Group 1 - The article presents the rankings of various securities firms in different research categories as part of the 23rd New Fortune Best Analyst Awards [1][2][3] - The rankings are based on a phased statistical result as of August 11, indicating that they are not final [1][2] - The categories include macroeconomic research, strategy research, fixed income research, and sector-specific research such as real estate, food and beverage, and healthcare [1][2][3][4] Group 2 - In the macroeconomic research category, the top firms include GF Securities, Huachuang Securities, and Shenwan Hongyuan Securities [1] - For strategy research, the leading firms are CITIC Securities, GF Securities, and Shenwan Hongyuan Securities [2] - In fixed income research, the top firms are Huatai Securities, Shenwan Hongyuan Securities, and GF Securities [3] Group 3 - The rankings for specific sectors show that in real estate, the top firms are Longjiang Securities, Shenwan Hongyuan Securities, and GF Securities [6] - In the food and beverage sector, the leading firms are GF Securities, Shenwan Hongyuan Securities, and CITIC Securities [9] - For healthcare, the top firms include Industrial Securities, CITIC Securities, and Tianfeng Securities [7]

陈顾君拥有10年证券及投资管理经验，历任海通证券、浙商证券量化研究员及基金经理助理，现任浙商 汇金量化臻选股票型基金、中证A500指数型基金经理。 8月11日，浙江浙商证券资产管理有限公司公告，浙商汇金新兴消费增聘陈顾君，叶方强离任。 截至2025年8月8日，浙商汇金新兴消费合计规模0.23亿元，年内收益率为15.25%，总回报为12.81%，累 计净值为1.1823元。 （基金公告、wind数据） wind数据统计显示，浙商汇金新兴消费成立于2020年5月29日，离任基金经理叶方强在管2.72年期间， 任职总回报13.79%，任职年化回报4.88%，同类排名501/2097。 ...

Quantitative Models and Construction Methods 1. Model Name: Multivariate CNN-LSTM - **Model Construction Idea**: The model leverages the advantages of CNN and LSTM in different scenarios to predict multiple parallel financial time series by considering the correlation between them[12][14]. - **Detailed Construction Process**: - **General Structure**: The model consists of an input layer, a one-dimensional convolutional layer, a pooling layer, an LSTM hidden layer, and a fully connected layer to produce the final prediction results[14]. - **Formula**: $$ {\hat{x}}_{k,t+h}=f_{k}(x_{1,t},\dots,x_{k,t},\dots,x_{1,t-1},\dots,x_{k,t-1},\dots) $$ This formula indicates that each variable depends not only on its past values but also on the past values of other variables[11]. - **Hyperparameters**: - Number of convolution filters: 64 - Convolution kernel size: 2 - Use of padding: Yes - Pooling layer window size: (2,2) - Number of hidden units in the first LSTM layer: 128 - Number of hidden units in the second LSTM layer: 128 - Activation method between LSTM layers: ReLU - Time series look-back window: 10 - Number of training epochs: 100[20] - **Evaluation Metric**: Root Mean Square Error (RMSE) $$ RMSE={\sqrt{\frac{1}{n}\sum_{i}({\hat{y_{i}}}-y_{i}\,)^{2}}} $$ where \( y_i \) represents the standardized index price, and \( \hat{y_i} \) represents the CNN-LSTM prediction value[21]. - **Model Evaluation**: The model achieved good tracking and high accuracy in predicting multiple parallel financial time series, similar to the performance in predicting stock indices in the Asia-Pacific market[14][17]. 2. Model Name: Grouped Multivariate CNN-LSTM - **Model Construction Idea**: To improve prediction accuracy, the industry indices are grouped based on investment attributes, and a separate prediction model is constructed for each group[26][27]. - **Detailed Construction Process**: - **Grouping**: The industry indices are divided into six groups: Consumer and Medicine, Upstream Resources and Materials, High-end Manufacturing, Real Estate and Infrastructure, Big Tech, and Big Finance[27]. - **Model Structure**: Each group of industry indices is predicted using a separate CNN-LSTM model, as shown in the general structure diagram[28]. - **Evaluation Metric**: The prediction accuracy is evaluated using RMSE, similar to the original model[33]. - **Model Evaluation**: Grouping and training different CNN-LSTM sub-models for each industry group improved the prediction accuracy, especially for industries with previously low prediction accuracy[30][32]. Model Backtesting Results 1. Multivariate CNN-LSTM Model - **Prediction Error (Training Phase)**: 1.52% to 3.18%[23] - **Prediction Error (Testing Phase)**: 1.56% to 3.30%[23][25] 2. Grouped Multivariate CNN-LSTM Model - **Prediction Error (Training Phase)**: 1.49% to 2.60%[33] - **Prediction Error (Testing Phase)**: 1.61% to 2.82%[33] Quantitative Factors and Construction Methods 1. Factor Name: Weekly Industry Rotation Signal - **Factor Construction Idea**: Use the predicted values from the multivariate CNN-LSTM model to estimate the future weekly returns of industry indices and select the top five industries with the highest expected returns for equal-weight allocation[3]. - **Detailed Construction Process**: - **Prediction**: Predict the future weekly returns of industry indices using the multivariate CNN-LSTM model[34]. - **Allocation**: Every five trading days, select the top five industries with the highest expected returns for equal-weight allocation[35]. - **Training**: Retrain the model at the beginning of each quarter using an extended window of historical data from March 2014 to the training point[35]. - **Factor Evaluation**: The annualized return of the industry rotation portfolio reached 15.6%, with an annualized excess return of approximately 11.6%, and the risk-return characteristics significantly improved compared to the benchmark[3][35]. Factor Backtesting Results 1. Weekly Industry Rotation Signal - **Annualized Return**: 15.6%[38] - **Annualized Volatility**: 25.6%[38] - **Maximum Drawdown**: -27.1%[38] - **Sharpe Ratio**: 0.7[38] - **Longest Drawdown Recovery Time**: 248 days[38]

