Investment Rating - The report assigns a "Buy" rating for the company, indicating a positive outlook following the divestiture of its real estate business and steady growth in the cosmetics segment [5][42]. Core Insights - The company has transitioned its main business focus to cosmetics and pharmaceuticals after divesting its real estate operations in 2023. The cosmetics segment has shown resilience, contributing over 52.8% of total revenue [10][41]. - The company is leveraging a "product + R&D + channel" strategy to build competitive advantages, focusing on marketing synergies, innovation in product development, and a comprehensive online and offline sales network [8][30][34]. - Revenue projections for 2025-2027 are estimated at RMB 38.1 billion, RMB 39.3 billion, and RMB 43.0 billion, with corresponding growth rates of -4.3%, +3.1%, and +9.5% respectively. Net profit is expected to reach RMB 2.4 billion, RMB 2.7 billion, and RMB 3.1 billion during the same period [9][41]. Summary by Sections Market Performance - The closing price of the stock is RMB 7.36, with a total market capitalization of RMB 7,481.95 million and a circulating market value of RMB 7,481.95 million [3]. Financial Data - The company reported a gross margin of 46.49% in 2023, a significant increase of 21.84 percentage points from the previous year, primarily due to the strategic shift away from low-margin real estate operations [21]. Revenue and Profit Forecast - The company anticipates a decline in revenue for 2023, followed by a gradual recovery, with net profit growth projected at +0.2%, +8.8%, and +15.3% for the years 2025 to 2027 [9][41]. Business Segments - The cosmetics segment is expected to see revenue growth of -5%, +6%, and +14% from 2025 to 2027, while the pharmaceutical segment is projected to grow at -5%, -3%, and +5% during the same period [10][41]. - The raw materials and additives segment is forecasted to grow steadily, with revenue increases of +3%, +4%, and +5% from 2025 to 2027 [10]. Competitive Positioning - The company is positioned favorably against comparable domestic brands, with a projected price-to-earnings (P/E) ratio of 28 times for 2026, aligning with industry averages [42]. Marketing and R&D Strategy - The company has invested significantly in R&D, with a budget of RMB 1.16 billion for the first three quarters of 2025, representing a research expense ratio of 4.47% [30]. - New product launches and marketing initiatives are focused on enhancing brand visibility and consumer engagement across various channels, including e-commerce and physical retail [28][34]. Sales Channels - The cosmetics segment's revenue is heavily driven by online sales, contributing 84.2% of total revenue, while offline sales account for 15.8% [34]. - The company is expanding its presence in both online and offline markets, establishing flagship stores on major e-commerce platforms and increasing the number of physical retail locations [34].

证券研究报告 医药生物 | 化学制药 港股|公司点评报告 hyzqdatemark 2026 年 02 月 03 日 证券分析师 刘闯 SAC：S1350524030002 liuchuang@huayuanstock.com 市场表现： | 基本数据 | 2026 | 年 | 月 02 日 | 02 | | --- | --- | --- | --- | --- | | 收盘价（港元） | | | 15.12 | | | 一年内最高/最低（港 | | | 15.63/6.83 | | | 元） | | | | | | 总市值（百万港元） | | | 36,885.67 | | | 流通市值（百万港元） | | | 36,885.67 | | | 资产负债率（%） | | | 9.35 | | | 资料来源：聚源数据 | | | | | 康哲药业(00867.HK) 投资评级： 买入（维持） ——芦可替尼乳膏获批上市，掘金白癜风市场 投资要点： | 盈利预测与估值（人民币） | | | | | | | --- | --- | --- | --- | --- | --- | | | 2023 | 2024 | ...

