数据宝统计，截至14:20，重庆百货成交额10.02亿元，创2015年6月12日以来新高。最新股价上涨 9.97%，换手率18.31%。上一交易日该股全天成交额为1.47亿元。（数据宝） （文章来源：证券时报网） ...

1. Report Industry Investment Rating The provided content does not mention the industry investment rating. 2. Core Viewpoints of the Report - From January 4 - 9, 2026, bond market yields first rose and then fell. Interest - rate bond yields increased across the board, and credit bonds became a safe - haven in the bond market with narrowing credit spreads. AA and below credit spreads mostly narrowed by 3 - 6bp [1][9]. - After the holiday, bond fund redemptions led to large - scale selling of bonds in the secondary market, mainly interest - rate bonds and Tier 2 and perpetual bonds, with a slight increase of 54.1 billion yuan in general credit bonds. Currently, the redemptions of impulsive funds have ended [1][9]. - The allocation demand for general credit bonds from financial management, insurance, money funds, and other asset management institutions has rebounded. Most non - bank institutions are still cautious about the maturity of credit bonds they buy, but other asset management institutions have been snapping up 7 - 10 - year credit bonds since mid - December 2025 [2][10]. - In the short term, due to the increase in market risk appetite during the spring rally, it is difficult to have a trending bond market, and the market may still favor the coupon strategy. It is recommended to focus on medium - and short - term coupon - rich varieties [2][13]. - Bank Tier 2 and perpetual bonds still have room for repair. For trading portfolios, it is recommended to control positions; for allocation portfolios with stable liability ends, 3 - 5 - year Tier 2 and perpetual bonds have cost - effectiveness [3][21]. 3. Summary by Relevant Catalogs 3.1 City Investment Bonds: Jiangsu and Zhejiang County - Level Platforms Contribute to Net Financing Increment, Short - Term Bonds are Preferred - From January 1 - 11, 2026, the net financing of city investment bonds was positive, mainly contributed by county - level platforms in Jiangsu and Zhejiang. The primary issuance sentiment improved, and the proportion of full - field multiples above 3 times increased from 35% to 46% [25]. - In terms of issuance interest rates, there was a divergence among different terms. The medium - and short - term rates stabilized, while the long - term rates continued to rise. The weighted average issuance interest rates for different terms showed different trends compared to the previous month [25]. - In the secondary market, city investment bonds remained resilient. Low - grade short - term bonds performed well, while high - grade 3 - 5 - year bonds performed poorly. The average daily trading volume was relatively high, and short - term, weak - quality varieties were more popular [28][32]. 3.2 Industrial Bonds: Net Financing Decreased Year - on - Year, and the Issuance Interest Rates for Medium - and Long - Term Bonds Continued to Rise - From January 1 - 11, 2026, the issuance and net financing of industrial bonds decreased year - on - year. The food and beverage, construction and decoration, and comprehensive industries had relatively large net financing scales. The issuance sentiment improved [34]. - The proportion of short - term issuance increased significantly. The issuance interest rates for 3 - year and shorter terms decreased, while those for 3 - 5 - year and 5 - year and above terms increased. The buying sentiment from brokers continued to weaken, and the trading duration lengthened [34][36]. 3.3 Bank Tier 2 and Perpetual Bonds: Spreads Narrowed Across the Board, and Trading Sentiment Warmed Up - From January 4 - 9, 2026, there were no new bank Tier 2 and perpetual bonds issued. In the secondary market, yields generally declined by 0 - 4bp, and spreads narrowed across the board. The trading sentiment warmed up slightly [39][42].

Core Viewpoint - The article highlights the success of Longfeng Group in creating job opportunities and its recognition as a model private enterprise in Inner Mongolia for its contributions to employment and community welfare [1][2]. Group 1: Employment Creation - Longfeng Group has absorbed over 3,200 employees in the past three years, including 53 laid-off workers and 120 college graduates [1]. - The average monthly salary for employees at Longfeng Group is 4,500 yuan, which is 20% higher than the local average [1]. Group 2: Investment and Business Development - In 2025, Longfeng Group plans to invest 30 million yuan to upgrade its core commercial facilities, creating two major projects: "Cultural Tourism Street" and "Food Garden Town," which will introduce over 80 new business formats [1]. - The new projects are expected to generate over 100 million yuan in annual sales and support the establishment of more than 240 entrepreneurial projects [1]. Group 3: Employee Support and Development - The company has implemented a "Nebula Structure" management model, supporting over 30 employees in starting their own businesses and incubating 12 projects, including local service platforms and cultural creative studios [2]. - Longfeng Group collaborates with local universities for recruitment, providing internships and job opportunities to help students transition smoothly into the workforce [2]. Group 4: Community Engagement - The company has established a "green channel" for laid-off workers, simplifying the recruitment process and providing dedicated training to help them regain confidence [2]. - Longfeng Group actively engages with the community by offering flexible job positions such as cashiers and stock clerks, enabling residents to find employment close to home [2]. Group 5: Recognition and Future Plans - Longfeng Group has received over 40 honors, including the title of "China's Commercial Brand Enterprise" and the "May Day Labor Award" from Inner Mongolia [3]. - The company aims to continue expanding its business in trade, culture, and health sectors, creating more job opportunities and contributing to local economic development [3].

