Core Viewpoint - China Minsheng Bank is implementing a fiscal interest subsidy policy for personal consumption loans to reduce consumer credit costs and stimulate consumer spending potential from September 1, 2025, to August 31, 2026 [1] Group 1: Fiscal Subsidy Execution Period and Scope - The subsidy applies to personal consumption loans used for consumption, excluding credit card transactions, with identifiable consumption transaction information [1] - The subsidy covers single transactions below 50,000 yuan and significant expenditures in key areas such as automobiles, education, and healthcare [1] Group 2: Fiscal Subsidy Standards - The annual subsidy rate is set at 1 percentage point, with a maximum of 50% of the loan contract interest rate [2] - The total subsidy limit for each individual customer is capped at 3,000 yuan, with a specific limit of 1,000 yuan for transactions below 50,000 yuan [2] Group 3: Fiscal Subsidy Rules - Customers must authorize China Minsheng Bank to access transaction information to qualify for the subsidy, which can be done through the bank's mobile app [3] - The signing of the subsidy agreement does not affect the normal application and use of personal consumption loans, but failure to sign will result in ineligibility for the subsidy [4] Group 4: Consumption Transaction Identification - The bank will identify transactions through system recognition or manual review to determine eligibility for the subsidy [5] Group 5: Subsidy Loan Interest Calculation and Settlement - The bank will calculate the subsidy amount daily based on the stipulated rate and limits, deducting it from the loan interest charged [6] - If a loan that has received a subsidy is later deemed ineligible, the bank reserves the right to reclaim the subsidy amount [6] Group 6: Additional Information - China Minsheng Bank will not charge any fees for processing the subsidy and advises customers to be cautious of potential fraud [7]

Core Viewpoint - The mid-term profit distribution plans of listed banks in A-shares for 2025 show a significant increase in total dividends, reaching 237.54 billion yuan, with state-owned banks being the primary contributors [2][3][4]. Group 1: Dividend Distribution Overview - Among 42 listed banks, 17 have announced their mid-term dividend plans for 2025, with a total dividend amount of 237.54 billion yuan [2][3]. - The six major state-owned banks contributed 204.66 billion yuan, accounting for 86% of the total dividends announced by the 17 banks [3][4]. - Industrial and Commercial Bank of China leads with a dividend of 50.40 billion yuan, followed by China Construction Bank and Agricultural Bank of China with 48.61 billion yuan and 41.82 billion yuan respectively [3][4]. Group 2: Factors Influencing Dividend Decisions - The ability of state-owned banks to distribute dividends is supported by their strong capital strength, stable profitability, and ample cash flow, allowing them to maintain high dividend payouts [4][9]. - The decision to distribute dividends is influenced by a balance of capital adequacy, business expansion needs, regulatory requirements, and shareholder return expectations [4][9][10]. - Some banks, such as Zhengzhou Bank and Qingdao Rural Commercial Bank, have explicitly stated they will not distribute dividends for the first half of 2025, citing performance pressures and capital replenishment needs [8][9]. Group 3: Trends in Dividend Distribution - The trend of increasing mid-term and quarterly dividends among listed banks has been noted since the introduction of the new "National Nine Articles" policy, which encourages multiple dividend distributions within a year [2][4]. - Several joint-stock banks, including CITIC Bank and Minsheng Bank, have announced their mid-term dividend plans, with CITIC Bank aiming for a dividend payout ratio of 30.7% [4][6]. - The distribution landscape shows a clear differentiation, with some banks actively pursuing dividends while others pause due to various operational challenges [8][9].