北京市嘉源律师事务所 关于浙商证券股份有限公司 差异化分红事项的核查意见 ِ 师事务所 豆 旗在 YUAN LAW OFFICES 西城区复兴门内大街 158 号远洋大厦 4 楼 中国 · 北京 望师雪务所 YUAN LAW OFFICES 北京 BEIJING·上海 SHANGHAJ·深圳 SHENZHEN·香港 HONG KONG·广州 GUANGZHOU·西安 XI'AN 致:浙商证券股份有限公司 北京市嘉源律师事务所 关于浙商证券股份有限公司 差异化分红事项的核查意见 嘉源(2025)-05-261 敬启者: 本所接受浙商证券股份有限公司(以下简称"公司")的委托,就公司 2024年度利润分配涉及的差异化分红(以下简称"本次差异化分红")相关事项 出具本核查意见。 本核查意见依据《中华人民共和国公司法》(以下简称"《公司法》")、 《中华人民共和国证券法》(以下简称"《证券法》")、《上市公司股份回购规则》 (以下简称"《回购规则》")、《上海证券交易所股票上市规则》(以下简称 "《上市规则》")及《上海证券交易所上市公司自律监管指引第 7 号 -- 回购 股份》(以下简称"《回购指引》")等法律、法 ...

本公司董事会及全体董事保证本公告内容不存在任何虚假记载、误导性陈述或者重大遗 漏，并对其内容的真实性、准确性和完整性承担法律责任。 重要内容提示： 每股分配比例 A 股每股现金红利0.1元 相关日期 | 股份类别 | 股权登记日 | 最后交易日 | 除权（息）日 | 现金红利发放日 | | --- | --- | --- | --- | --- | | Ａ股 | 2025/8/18 | － | 2025/8/19 | 2025/8/19 | 证券代码：601878 证券简称：浙商证券 公告编号：2025-044 浙商证券股份有限公司 2024年年度权益分派实施公告 差异化分红送转： 是 一、 通过分配方案的股东大会届次和日期 本次利润分配方案经公司2025 年 6 月 26 日的2024年年度股东大会审议通过。 二、 分配方案 截至股权登记日下午上海证券交易所收市后，在中国证券登记结算有限责任公司上海分 公司（以下简称"中国结算上海分公司"）登记在册的本公司全体股东。 根据《上海证券交易所上市公司自律监管指引第 7 号——回购股份》第二十二条，上市 公司回购专用账户中的股份，不享有股东大会表决权、利润分配、公 ...

浙商汇金新兴消费成立于2020年5月29日，截至2025年8月8日，其今年来收益率为15.25%，成立来收益 率为12.81%，累计净值为1.7823元。 | 基金名称 | 浙商汇金新兴消费灵活配置混合型证券投资基 | | --- | --- | | | 金 | | 基金简称 | 浙商汇金新兴消费 | | 基金主代码 | 009527 | | 基金管理人名称 | 浙江浙商证券资产管理有限公司 | | 公告依据 | 《公开募集证券投资基金信息披露管理办法》 | | 基金经理变更类型 | 兼有增聘和解聘基金经理 | | 新任基金经理姓名 | 陈顾君 | | 共同管理本基金的其他基 | | | 金经理姓名 | | | 离任基金经理姓名 | 叶方弹 | 中国经济网北京8月11日讯今日，浙江浙商证券（601878）资产管理有限公司公告，浙商汇金新兴消费 增聘陈顾君，叶方强离任。 陈顾君历任上海海通证券资产管理有限公司量化研究员，曾任浙江浙商证券资产管理有限公司量化研究 员、基金经理助理、浙商汇金中证转型成长指数型证券投资基金的基金经理；现任浙商汇金量化臻选股 票型证券投资基金、浙商汇金中证A500指数型证券投资基金的基 ...