上海九百(600838)今日下跌2.53%，全天换手率28.55%，成交额15.80亿元，振幅14.04%。龙虎榜数据显 示，营业部席位合计净卖出6816.20万元。 上交所公开信息显示，当日该股因日换手率达28.55%上榜，营业部席位合计净卖出6816.20万元。 近半年该股累计上榜龙虎榜6次，上榜次日股价平均涨0.34%，上榜后5日平均涨4.55%。 资金流向方面，今日该股主力资金净流出9141.62万元，其中，特大单净流出2986.81万元，大单资金净 流出6154.81万元。近5日主力资金净流入7419.82万元。 融资融券数据显示，该股最新（1月8日）两融余额为3.60亿元，其中，融资余额为3.60亿元，融券余额 为42.27万元。近5日融资余额合计增加1.01亿元，增幅为38.77%。融券余额合计增加3.37万元，增幅 8.65%。 2025年10月28日公司发布的三季报数据显示，前三季度公司共实现营业收入6357.45万元，同比下降 3.81%，实现净利润2842.47万元，同比增长2.91%。（数据宝） 上海九百1月9日交易公开信息 | 买/ | 会员营业部名称 | 买入金额（万 | 卖出金额 ...

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to construct a stock selection factor[4][13][14] - **Model Construction Process**: 1. **GAN Component**: - The generator (G) learns the real data distribution and generates realistic samples from random noise \( z \) (Gaussian or uniform distribution). The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( D(G(z)) \) represents the discriminator's probability of classifying generated data as real[24][25][26] - The discriminator (D) distinguishes real data from generated data. Its loss function is: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( D(x) \) is the probability of real data being classified as real, and \( D(G(z)) \) is the probability of generated data being classified as real[27][29][30] - GAN training alternates between optimizing \( G \) and \( D \) until convergence[30] 2. **GRU Component**: - Two GRU layers (GRU(128, 128)) are used to encode time-series features, followed by a Multi-Layer Perceptron (MLP) with layers (256, 64, 64) to predict returns. The final output \( pRet \) is used as the stock selection factor[22] 3. **Feature Input and Processing**: - Input features include 18 price-volume characteristics (e.g., closing price, turnover, etc.) sampled over the past 400 days, with a shape of \( 40 \times 18 \) (40 days of features)[18][19][37] - Features undergo outlier removal, standardization, and cross-sectional normalization[18] 4. **Training Details**: - Training-validation split: 80%-20% - Semi-annual rolling training (June 30 and December 31 each year) - Hyperparameters: batch size equals the number of stocks, Adam optimizer, learning rate \( 1e-4 \), IC loss function, early stopping (10 rounds), max training rounds (50)[18] 5. **Stock Selection**: - Stocks are filtered to exclude ST stocks and those listed for less than six months[18] - **Model Evaluation**: The GAN_GRU model effectively captures price-volume time-series features and demonstrates strong predictive power for stock returns[4][13][22] --- Model Backtesting Results 1. GAN_GRU Model - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] --- Quantitative Factors and Construction Methods 1. Factor Name: GAN_GRU Factor - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, leveraging GAN for price-volume feature generation and GRU for time-series encoding[4][13][14] - **Factor Construction Process**: - The GAN generator processes raw price-volume time-series features (\( Input\_Shape = 40 \times 18 \)) and outputs transformed features with the same shape (\( Input\_Shape = 40 \times 18 \))[37] - The GRU component encodes these features into a predictive factor for stock selection[22] - The factor undergoes industry and market capitalization neutralization and standardization[22] - **Factor Evaluation**: The GAN_GRU factor demonstrates robust performance across various industries and time periods, with significant IC values and excess returns[4][41] --- Factor Backtesting Results 1. GAN_GRU Factor - **IC Mean**: 0.1119*** (2019-2025)[4][41] - **ICIR (non-annualized)**: 0.89[42] - **Turnover Rate**: 0.83X[42] - **Recent IC**: 0.0331*** (December 2025)[4][41] - **1-Year IC Mean**: 0.0669***[4][41] - **Annualized Return**: 37.40%[42] - **Annualized Volatility**: 23.39%[42] - **IR**: 1.60[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.42%[4][42] 2. Industry-Specific Performance - **Top 5 Industries by Recent IC (October 2025)**: - Social Services: 0.4243*** - Coal: 0.2643*** - Environmental Protection: 0.2262*** - Retail: 0.1888*** - Steel: 0.1812***[4][41][42] - **Top 5 Industries by 1-Year IC Mean**: - Social Services: 0.1303*** - Steel: 0.1154*** - Non-Bank Financials: 0.1157*** - Retail: 0.1067*** - Building Materials: 0.1017***[4][41][42] 3. Industry-Specific Excess Returns - **Top 5 Industries by December 2025 Excess Returns**: - Banking: 4.30% - Real Estate: 3.51% - Environmental Protection: 2.18% - Retail: 1.76% - Machinery: 1.71%[2][45] - **Top 5 Industries by 1-Year Average Excess Returns**: - Banking: 2.12% - Real Estate: 1.93% - Environmental Protection: 1.50% - Retail: 1.46% - Machinery: 1.23%[2][46]

