Group 1 - The securities sector experienced a significant surge, with Huayin Securities hitting the daily limit, and Huazheng Securities rising nearly 9% [3] - The A-share market showed positive momentum, with the Shanghai Composite Index breaking through the high point of November 14, 2025, reaching its highest level since July 2015 [1] - The Hong Kong stock market also saw gains, with the Hang Seng Index and Hang Seng Tech Index both rising, and notable stocks like Pop Mart and China Hongqiao increasing by over 3% [5][6] Group 2 - The solid-state battery concept saw a notable rise, with Jinlongyu hitting the daily limit, and several other companies following suit [7] - The chemical sector showed strength, particularly in the salt chemical direction, with companies like Zhongtai Chemical and Luhua Technology reaching the daily limit [11] - The semiconductor equipment sector continued to perform well, with Jinhai Tong hitting the daily limit and other companies like Jingyi Equipment and Zhongwei Company showing significant gains [12]

Market Overview - The Shanghai Composite Index has reached a new ten-year high, surpassing the previous peak of 4034.08 points, closing at 4054.30 with a gain of 0.77% [4][5] - A total of over 4000 stocks rose in the market, indicating a broad-based rally [2] Sector Performance - Non-bank financial stocks, often seen as the "flag bearers" of the bull market, have shown significant gains, with stocks like Huayin Securities and Huatai Securities hitting their daily limits [12][13] - The semiconductor sector and precious metals have also performed well, while some concepts like optical modules and optical communication have seen pullbacks [5][6] Brain-Computer Interface Sector - The brain-computer interface concept stocks have remained active, with companies like Aipeng Medical and Xiangyu Medical hitting their daily limits with gains of 20% [9][10] - Neuralink's announcement of large-scale production of brain-computer interface devices by 2026 has been highlighted as a significant milestone for the AI industry, indicating a shift towards human-machine symbiosis [11] Stock Highlights - Notable stock performances include: - Aipeng Medical (300753) at 39.96, up 20.00% - Xiangyu Medical (688626) at 87.12, up 20.00% - Meihua Medical (301363) at 34.63, up 19.99% [10] - In the software trading sector, stocks like Zhinanzhen and Tonghuashun have surged over 8% [15][16] Summary of Key Stocks - Huayin Securities (002945) rose to 17.84, up 9.99% - Huatai Securities (601688) increased to 25.33, up 4.02% - Zhinanzhen (300803) reached 147.06, up 8.35% [14][16]

| 名称 | 现价 涨跌 涨跌幅▼ | | --- | --- | | 华林证券 | 17.84 1.62 9.99% | | 华安证券 | 7.58 0.67 9.70% | | 东北证券 | 9.92 0.51 5.42% | | 圆刺湖园 | 21.82 0.89 4.25% | | 财达证券 | 7.01 0.28 4.16% | | 华泰证券 | 25.26 0.91 3.74% | | 东方财富 | 24.60 0.85 3.58% | | 国联民生 | 10.15 0.31 3.15% | | 天风证券 | 4.34 0.13 3.09% | | 长江证券 | 8.54 0.25 3.02% | 开源证券研报分析称，监管政策进入"积极"周期，券商投行、公募基金和海外业务有望接棒传统业务， 支持证券行业2026年盈利景气度，当下券商板块估值和机构持仓仍在低位，过往一年整体滞涨，看好券 商板块开年布局机会，短期看业绩预告和政策端有望带来催化。 华龙证券认为，市场慢牛预期下，券商权益资产配置比例持续上升，叠加指数化投资与公募基金代销能 力增强，财富管理业务迎来结构性机遇。监管导向从规模扩张转向效率与回 ...