Core Viewpoint - The continuous decline in deposit rates, coupled with the concentration of fixed deposits maturing, is expected to significantly improve the cost of liabilities for banks [1][8]. Group 1: Deposit Trends - Recent reports indicate a trend of residents moving deposits from banks to other financial products such as funds and wealth management products [2][3]. - As of mid-2025, the total personal deposit balance of 13 major commercial banks reached 112.07 trillion yuan, an increase of 11.9 trillion yuan year-on-year [4][5]. - The average cost of deposits for these banks in the first half of 2025 was 1.61%, a decrease of 34 basis points compared to the same period in 2024 [12]. Group 2: Wealth Management Business Growth - The shift of deposits to wealth management products has led to significant growth in banks' wealth management income, with Agricultural Bank's wealth management income increasing by 62.3% [6]. - The total scale of bank wealth management products reached 30.67 trillion yuan by the end of June, with an estimated increase of about 2 trillion yuan by the end of July [6]. Group 3: Interest Margin and Cost of Liabilities - Despite the reduction in deposit costs, banks are still facing pressure on net interest margins, which have decreased to 1.42% as of the second quarter of 2025 [15][16]. - The average net interest margin for the 13 banks was 1.5%, down from 1.62% year-on-year [15]. - The decline in net interest margins is attributed to factors such as the reduction in the Loan Prime Rate (LPR) and adjustments in existing mortgage rates [17][18]. Group 4: Future Outlook - Analysts predict that the concentration of maturing fixed deposits will lead to a significant reduction in the cost of liabilities for banks in the coming years, with expected decreases of 17 to 24 basis points across different types of banks [11]. - The trend of increasing fixed deposits is expected to continue, with the proportion of fixed deposits among total deposits rising to approximately 59.7% in the first half of 2025 [9][10].

Quantitative Models and Construction Methods - **Model Name**: GAN_GRU **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for processing volume-price time-series features and Gated Recurrent Unit (GRU) for encoding time-series features to create a stock selection factor[4][13][41] **Model Construction Process**: 1. **GRU Component**: - Input features include 18 volume-price features such as closing price, opening price, turnover, and turnover rate[14][17][19] - Training data consists of the past 400 days of these features, sampled every 5 trading days, forming a 40x18 matrix to predict cumulative returns over the next 20 trading days[18] - Data preprocessing includes outlier removal and normalization at both time-series and cross-sectional levels[18] - Model architecture: Two GRU layers (128, 128) followed by an MLP (256, 64, 64), with the final output being the predicted return (pRet), which serves as the stock selection factor[22] - Training method: Semi-annual rolling training, with training conducted on June 30 and December 31 each year[18] - Optimization: Adam optimizer, learning rate of 1e-4, IC loss function, early stopping after 10 epochs, and a maximum of 50 training epochs[18] 2. **GAN Component**: - GAN consists of a generator (G) and a discriminator (D)[23] - Generator: Uses LSTM to preserve the time-series nature of the input features, transforming random noise into realistic data samples[33][37] - Loss function: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability[24][25] - Discriminator: Uses CNN to process the two-dimensional volume-price time-series features, distinguishing between real and generated data[33][37] - Loss function: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output for real data, and \( D(G(z)) \) is the output for generated data[27][29] - Training: Alternating updates of the generator and discriminator parameters until convergence[30] **Model Evaluation**: The GAN_GRU model effectively captures both time-series and cross-sectional features, leveraging the strengths of GAN and GRU for stock selection[4][13][41] --- Model Backtesting Results - **GAN_GRU Model**: - **IC Mean**: 11.36%[41][42] - **ICIR (Non-Annualized)**: 0.88[42] - **Turnover Rate**: 0.83[42] - **Recent IC**: -2.56%[41][42] - **1-Year IC Mean**: 8.94%[41][42] - **Annualized Return**: 38.09%[42] - **Annualized Volatility**: 23.68%[42] - **IR**: 1.61[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 23.52%[41][42] --- Quantitative Factors and Construction Methods - **Factor Name**: GAN_GRU Factor **Factor Construction Idea**: Derived from the GAN_GRU model, this factor encodes volume-price time-series features to predict stock returns[4][13][41] **Factor Construction Process**: - The factor is generated using the output of the GAN_GRU model, which combines GAN-based feature generation and GRU-based time-series encoding[4][13][41] - The factor undergoes industry and market capitalization neutralization, as well as standardization, before being used for testing[22] **Factor Evaluation**: The GAN_GRU factor demonstrates strong predictive power across various industries, with consistent outperformance in recent years[4][13][41] --- Factor Backtesting Results - **GAN_GRU Factor**: - **IC Mean**: 11.36%[41][42] - **ICIR (Non-Annualized)**: 0.88[42] - **Turnover Rate**: 0.83[42] - **Recent IC**: -2.56%[41][42] - **1-Year IC Mean**: 8.94%[41][42] - **Annualized Return**: 38.09%[42] - **Annualized Volatility**: 23.68%[42] - **IR**: 1.61[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 23.52%[41][42]

