][33] Group 6: Margin Financing Activity - Margin financing activity continues to decline, reaching a relative low since July 2025, with a net sell-off across various sectors [7][35] - The financing buy-in ratio has increased for sectors like telecommunications and non-bank financials, while net selling has occurred across various styles of stocks [7][39] Group 7: Fund Activity - Active equity funds have increased their positions, particularly in media, computing, and military sectors, while reducing positions in chemicals, automobiles, and electronics [9][45] - The correlation between active equity funds and large-cap growth/mid-cap value has increased, while the correlation with mid-cap/small-cap growth and large-cap/small-cap value has decreased [9][48]

创业板50ETF国泰（159375）跟踪的是创业板50指数（399673），单日涨跌幅限制达20%，该指数从创 业板市场中选取日均成交额较大的50只证券作为指数样本，以反映知名、市值大且流动性好的企业整体 表现。其成分股主要集中于电力设备及新能源、医药、计算机等高成长性行业，整体呈现出显著的高成 长风格与良好流动性。 （文章来源：每日经济新闻） 野村东方国际证券指出，在流动性改善带动市场情绪走强的背景下，投资者可以对一季度A股表现给予 更多期待。全球通胀及增长情形，以及地缘局势的日趋紧张将进一步驱动周期商品表现。考虑到"十五 五"规划出台，其中对安全韧性的描述（自主可控的投资加码）、新兴产业的商业化（商业航天卫星、 6G、低空机器人）和货币与财政政策的创新空间有望对A股形成积极影响。投资者或可同步增加对中美 两国自主可控题材的关注度，在中国资产中加大对军贸、创新药、AI基建、商业航天与6G基建、工控/ 办公/AI软件等题材的关注。 ...