盘面上，能源金属、医药商业、贵金属、有色金属、人脑工程概念等板块开盘活跃；船舶制造、航空航 天、玻璃玻纤、石油、燃气等板块开盘走低。 证券板块持续拉升，华林证券涨停，华安证券涨近9%，国泰海通、华泰证券、东北证券跟涨。 券商股集体爆发，其中华林证券直线拉升涨停。 1月6日早盘，A股三大指数开盘涨跌不一。截至发稿，三大指数全线飘红。上证指数持续走强，突破 2025年11月14日的阶段高点，刷新2015年7月底以来的新高。科创50指数涨超2%。 | 名称 | 序号 | 现价 | 涨跌 | 涨跌幅 | | --- | --- | --- | --- | --- | | 上证指数 | 1 | 4052.29 | 28.87 | 0.72% | | 深证成指 | 2 | 13918.96 | 90.33 | 0.65% | | 科创综指 | 3 | 1716.13 | 20.29 | 1.20% | | 万得全A | ব | 6595.03 | 57.09 | 0.87% | | 创业板指 | 5 | 3298.58 | 4.03 | 0.12% | | 北证50 | 6 | 1478.81 | 12.39 | 0.8 ...

Core Viewpoint - The A-share market has seen a strong performance in brokerage stocks, with several companies experiencing significant price increases, indicating a positive trend in the sector [1]. Group 1: Brokerage Stock Performance - Huayin Securities (华林证券) reached a limit-up increase of 9.99%, with a total market capitalization of 48.2 billion and a year-to-date increase of 16.37% [2]. - Hua'an Securities (华安证券) increased by 9.70%, with a market cap of 35.5 billion and a year-to-date increase of 11.80% [2]. - Zhina Compass (指南针) rose by 8.02%, with a market cap of 89.2 billion and a year-to-date increase of 12.03% [2]. - Tonghuashun (同花顺) saw a rise of 7.79%, with a market cap of 192.5 billion and a year-to-date increase of 11.11% [2]. - Northeast Securities (东北证券) increased by 4.68%, with a market cap of 23.1 billion and a year-to-date increase of 5.69% [2]. - Dongfang Caifu (东方财富) rose by 4.00%, with a market cap of 390.4 billion and a year-to-date increase of 6.51% [2]. - Other notable increases include Guotai Haitong (国泰海通) at 3.87%, Huatai Securities (华泰证券) at 3.90%, and Tianfeng Securities (天风证券) at 3.80% [2]. Group 2: Market Indicators - The MACD golden cross signal has formed, indicating a positive momentum for these stocks [2].

证券板块持续拉升，华林证券涨停，华安证券涨超6%，国泰海通、华泰证券、东北证券跟涨。 ...

