Investment Rating - The industry investment rating is "stronger than the market" with an expectation of a relative increase of over 10% in the industry index compared to the broader market within the next six months [25]. Core Insights - The textile and apparel industry is projected to have a pre-profit rate of 51% for the first half of 2025, with 22 out of 43 companies expected to be profitable [1][10]. - The performance of sub-sectors within the industry is varied, with the textile manufacturing sector showing a higher pre-profit rate of 60% [2][14]. - The overall industry performance is described as flat, with a decrease in the proportion of companies expecting profit growth and an increase in those continuing to incur losses [10][23]. Summary by Sections Industry Performance - As of July 28, 2025, 43 out of 107 A-share companies in the textile and apparel sector have released performance forecasts, resulting in a disclosure rate of 40%, ranking second among eight major consumption sectors [9][10]. - The proportion of companies reporting first-time losses decreased from 22% in 2024 to 14% in 2025, while the share of companies with ongoing losses increased from 28% to 35% [10][23]. Sub-sector Analysis - The textile manufacturing sector has a pre-profit rate of 60%, with 6 companies expected to be profitable, while the apparel and home textile sector has a pre-profit rate of 48% [2][14]. - The apparel and home textile sector saw a slight increase in the proportion of companies expecting profit growth from 14% to 17%, while the textile manufacturing sector experienced a decline in this metric from 55% to 40% [16][23]. Investment Recommendations - For textile manufacturing, it is advised to focus on companies with cost and scale advantages as tariff agreements improve [3][23]. - In the apparel and home textile sector, companies with strong brand power are expected to benefit from a recovery in downstream demand [3][23]. - In the jewelry sector, while high gold prices may suppress short-term demand, long-term improvements in craftsmanship are anticipated to enhance market penetration [3][23].

从年薪的变动来看，多数上市公司调增了董秘年薪，其中健盛集团董秘张望望年薪增幅最大，年薪由 2023年的43.05万年薪涨至115.08万，同比增长167%。凤竹纺织董秘陈美珍年薪降幅最大，由2023年的 47.21万降至28.98万元，同比下降38.61%。 从年龄角度看，纺织行业上市公司董秘年龄分布跨度较大，其中年龄最大的为朗姿股份董秘王建优，出 生于1963年，年龄为62岁；最年轻的为汇洁股份董秘蔡晓丽，出生于1993年，年仅32岁。 从学历来看，行业整体来看，CFO中学历多为本科，仅有少数上市公司董秘为专科学历。其中，华纺股 份、*ST金比、扬州金泉、富春染织、金春股份、江南高纤、万里马董秘为大专学历，朗姿股份董秘王 建优为博士学历。 董秘作为上市公司治理的核心角色，其专业能力和履职表现对公司股价的影响至关重要。在为董秘支付 超百万年薪的上市公司中，太平鸟、戎美股份市值连续四年下降。从二级市场股价表现来看，太平鸟 2021年-2024年股价接连下挫，年度股价变动分别为-5.55%、-31.88%、-6.54%、-10.12%。戎美股份2021 年-2024年股价接连下挫，年度股价变动分别为-21.89% ...

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 数据显示，截止7月29日，A股市场共有5817家上市公司。董秘作为连接投资者与上市公司的"桥梁"， 在上市公司资本运作中发挥着关键作用。据2024年年报显示，去年A股董秘薪酬合计达40.86亿元，平均 薪酬75.43万元。 分行业来看，在纺织服饰行业中，，上市公司为董秘支付的最高年度薪酬是235.33万元，为董秘支付的 最低年度薪酬是12.84万元，行业平均年薪为75万元。 从年薪的变动来看，多数上市公司调增了董秘年薪，其中健盛集团董秘张望望年薪增幅最大，年薪由 2023年的43.05万年薪涨至115.08万，同比增长167%。凤竹纺织董秘陈美珍年薪降幅最大，由2023年的 47.21万降至28.98万元，同比下降38.61%。 从年龄角度看，纺织行业上市公司董秘年龄分布跨度较大，其中年龄最大的为朗姿股份董秘王建优，出 生于1963年，年龄为62岁；最年轻的为汇洁股份董秘蔡晓丽，出生于1993年，年仅32岁。 从学历来看，行业整体来看，CFO中学历多为本科，仅有少数上市公司董秘为专科学历。其中，华纺股 份、*ST金比、扬州金泉、富春染织 ...

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 数据显示，截止7月29日，A股市场共有5817家上市公司。董秘作为连接投资者与上市公司的"桥梁"， 在上市公司资本运作中发挥着关键作用。据2024年年报显示，去年A股董秘薪酬合计达40.86亿元，平均 薪酬75.43万元。 分行业来看，在纺织服饰行业中，，上市公司为董秘支付的最高年度薪酬是235.33万元，为董秘支付的 最低年度薪酬是12.84万元，行业平均年薪为75万元。 从2024年薪总额看，太平鸟董秘王青林年薪最高为235万元，瑞贝卡（维权）董秘胡丽平年薪最低仅为 12.84万元。其中为董秘发放超百万年薪的上市公司共有10家，分别为恒辉安防、嘉欣丝绸、健盛集 团、报喜鸟、锦泓集团、洪兴股份、华利集团、戎美股份、汇洁股份、太平鸟。其中，汇洁股份董秘蔡 晓丽、太平鸟董秘王青林的年薪更是超过200万，分别为215.82万元、235.33万元。 | 公司名称 | 董易姓名 | 2024年薪酬 | 增减 | | --- | --- | --- | --- | | 恒辉安防 | 张武芬 | 103.32 | 2.82 | | 嘉欣丝绸 | 郑 ...