Core Viewpoint - Shanghai's new housing policy has led to a reduction in mortgage rates for existing loans and a minimum rate of 3.09% for new second-home loans, aligning them with first-home rates [1][3]. Group 1: New Mortgage Rates - The new policy eliminates the distinction between first and second home mortgage rates in Shanghai, with the specific rate determined by the market rate pricing mechanism and individual bank conditions [2][10]. - The minimum mortgage rate for new second-home loans in Shanghai is set at 3.09%, which is consistent with the first-home loan rate [3][2]. Group 2: Existing Mortgage Adjustments - Existing mortgage rates can be adjusted for eligible borrowers, particularly if their current rate exceeds the national average by more than 30 basis points [4][11]. - For example, a second-home loan with a current rate of 3.45% could potentially be reduced to 3.36% [6][4]. - The adjustment process will not incur any fees and will begin on September 1, 2025 [7][14]. Group 3: Implementation and Communication - Banks in Shanghai, including major institutions like ICBC and Bank of China, have issued announcements regarding the new mortgage rate adjustments [1][9]. - Borrowers can check their eligibility for rate adjustments through their respective banks starting September 1, 2025 [12][13].

8月29日，民生银行公布了2025年中期业绩报告。相关数据显示，报告期内，该行经营稳中有进，核心 指标表现稳健。 财务数据方面，民生银行上半年实现营业收入723.84亿元，同比增长7.83%；归属于本行股东的净利润 213.80亿元，同比下降4.87%。截至6月末，资产总额约7.77万亿元，降幅为0.59%；发放贷款和垫款总 额约4.47万亿元，增幅0.44%；吸收存款总额约4.31万亿元，较上年末增1.46%。 负债数据方面，截至6月末，民生银行负债总额70666.09亿元，比上年末下降917.92亿元，降幅1.28%。 在资产质量方面，截止六月末，不良贷款总额660.52亿元，比上年末增加4.42亿元；不良贷款率 1.48%，比上年末上升0.01个百分点；拨备覆盖率145.06%，比上年末上升3.12个百分点。 值得注意的是，民商银行的房地产业务不良率也在今年上半年出现了下降。 截至6月末，民生银行对公房地产相关的贷款、表外授信、标准债权投资、非标债权投资、债券投资等 承担信用风险的授信业务余额3906.53亿元，比上年末减少45.11亿元，降幅1.14%。其中，房地产业不 良贷款余额115.86亿元，比 ...

（民生银行） 8月29日，民生银行半年度业绩报告中披露的信息显示，截至报告期末，民生加银基金资产总额24.86亿 元，净资产19.59亿元。公司资产管理总规模2,139.88亿元，比上年末增长15.31%。 ...

Core Insights - The core viewpoint of the article highlights the financial performance of Minsheng Bank for the first half of the year, showcasing significant growth in both assets and profits [1] Financial Performance - As of the end of the reporting period, Minsheng Bank's total assets reached 90.78 billion yuan, with net assets amounting to 87.40 billion yuan [1] - The bank achieved a net profit of 5.31 billion yuan during the reporting period [1] - The scale of wealth management products at the end of the reporting period was 11,380.84 billion yuan, reflecting a year-on-year growth of 12.05% [1]