（文章来源：证券日报） 证券日报网讯 12月26日，深赛格在互动平台回答投资者提问时表示，公司会持续改善赛格通信市场环 境。 ...

Group 1 - The core point of the article is that Lishang Guochao (SH 600738) held its 40th board meeting on December 26, 2025, to review the proposal for amending the "Board Meeting Rules" [1] - For the year 2024, Lishang Guochao's revenue composition is as follows: property and leasing business accounts for 62.94%, retail trade accounts for 18.44%, other businesses account for 8.34%, commercial management accounts for 6.31%, and new retail business accounts for 3.96% [1] - As of the report date, Lishang Guochao has a market capitalization of 4.1 billion yuan [1]

Core Viewpoint - Nanjing Shanglv announced the termination of its major asset restructuring plan to acquire 100% equity of Nanjing Huangpu Hotel due to changes in market conditions after a year and a half of planning [1][8]. Group 1: Transaction Details - The transaction involved Nanjing Shanglv planning to acquire 100% equity of Nanjing Huangpu Hotel from its controlling shareholder, Nanjing Tourism Group, for approximately 199 million yuan, with cash payment of about 29.83 million yuan and share payment close to 169 million yuan [2][9]. - The acquisition was intended to enhance Nanjing Shanglv's dual business layout of "tourism + commerce" and extend its cultural tourism industry chain [10]. Group 2: Financial Performance - Huangpu Hotel's net profits for 2023, 2024, and Q1 2025 were reported as 8.16 million yuan, 6.28 million yuan, and 1.35 million yuan, respectively, indicating a significant downward trend [10]. - The hotel's operating revenue decreased from 67.53 million yuan in 2023 to 60.56 million yuan in 2024 [10]. Group 3: Valuation Concerns - The asset valuation of 199 million yuan represented a 150% appraisal increase, raising concerns among market participants regarding the valuation method used [10]. - Analysts noted that the asset-based valuation method, typically suited for fixed asset-heavy businesses, is uncommon for hotel operations, which are more reliant on operational performance [10]. Group 4: Lack of Performance Commitments - The absence of performance commitments in the transaction further heightened valuation risks, as both parties agreed not to set performance guarantees due to the use of the asset-based valuation method [11].

Group 1 - The core point of the article is that Shanghai Sanmao announced the convening of its 12th Board of Directors meeting for the second time in 2025, which will discuss the compensation settlement for senior management for the year 2024 [1] - For the year 2024, Shanghai Sanmao's revenue composition is as follows: commercial activities account for 70.46%, other industries account for 26.77%, and other businesses account for 2.77% [1] - As of the time of reporting, Shanghai Sanmao has a market capitalization of 2.7 billion yuan [1]

Core Viewpoint - The announcement from Qingtang City indicates significant corporate governance actions, including the approval of proposals related to land acquisition and financial guarantees for associated companies [2] Group 1: Board Decisions - The 18th meeting of the 11th Board of Directors was conducted via communication voting, with all 11 directors voting in favor [2] - The board approved a proposal regarding the planned expropriation of the subsidiary Huaneng Mall [2] - A proposal was also approved for the company to provide guarantees for its associated companies [2]