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 从年薪的变动来看，多数上市公司调增了董秘年薪，其中健盛集团董秘张望望年薪增幅最大，年薪由 2023年的43.05万年薪涨至115.08万，同比增长167%。凤竹纺织董秘陈美珍年薪降幅最大，由2023年的 47.21万降至28.98万元，同比下降38.61%。 从年龄角度看，纺织行业上市公司董秘年龄分布跨度较大，其中年龄最大的为朗姿股份董秘王建优，出 生于1963年，年龄为62岁；最年轻的为汇洁股份董秘蔡晓丽，出生于1993年，年仅32岁。 从学历来看，行业整体来看，CFO中学历多为本科，仅有少数上市公司董秘为专科学历。其中，华纺股 份、*ST金比、扬州金泉、富春染织、金春股份、江南高纤、万里马董秘为大专学历，朗姿股份董秘王 建优为博士学历。 董秘作为上市公司治理的核心角色，其专业能力和履职表现对公司股价的影响至关重要。在为董秘支付 超百万年薪的上市公司中，太平鸟、戎美股份市值连续四年下降。从二级市场股价表现来看，太平鸟 2021年-2024年股价接连下挫，年度股价变动分别为-5.55%、-31.88%、-6.54%、-10.12%。戎美 ...

Core Viewpoint - On July 30, the A-share market in Shanghai and Shenzhen showed a significant disparity in stock performance, with a total of 1,632 stocks rising and 3,376 stocks falling, indicating a bearish market sentiment overall [1] Industry Summary - The stocks that hit the upper limit of their trading range were primarily concentrated in the following sectors: pharmaceuticals and biotechnology, textiles and apparel, machinery and equipment, food and beverage, and light industry manufacturing [1]

Quantitative Factors and Construction Factor Name: GAN_GRU Factor - **Construction Idea**: The GAN_GRU factor is derived by processing volume-price time-series features using a Generative Adversarial Network (GAN) model, followed by encoding these time-series features with a Gated Recurrent Unit (GRU) model to generate a stock selection factor [4][13][41] - **Construction Process**: 1. **Input Features**: 18 volume-price features such as closing price, opening price, turnover, and turnover rate are used as input data. These features are sampled every 5 trading days over the past 400 days, resulting in a feature matrix of shape (40,18) [14][17][18] 2. **Data Preprocessing**: - Outlier removal and standardization are applied to each feature over the 40-day time series - Cross-sectional standardization is performed at the stock level [18] 3. **GAN Model**: - **Generator**: An LSTM-based generator is used to preserve the sequential nature of the input features. The generator takes random noise (e.g., Gaussian distribution) as input and generates data that mimics the real data distribution [23][33][37] - **Discriminator**: A CNN-based discriminator is employed to classify real and generated data. The discriminator uses convolutional layers to extract features from the 2D volume-price time-series "images" [33][35] - **Loss Functions**: - Generator Loss: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability for the generated data being real [24] - Discriminator Loss: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output probability for real data, and \( D(G(z)) \) is the discriminator's output probability for generated data [27] 4. **GRU Model**: - Two GRU layers (GRU(128,128)) are used to encode the time-series features, followed by an MLP (256,64,64) to predict future returns [22] 5. **Factor Output**: The predicted returns (\( pRet \)) from the GRU+MLP model are used as the stock selection factor. The factor is neutralized for industry and market capitalization effects and standardized [22] Factor Evaluation - The GAN_GRU factor effectively captures the sequential and cross-sectional characteristics of volume-price data, leveraging the strengths of GANs for feature generation and GRUs for time-series encoding [4][13][41] --- Factor Backtesting Results GAN_GRU Factor Performance Metrics - **IC Mean**: 11.43% (2019-2025), 10.97% (last year), 9.27% (latest month) [41][42] - **ICIR**: 0.89 [42] - **Turnover Rate**: 0.82 [42] - **Annualized Return**: 38.52% [42] - **Annualized Volatility**: 23.82% [42] - **IR**: 1.62 [42] - **Maximum Drawdown**: 27.29% [42] - **Annualized Excess Return**: 24.86% [41][42] GAN_GRU Factor Industry Performance - **Top 5 Industries by IC (Latest Month)**: - Home Appliances: 27.00% - Non-Bank Financials: 23.08% - Retail: 20.01% - Steel: 14.83% - Textiles & Apparel: 13.64% [41][42] - **Top 5 Industries by IC (Last Year)**: - Utilities: 14.43% - Retail: 13.33% - Non-Bank Financials: 13.28% - Steel: 13.23% - Telecommunications: 12.36% [41][42] GAN_GRU Factor Long Portfolio Performance - **Top 5 Industries by Excess Return (Latest Month)**: - Textiles & Apparel: 5.19% - Utilities: 3.62% - Automobiles: 3.29% - Non-Bank Financials: 2.56% - Pharmaceuticals: 1.47% [2][43] - **Top 5 Industries by Average Monthly Excess Return (Last Year)**: - Home Appliances: 5.44% - Building Materials: 4.70% - Textiles & Apparel: 4.19% - Agriculture: 4.09% - Utilities: 3.92% [2][43]