Quantitative Models and Construction Methods Model Name: mHC-MLP - **Model Construction Idea**: The mHC-MLP model introduces manifold-constrained hyper-connections (mHC) into the traditional MLP framework to address issues such as low signal-to-noise ratio, non-stationarity, and extreme tail behavior in financial data. It achieves this by incorporating multi-stream residual channels, gated fan-in/fan-out mappings, and doubly stochastic manifold projections (via Sinkhorn-Knopp) to enhance numerical stability and extrapolation resistance[1][16][22]. - **Model Construction Process**: 1. **Multi-Stream Residual Channels**: The model expands the single residual channel in traditional ResNet to multiple parallel sub-streams, allowing independent feature representations and dynamic routing between streams[19][20]. 2. **Manifold Constraints**: - Residual mixing matrices are constrained to the Birkhoff polytope (doubly stochastic matrices), ensuring non-negativity, row sums of 1, and column sums of 1. This is achieved using the Sinkhorn-Knopp algorithm during training[22][23][54]. - Fan-in and fan-out mappings are constrained to non-negative values using sigmoid functions, ensuring that output features remain within the convex hull of input features[24]. 3. **Dynamic Routing Mechanism**: The model uses a combination of linear mixing (via residual matrices) and non-linear transformations (via MLP blocks) to balance feature interaction and noise suppression[49][50][51]. 4. **Deep Stacking**: The mHC-MLP extends the network depth to six layers, leveraging the numerical stability provided by manifold constraints to capture higher-order interactions[56][57]. 5. **Initialization and Regularization**: Parameters are initialized with minimal values (e.g., alpha = 0.01) to ensure stable gradient flow during early training stages. Regularization is achieved through manifold constraints rather than traditional dropout or L2 regularization[25][55]. - **Model Evaluation**: The mHC-MLP model demonstrates improved numerical stability, reduced overfitting, and enhanced robustness against noise. However, it may underperform in short-term, high-volatility scenarios due to its conservative nature[2][75][86]. --- Model Backtesting Results mHC-MLP Model - **Cumulative Return**: 49% (compared to 56% for the unconstrained MLP model)[75] - **t-Statistic**: Not explicitly mentioned for mHC-MLP - **IC_IR**: Not explicitly mentioned for mHC-MLP - **Turnover**: Lower than the unconstrained MLP model, indicating better stability[2][75] - **Maximum Drawdown**: Lower than the unconstrained MLP model, reflecting reduced risk exposure[2][75] --- Quantitative Factors and Construction Methods Factor Name: Barra MLP Factor - **Factor Construction Idea**: The Barra MLP factor leverages neural networks to capture non-linear interactions and complex relationships between Barra style factors and residual stock returns, overcoming the limitations of traditional linear factor models[30][31]. - **Factor Construction Process**: 1. **Baseline Risk Model**: A long-term risk model is constructed using the Barra CNE6 framework, incorporating one country factor, 31 industry factors, and 15 style factors (e.g., size, beta, momentum, value)[36][37][38]. 2. **Residual Return Extraction**: Stock returns are decomposed into common factor contributions and residual returns via cross-sectional regression. The residual returns serve as the prediction target for the MLP model[40]. 3. **Rolling Training**: The MLP model is trained using rolling windows of 24, 36, and 72 months to balance bias and variance. Features include the 15 style factors, and the target is the next-period residual return[41]. 4. **Multi-Period Signal Synthesis**: Predictions from the three training windows are standardized (Z-score) and combined using equal weighting or IC-based weighting to generate a composite factor[42][43]. 5. **Orthogonalization**: The composite factor is regressed against the 15 style factors to remove linear correlations, ensuring it provides incremental information[44]. 6. **Pure Factor Return Calculation**: The orthogonalized factor is incorporated into an enhanced Barra risk model, and its pure factor return is estimated via cross-sectional regression[45]. - **Factor Evaluation**: The Barra MLP factor effectively captures non-linear alpha signals and demonstrates significant cumulative returns and IC_IR values, validating its utility in quantitative strategies[46]. --- Factor Backtesting Results Barra MLP Factor - **Cumulative Return**: Over 15%[46] - **t-Statistic**: 2.8[46] - **IC_IR**: 0.45[46] - **Turnover**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned --- Composite Model: mHC-Enhanced Barra MLP Factor - **Model Construction Idea**: The mHC-enhanced Barra MLP factor integrates the mHC architecture into the Barra MLP framework to improve robustness and stability while retaining the ability to capture non-linear interactions[48]. - **Model Construction Process**: The MLP core in the Barra MLP factor is replaced with the mHC-MLP architecture, maintaining the same input features, target variables, and training framework. This modification introduces manifold constraints and dynamic routing to enhance numerical stability and reduce overfitting[48][49][50]. - **Model Evaluation**: While the mHC-enhanced factor demonstrates superior stability and robustness, it may lag in short-term, high-volatility markets due to its conservative design[75][86]. --- Composite Model Backtesting Results mHC-Enhanced Barra MLP Factor - **Cumulative Return**: Not explicitly mentioned - **t-Statistic**: Not explicitly mentioned - **IC_IR**: Not explicitly mentioned - **Turnover**: Lower than the original Barra MLP factor[2][75] - **Maximum Drawdown**: Lower than the original Barra MLP factor[2][75]