证券分析师 证券研究报告 社会服务 | 旅游及景区 非金融|首次覆盖报告 hyzqdatemark 2026 年 02 月 03 日 请务必仔细阅读正文之后的评级说明和重要声明 丁一 SAC：S1350524040003 dingyi@huayuanstock.com 李禹蒙 liyumeng@huayuanstock.com 市场表现： | 基本数据 | | | | 02 | 年 | 月 | 02 | 日 | | 2026 | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 收盘价（元） | | | | 42.47 | | | | | | | | | 年 内 最 低 | 最 | 高 | / | | | | | | 60.33/29.73 | | 一 | | （元） | | | | | | | | | | | | | 总市值（百万元） | | | | | | | | | 11,566.95 | | | | 流通市值（百万元） | | | | | | | | | 11,325.47 | | | | 总股本 ...

证券研究报告 固收定期报告 hyzqdatemark 2026 年 02 月 03 日 ——利率半月报（2026.1.19-2026.2.1） 投资要点： 联系人 mahe@huayuanstock.com 制造业 PMI 重回荣枯线以下 报告核心观点：1 月制造业 PMI 较上月回落，重回荣枯线以下，非制造业 PMI 同步 回落，均时隔一个月再度进入收缩区间，或表明我国企业生产经营活动总体较上月 有所放缓。1 月制造业 PMI 环比-0.8pct 至 49.3%，1 月非制造业商务活动指数为 49.4%，环比-0.8pct，1 月综合 PMI 产出指数为 49.8%，较上月-0.9pct，均略低于 荣枯线。预计 26 年消费端持续有政策支持但力度或较 25 年有所回落，叠加 25 年社 零表现亮眼，高基数下 26 年消费端对经济的支撑效果或有所减弱。投资端基建和地 产对经济的拖累或将持续。受 25 年上半年抢出口影响，26 年外贸增长韧性有待观 察。或需持续关注稳增长政策落地效果及新质生产力等方向对经济的支撑作用。 本周（1/19-2/1）市场概览： 证券分析师 廖志明 SAC：S1350524100002 ...

证券研究报告 固收点评报告 hyzqdatemark 2026 年 02 月 03 日 ——债基 2025 年 Q4 季报分析 联系人 mahe@huayuanstock.com 从 2025Q4 季报看利率债基变化 利率债基整体规模缩减，债券配置比例下降，杠杆率略有下降。截至 25Q4，利率债 基资产总值为 3.0 万亿元，较 25Q3 下降 0.09 万亿元，其中主动型利率债基和被动 型利率债基分别为 2.06 万亿元和 0.96 万亿元，较 25Q3 分别-0.11 万亿/+0.02 万亿 元。从大类资产配置来看，截至 25Q4，利率债基主要配置于债券（规模约为 2.9 万 亿元，占比 96.18%），其次是存款（规模约为 0.04 万亿元，占比 1.35%），规模 占比较上一季度分别-0.21pct/+0.43pct，其中主动型利率债基的债券和存款配置规 模分别为 2.0 万亿元和 0.03 万亿元，比例分别为 95.99%/1.62%，较上一季度分别 -0.09pct/+0.52pct。25Q4 利率债基平均杠杆率为 113.1%，较 25Q3 减少 0.94pct。 主动型利率债基重仓券小幅减配国 ...