Group 1 - On July 28, a total of 13 companies were investigated by institutions, with 9 companies being surveyed by funds, indicating a strong interest in these firms [1] - Among the surveyed companies, Shenghong Technology received the most attention, with 31 funds participating in its investigation, while Cuihua Jewelry and Maidi Technology were investigated by 11 and 10 funds respectively [1] - The surveyed companies included 3 from the Shenzhen Main Board, 5 from the ChiNext, and 1 from the Shanghai Main Board [1] Group 2 - The total market capitalization of the surveyed companies included 1 company with a market cap over 500 billion, and Shenghong Technology had a market cap exceeding 1 trillion [1] - In terms of market performance, 3 out of the surveyed stocks increased in value over the past 5 days, with Shenghong Technology leading at a rise of 15.79%, followed by Hengshuai Co. and Fengmao Co. with increases of 7.14% and 4.62% respectively [1] - Conversely, 6 stocks experienced declines, with Maidi Technology, Zhongmi Holdings, and Cuihua Jewelry seeing drops of 5.65%, 3.46%, and 0.76% respectively [1] Group 3 - Among the surveyed stocks, 4 experienced net capital inflows over the past 5 days, with Shenghong Technology attracting 1.014 billion yuan, the highest among them [2] - Other companies with significant net inflows included Hengshuai Co. and Cuihua Jewelry, with inflows of 36.95 million yuan and 19.90 million yuan respectively [2] - In terms of performance forecasts, only one company provided a half-year earnings forecast, with Maidi Technology expecting a net profit of 26 million yuan, reflecting a year-on-year increase of 134.06% [2]

Market Overview - On July 28, the Shanghai Composite Index rose by 0.12%, the CSI 300 increased by 0.21%, the STAR Market 50 gained 0.09%, the CSI 1000 was up by 0.35%, the ChiNext Index climbed by 0.96%, and the Hang Seng Index increased by 0.68% [3][4] - The best-performing sectors on July 28 were defense and military (+1.86%), non-bank financials (+1.51%), pharmaceutical and biological (+1.47%), comprehensive (+1.29%), and communication (+1.24%). The worst-performing sectors were coal (-2.6%), steel (-1.41%), transportation (-1.38%), oil and petrochemicals (-1.02%), and textiles and apparel (-0.93%) [3][4] - The total trading volume for the A-share market on July 28 was 1.7662 trillion yuan, with a net inflow of 9.253 billion Hong Kong dollars from southbound funds [3][4] Key Insights - The report emphasizes a focus on consumption and growth styles, with industry attention on electric equipment, non-ferrous metals, pharmaceuticals, electronics, and brokerage firms [5] - The report suggests that under the current monetary environment, the "dumbbell strategy" remains effective, but the large-cap growth style may attract market attention in the short term [5] - Factors driving this outlook include strong support from hydropower projects and policy catalysts such as "anti-involution" and Hainan's customs closure, which have impacted the previously strong dumbbell strategy [5] - The report recommends increasing focus on mid-to-large-cap growth styles in August, particularly in sectors related to consumption and growth, as well as electric equipment and non-ferrous metals influenced by industry trends in pharmaceuticals (innovative drugs, AI healthcare) and electronics [5]

产业债：6月各行业AAA级产业债信用利差有涨有跌。公募债中，金融控股行业利差收窄幅度最大，纺织服饰行业利差走阔幅度最大；私募债 中，医药生物行业利差收窄幅度最大，公用事业行业利差走阔幅度最大。 城投债：6月，主要评级、期限城投债信用利差走势分化，低评级利差持续收窄，中高等级利差波动上行。分区域看，5月各省份、各主体级别 城投债信用利差多数收窄，私募债利差收窄幅度较大。 6月，信用债收益率整体下行，短久期信用债信用利差多数走阔，中长久期信用债利差多数收窄。市场继续通过拉长久期、下沉资质和挖掘品 种利差增厚收益，当月各品种信用债等级利差和期限利差多数收窄。 摘要 | 6 月末利差 (bp) | 1 से | | | | 3 सेंट | | ਦੇ ਲੇ | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | AAA | AA+ | AA | AA- AAA | AA+ | AA AA- AAA | AA+ | AA | AA- | | 公开产业绩 | 22.86 29.86 | | 33.86 | 79.86 25.32 32 ...