Core Viewpoint - The bank emphasizes high-quality development and strategic execution, focusing on customer-centric operations, risk control, and digital transformation to enhance overall business performance [15][16][17]. Company Overview - The bank's total assets reached 77,689.21 billion RMB, a decrease of 460.48 billion RMB or 0.59% from the previous year [16]. - The total liabilities amounted to 70,666.09 billion RMB, down by 917.92 billion RMB or 1.28% [16]. - The bank's net profit attributable to shareholders was 213.80 billion RMB, a decrease of 10.94 billion RMB or 4.87% year-on-year [17]. Financial Performance - Operating income increased to 723.84 billion RMB, up by 52.57 billion RMB or 7.83% year-on-year [17]. - Net interest income was 492.03 billion RMB, reflecting a year-on-year increase of 6.21 billion RMB or 1.28% [17]. - Non-interest income reached 231.81 billion RMB, a rise of 46.36 billion RMB or 25.00% compared to the previous year [17]. Risk Management - The bank's non-performing loan (NPL) ratio stood at 1.48%, slightly up by 0.01 percentage points from the previous year [18]. - The provision coverage ratio improved to 145.06%, an increase of 3.12 percentage points year-on-year [18]. Business Review - The bank served 12.19 million technology enterprises, with 2.80 million being specialized and innovative clients [20]. - Green finance initiatives included issuing 100 billion RMB in green bonds, with a green loan balance of 3,264.85 billion RMB, up by 286.81 billion RMB or 9.63% [22]. - Inclusive finance efforts resulted in a balance of 6,667.51 billion RMB in loans to small and micro enterprises, with 48.29 million clients served [24]. Retail Banking - Retail customer assets totaled 31,539.76 billion RMB, an increase of 2,077.29 billion RMB or 7.05% [36]. - The number of retail customers reached 139.52 million, growing by 3.89% year-on-year [37]. - Retail loans amounted to 17,232.78 billion RMB, a decrease of 274.83 billion RMB or 1.57% [36]. Digital Transformation - The bank launched 152 key features on its digital platform, enhancing service capabilities and customer experience [27][28]. - Online retail platform users reached 124.49 million, a growth of 3.18% from the previous year [28]. International Business - The Hong Kong branch's total assets grew to 236.93 billion HKD, an increase of 11.83% year-on-year [52]. - The branch's net income was 1.648 billion HKD, reflecting a year-on-year growth of 22.62% [52].

Core Viewpoint - The banking sector demonstrated stability and resilience in the first half of 2025, achieving a revenue of over 2.9 trillion yuan and a net profit of 1.1 trillion yuan, while focusing on supporting the real economy and preparing for digital transformation [1] Group 1: Financial Performance - A total of 42 A-share listed banks reported a revenue exceeding 2.9 trillion yuan, with a year-on-year growth of over 1% [1] - The net profit attributable to shareholders reached 1.1 trillion yuan, reflecting a year-on-year increase of 0.8% [1] - The six major state-owned banks collectively achieved a revenue of 1.8 trillion yuan and a net profit of 682.52 billion yuan in the first half of 2025 [3] Group 2: Asset and Liability Management - The total asset scale of the six major banks reached approximately 214 trillion yuan, an increase of about 7% compared to the end of the previous year [3] - The total asset scale of nine listed joint-stock banks was approximately 72 trillion yuan, growing by 2.37% [3] - The Industrial and Commercial Bank of China (ICBC) reported an asset scale of 52 trillion yuan, leading the industry in customer loans and deposits [3] Group 3: Dividend Distribution - The six major state-owned banks announced a total cash dividend exceeding 204.65 billion yuan for the first half of 2025 [2][4] Group 4: Digital Transformation - The application of artificial intelligence (AI) has become a key driver for the banks' transformation, with various banks launching AI initiatives and projects [5] - ICBC has initiated the "AI+" action, while Agricultural Bank of China is advancing its "AI+" applications [5] - By the end of June, ICBC had implemented over 100 AI application scenarios across key business areas [5] Group 5: Credit Growth and Focus on Real Economy - The total loan balance of 42 A-share listed banks reached approximately 180 trillion yuan, with a year-on-year growth of about 6% [6] - State-owned banks are the main contributors to credit issuance, with a loan balance exceeding 120 trillion yuan, growing by 6.59% [6] - Agricultural Bank of China reported a loan and advance total of 26.73 trillion yuan, with significant growth in manufacturing, green loans, and inclusive loans [7]