8月8日主力资金净流出477.04万元，占总成交额1.2%；游资资金净流出361.02万元，占总成交额 0.91%；散户资金净流入838.06万元，占总成交额2.11%。 以上内容为证券之星据公开信息整理，由AI算法生成（网信算备310104345710301240019号），不构成 投资建议。 交易信息汇总 交易信息汇总：8月8日主力资金净流出477.04万元，占总成交额1.2%；游资资金净流出361.02万 元，占总成交额0.91%；散户资金净流入838.06万元，占总成交额2.11%。 公司公告汇总：浙商证券股份有限公司2025年度第八期短期融资券成功发行，实际发行总额为10 亿元人民币，票面利率为1.67%。 截至2025年8月8日收盘，浙商证券(601878)报收于11.38元，下跌0.7%，换手率0.76%，成交量34.88万 手，成交额3.97亿元。 公司公告汇总 当日关注点 浙商证券股份有限公司2025年度第八期短期融资券已于2025年8月6日发行完毕。短期融资券名称为浙商 证券股份有限公司2025年度第八期短期融资券，发行简称为25浙商证券CP008，流通代码为 072510151。发行日为 ...

截至2025年8月8日收盘，浙商证券（601878）报收于11.38元，较上周的11.34元上涨0.35%。本周，浙 商证券8月7日盘中最高价报11.56元。8月4日盘中最低价报11.26元。浙商证券当前最新总市值520.5亿 元，在证券板块市值排名19/49，在两市A股市值排名277/5151。 本周关注点 公司公告汇总: 浙商证券完成发行10亿元短期融资券，期限365天，票面利率1.67% 公司公告汇总 浙商证券股份有限公司完成了2025年度第八期短期融资券的发行，简称为25浙商证券CP008，流通代码 为072510151。该融资券于2025年8月6日起息，兑付日期定于2026年8月6日，期限为365天。原计划发行 总额为20亿元人民币，但实际发行总额为10亿元人民币，票面利率设定为1.67%，发行价格为100元每 张。相关文件已在中国货币网和上海清算所网站上公布。此公告由浙商证券股份有限公司董事会于2025 年8月8日发布，董事会及全体董事对公告内容的真实性、准确性和完整性承担责任。 以上内容为证券之星据公开信息整理，由AI算法生成（网信算备310104345710301240019号），不构成 投资 ...

Core Insights - The report from Zhejiang Securities indicates that bank stocks can meet the insurance capital's requirements for "long-term stability and absolute returns" [1] - It is expected that over 500 billion yuan of insurance capital will be allocated to banks over the next three years, considering both incremental and existing premium inflows [1] - High dividend, high yield, and well-positioned bank stocks remain favorites among insurance capital [1]

Core Viewpoint - The company, Xizi Clean Energy Equipment Manufacturing Co., Ltd., is planning to repurchase its shares to enhance shareholder value and maintain investor confidence, which will involve using its own funds to buy back shares for cancellation, thereby reducing registered capital [1][2]. Group 1: Bond Information - The total face value of the convertible bonds issued by the company is 1.11 billion yuan [1][2]. - The bonds have a term of 6 years, with an interest rate that increases progressively from 0.30% in the first year to 2.00% in the sixth year [3]. - The current conversion price of the bonds is 11.00 yuan per share, while the initial conversion price was set at 28.08 yuan per share [5][3]. Group 2: Meeting Details - The first bondholders' meeting for the "Xizi Convertible Bonds" will be held online from August 4 to August 8, 2025, using a simplified procedure [6][7]. - The meeting will discuss a proposal regarding the company's share repurchase and will not require early repayment of the bond debt or additional guarantees [6]. - The proposal received unanimous approval from bondholders, with 100% in favor and no opposition or abstentions [6]. Group 3: Legal and Regulatory Compliance - The bondholders' meeting was witnessed by Zhejiang Jindao Law Firm, which confirmed that the meeting's procedures and voting were in compliance with relevant laws and regulations [6]. - The bondholders were given a period to raise objections, but no objections were received during the specified timeframe [6].