Core Viewpoint - The brokerage sector is experiencing significant gains, driven by increased client activity and favorable market conditions, indicating a potential new growth phase for the securities industry [1][3][4]. Group 1: Market Performance - The brokerage sector saw a surge in stock prices, with notable gains from companies like Guosheng Jinkong and Changcheng Securities, contributing to a positive market sentiment [1][3]. - The A-share market has been defined as a "bull market" by multiple brokerages, with expectations of increased capital inflow due to the anticipated interest rate cuts in the U.S. [6]. Group 2: Driving Factors - Three main drivers for the recent performance include an increase in T0 client numbers, steady growth in client margin scales, and a noticeable rise in leverage among existing clients, as evidenced by the financing balance exceeding 2.02 trillion yuan [1][3]. - Analysts suggest that policy guidance is encouraging long-term capital to enter the market, with structural adjustments indicating a turning point, which could enhance the growth potential for brokerage services [1][6]. Group 3: Future Outlook - The securities industry is expected to benefit from a new growth cycle, with increased demand for brokerage, margin financing, asset management, and investment banking services as new capital flows into the market [1][6]. - The improvement in asset quality and the reduction of valuation concerns are also contributing to a more favorable outlook for brokerages, with a focus on cost reduction and efficiency improvements [7].

浙商证券（601878）发布研报称，据中国工程机械工业协会，2025年7月销售各类挖掘机17138台，同比 增长25.2%。其中国内销量7306台，同比增长17.2%;出口量9832台，同比增长31.9%。中国工程机械行 业是全球优势产业(性价比等优势)，有望从进口替代逐步走向供应全球。该行建议持续聚焦行业龙头。 工程机械内需也有望边际改善，海外与国内有望共振，雅江、新藏铁路等项目促新增需求。 浙商证券主要观点如下： 7月挖掘机总销量同比增长25% 其中内销同比增长17%，外销同比增长32%。2025年7月挖掘机总体销量17138台，同比增长25.2%， 2024年4月-2025年7月连续16个月同比正增长;国内销量7306台，同比增长17.2%;出口销量9832台，同比 增长31.9%，2024年8月-2025年7月连续12个月正增长。 1)出口：全球化持续推进，海外市占率有望不断提升。中国工程机械行业是全球优势产业(性价比等优 势)，有望从进口替代逐步走向供应全球。据KHL，2024年全球Top50工程机械制造商总销售额2376亿美 元，其中徐工机械（000425）/三一重工（600031）/中联重科（0 ...

证券之星消息，8月11日，浙商证券（601878）融资买入1.2亿元，融资偿还1.19亿元，融资净买入 119.27万元，融资余额26.51亿元，近3个交易日已连续净买入累计827.79万元，近20个交易日中有12个 交易日出现融资净买入。 | 交易日 | 融券冷卖用(股) | | 融券余量(股) | 融券余额(元) | | --- | --- | --- | --- | --- | | 2025-08-11 | | 9600.0 | 6.40万 | 73.47万 | | 2025-08-08 | | 3500.0 | 5.44万 | 61.91万 | | 2025-08-07 | | -5300.0 | 5.09万 | 58.33万 | | 2025-08-06 | | -2600.0 | 5.62万 | 64.46万 | | 2025-08-05 | | 1.15万 | 5.88万 | 67.38万 | 融资融券余额26.52亿元，较昨日上涨0.05%。 | 交易日 | 两融余额(元) | 余额变动(元) | 变动幅度 | | --- | --- | --- | --- | | 2025-08-11 | ...

Core Points - The company announced a cash dividend of 0.1 yuan per share for the fiscal year 2024, approved at the annual shareholders' meeting on June 26, 2025 [1][2][4] Distribution Plan - The dividend distribution is for the fiscal year 2024 [2] - Eligible shareholders are those registered with the China Securities Depository and Clearing Corporation Limited Shanghai Branch as of the close of trading on the day before the equity registration date [2] - The company will distribute 1.00 yuan in cash dividends for every 10 shares held, excluding shares in the repurchase account [3][4] Calculation of Ex-Dividend Price - The ex-dividend reference price will be calculated as: (previous closing price - cash dividend) / (1 + change in circulating shares ratio) [4][6] - The virtual cash dividend per share is approximately 0.10 yuan based on the total shares eligible for distribution [4] Implementation Details - Cash dividends will be distributed through the China Securities Depository and Clearing Corporation's clearing system to eligible shareholders [6] - Shareholders who have not completed designated trading will have their dividends held by the clearing company until the trading is completed [6] Taxation Information - Individual shareholders holding shares for over one year will not be subject to personal income tax on dividends, while those holding for less than one year will have taxes withheld upon stock transfer [9] - For qualified foreign institutional investors (QFII), a 10% withholding tax will apply, resulting in a net dividend of 0.09 yuan per share [10] - Hong Kong investors will also face a 10% withholding tax, with a net dividend of 0.09 yuan per share [11]