Core Viewpoint - Northeast Securities has initiated coverage on Sanhua Intelligent Control (02050), projecting revenue growth from 2025 to 2027, with corresponding net profits and PE ratios, and has given a "Buy" rating [1] Group 1: Financial Projections - The company is expected to achieve revenue of 322.6 billion, 371.3 billion, and 441.2 billion yuan for the years 2025, 2026, and 2027 respectively, with net profits of 42.4 billion, 51.2 billion, and 60.3 billion yuan [1] - For 2025, the company forecasts a net profit between 38.7 billion and 46.5 billion yuan, representing a year-on-year increase of 25% to 50%, and a non-recurring net profit between 36.8 billion and 46.1 billion yuan, reflecting an 18% to 48% increase [2] Group 2: Business Performance - The traditional refrigeration business has shown strong performance with a revenue of 103.9 billion yuan in the first half of 2025, marking a year-on-year growth of 25.5% and a gross margin of 28.2%, up by 0.65 percentage points [3] - The automotive parts business has seen a revenue of 58.7 billion yuan in the first half of 2025, with a modest year-on-year growth of 8.8%, influenced by Tesla's production and sales improvements [4] Group 3: Strategic Developments - The company has established a dedicated division for humanoid robots and set up a production base in Thailand to support Tesla's mass production needs, with expectations of significant revenue contributions starting in 2026 [5]

Group 1 - The core viewpoint of Northeast Securities is that Sanhua Intelligent Controls (002050) is expected to see significant revenue growth from 2025 to 2027, with projected revenues of 32.26 billion, 37.13 billion, and 44.12 billion yuan, and net profits of 4.24 billion, 5.12 billion, and 6.03 billion yuan respectively, leading to a PE ratio of 35, 29, and 25 times [1] - The company has released a performance forecast for 2025, expecting a net profit of 3.87 to 4.65 billion yuan, representing a year-on-year increase of 25% to 50%, and a non-net profit of 3.68 to 4.61 billion yuan, with a year-on-year increase of 18% to 48% [1] Group 2 - The traditional refrigeration business has shown strong performance, with a revenue of 10.39 billion yuan in the first half of 2025, reflecting a year-on-year growth of 25.5%, and a gross margin of 28.2%, up by 0.65 percentage points [2] - The automotive parts business is recovering, with a revenue of 5.87 billion yuan in the first half of 2025, showing a year-on-year growth of 8.8%, driven by improved production and sales from major client Tesla, which delivered 497,000 vehicles globally in Q3, a year-on-year increase of 7.4% [3] - The company is reducing its reliance on Tesla, as it begins to see contributions from other major clients such as General Motors and domestic new energy vehicle manufacturers like Xiaomi, Li Auto, and XPeng, while also making progress with European clients like Mercedes-Benz and Volkswagen [3] Group 3 - The company has established a clear strategic position in humanoid robotics, being a core supplier of electromechanical actuators for Tesla's humanoid robots, and has set up a dedicated humanoid robotics division along with a production base in Thailand to support customer demand [4] - Significant revenue contributions from the humanoid robotics segment are expected to begin in 2026 [4]

Core Insights - The A-share market in 2025 closed positively, with major indices showing significant gains, and brokerage "golden stock" portfolios delivering impressive results [1] - The top ten brokerage golden stock portfolios achieved returns exceeding 50%, with the highest being Guoyuan Securities at 84.08% [1][5] Brokerage Performance - Guoyuan Securities led with a return of 84.08%, with its best stock being Zhongtung Gaoxin [2][5] - Northeast Securities ranked second with a return of 68.38%, highlighting its best stock as Wukuang Resources [2][6] - Other notable performers include Kaiyuan Securities (67.1%), Huaxin Securities (62.23%), and Dongxing Securities (61.98%) [2] Stock Selection Strategies - Guoyuan Securities focuses on selecting stocks based on China's new economic growth drivers and emerging industries, aiming to identify companies with strong fundamentals and market expectations [5] - Northeast Securities employs a collaborative approach with nearly 30 teams contributing to stock recommendations, emphasizing technology and cyclical sectors [6][7] - Dongguan Securities utilizes a systematic research framework that integrates macroeconomic analysis with industry insights to select high-quality stocks [8] Research Methodologies - Huazhong Securities emphasizes a dual approach, combining top-down macroeconomic analysis with bottom-up stock selection to identify high-potential sectors and individual stocks [9] - The overall performance of brokerage golden stock portfolios in 2025 significantly outperformed previous years, with the lowest return among the top 20 brokerages nearing 40% [5]