Core Points - The company has approved a cash dividend of 0.1 yuan per share for its A shares, as decided in the 2024 annual shareholders' meeting held on June 26, 2025 [1] - The dividend distribution will be based on the total share capital minus the shares held in the company's repurchase account, with a distribution ratio of 1.00 yuan for every 10 shares held [1][2] - The ex-dividend date is set for August 19, 2025, with the record date being August 18, 2025 [2] Dividend Distribution Details - The cash dividend will be distributed to all shareholders registered with the China Securities Depository and Clearing Corporation Limited, Shanghai Branch, as of the close of trading on the record date [1][2] - The reference price for the ex-dividend will be calculated as the previous closing price minus the cash dividend, with no change in the circulating shares [2] - The company will not distribute dividends for shares held in the repurchase account [1][3] Taxation Information - For individual shareholders holding shares for over one year, the cash dividend income is exempt from personal income tax, resulting in a net distribution of 0.10 yuan per share [4] - For shares held for one month or less, a 20% tax will be applied, while shares held between one month and one year will incur a 10% tax [4][5] - For qualified foreign institutional investors (QFII), a 10% withholding tax will be applied, resulting in a net distribution of 0.09 yuan per share [5][6] Contact Information - For inquiries regarding the dividend distribution, shareholders can contact the board office of Zhejiang Merchants Securities Co., Ltd. at 0571-87901964 [6]

Core Viewpoint - The report from Zheshang Securities indicates that the A-share market is currently experiencing its first "systematic slow bull" since 2005, driven by improved risk appetite and declining risk-free interest rates, alongside China's rise and advantages [1][3]. Historical Context - Since the initiation of the stock reform in April 2005, the A-share market has undergone four bull markets, with the first three being "systematic bull markets" characterized by steep upward slopes, while the fourth was a "structural bull market" with a gentler slope. The fifth bull market is expected to commence in 2025 [2]. Macro Factors - The combination of enhanced risk appetite and declining risk-free interest rates is fostering a "systematic bull market." Key factors include supportive policies, a stable response to trade tensions, and recognition of China's military capabilities. Additionally, the significant drop in risk-free interest rates is likely to attract new capital into the A-share market [3]. Technical and Quantitative Factors - The report highlights four key factors supporting the "systematic slow bull": the stable appreciation of the RMB against the USD, the upward trend of the Shanghai Composite Index, the "rolling peak" structure of the index, and the divergence in sector performance, indicating a unique "systematic slow bull" [4]. Investment Recommendations - The investment strategy suggests a "1+X" allocation approach focusing on "big finance + broad technology" to enhance success rates, while also considering undervalued real estate and engineering machinery for higher returns. Additionally, it recommends focusing on innovative pharmaceuticals and renewable energy with external advantages, as well as banks that serve as defensive "ballast" [5].

（编辑 任世碧） 证券日报网讯 8月11日晚间，浙商证券发布2024年年度权益分派实施公告称，公司2024年年度权益分派 方案为A股每股现金红利0.1元（含税），股权登记日为2025年8月18日，除权（息）日为2025年8月19 日。 ...

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]

Core Insights - Zhejiang Zheshang Securities Asset Management Co., Ltd. announced the appointment of Chen Gujun to the management of the Zheshang Huijin Emerging Consumption Fund, while former manager Ye Fangqiang has stepped down [1] Fund Performance - The Zheshang Huijin Emerging Consumption Fund was established on May 29, 2020, and during Ye Fangqiang's tenure of 2.72 years, the total return was 13.79%, with an annualized return of 4.88%, ranking 501 out of 2097 in its category [1] - As of August 8, 2025, the total scale of the Zheshang Huijin Emerging Consumption Fund is 0.23 billion, with a year-to-date return of 15.25% and a total return of 12.81%, resulting in a cumulative net value of 1.1823 yuan [1]

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]