1. Report Industry Investment Rating - Not mentioned in the provided content 2. Core Viewpoints of the Report - Forecasts for January 2026: 4.9 trillion yuan in new loans, 7.07 trillion yuan in social financing increment; at the end of January, M2 reaches 345.1 trillion yuan with a YoY increase of 8.3%, new - caliber M1 YoY increase of 3.7%, and social financing growth rate of 8.1% [2] - New loans in January may be close to the same period last year, but the new loans in 2026 may still increase less year - on - year due to weak credit demand and non - negligible credit risks [3] - M1 growth rate may decline in January, and M2 growth rate may also decline slightly [3] - Social financing increment in January may be close to the same period last year, and the growth rate may decline slightly. The social financing growth rate may continue to decline in the next few months, and is expected to drop to around 7.5% by the end of 2026. The predicted social financing increment for 2026 is about 35 trillion yuan [3] - Long - term bonds may continue a small - scale rebound in February, and the yield of the active 30Y Treasury bond may return to around 2.2%. The yield of the 10Y Treasury bond is expected to fluctuate between 1.6% - 1.9% in 2026 [3] 3. Summary by Related Catalogs New Loans - It is expected that new loans in January will be 4.9 trillion yuan, with individual loans increasing by 450 billion yuan, corporate loans increasing by 4.5 trillion yuan, and non - bank inter - bank loans decreasing by 50 billion yuan [3] - Among individual loans, short - term loans are expected to increase by 50 billion yuan, and medium - and long - term loans are expected to increase by 400 billion yuan. Among corporate loans, short - term loans are expected to increase by 1.6 trillion yuan, medium - and long - term loans are expected to increase by 3.3 trillion yuan, and bill financing is expected to decrease by 400 billion yuan [3] M1 and M2 - The new - caliber M1 growth rate at the end of January is expected to be 3.7%, with a slight month - on - month decrease. The M2 growth rate at the end of January is expected to be 8.3%, with a slight month - on - month decline [3] Social Financing - The social financing increment in January is predicted to be 7.07 trillion yuan, close to the 7.05 trillion yuan in January 2025. The increment of RMB loans to the real economy is expected to be 4.95 trillion yuan, undiscounted bank acceptance bills to increase by 30 billion yuan, net corporate bond financing to be 50 billion yuan, and net government bond financing to be 110 billion yuan [3] - The social financing growth rate is expected to drop to 8.1% at the end of January, and may continue to decline in the next few months, reaching around 7.5% by the end of 2026. The predicted social financing increment for 2026 is about 35 trillion yuan [3] Bond Market - From November 20, 2025, to the end of January 2026, securities firms' proprietary trading, funds, and annuities significantly reduced their holdings of ultra - long - term interest - rate bonds, with a net sale of 349.8 billion yuan in total. Long - term bonds may continue to rebound in February, and the yield of the active 30Y Treasury bond may return to around 2.2%. The yield of the 10Y Treasury bond is expected to fluctuate between 1.6% - 1.9% in 2026 [3]

Investment Rating - The report does not explicitly provide an investment rating for the industry or specific companies Core Insights - The "Work Plan" issued by the State Council aims to accelerate the cultivation of new growth points in service consumption, focusing on six key areas and three potential areas, which is expected to open up incremental space for service consumption [3][6] - By 2025, the per capita service consumption expenditure in China is projected to reach 13,602 yuan, with a year-on-year increase of 4.5%, accounting for 46.1% of total per capita consumption expenditure [3][11] - The domestic tourism market is expected to see 6.522 billion trips in 2025, a year-on-year increase of 16.2% [3][14] - The ice and snow industry is projected to generate over 187.5 billion yuan in consumption during the 2024-2025 season, with a growth rate exceeding 25% [3][30] - The emotional economy market in China is expected to reach 23,077.67 billion yuan in 2024 and surpass 45,000 billion yuan by 2029 [3][36] Summary by Relevant Sections Service Consumption Growth - The "Work Plan" focuses on optimizing service supply and fostering new service consumption models, particularly in transportation, housekeeping, automotive aftermarket, online audio-visual services, travel services, and inbound consumption [3][6] - The report highlights the increasing contribution of final consumption expenditure to economic growth, projected to be 52% in 2025, up by 5 percentage points from 2024 [9] Market Performance - The median price-to-earnings (P/E) ratio for the North Exchange service consumption sector decreased from 48.2X to 47.7X, with a total market capitalization dropping from 114.684 billion yuan to 112.981 billion yuan [46][51] - The median market cap for companies in this sector fell from 2.038 billion yuan to 1.978 billion yuan [51] Company Insights - Notable companies in the service consumption sector include: - Elderly care: Zhisheng Information, Beiyikang - Sports: Huayang Racing, Kangbiter - Tourism: Meiya Technology - Emotional/experiential consumption: Taihu Snow, Lusi Co., Baixinglong - Education: Digital Human, Tonghui Information - Online audio-visual: Xiange International, Haifiman - Apparel: Zhongfangbiao, Tianfangbiao [3][40] Performance Highlights - The report indicates that 29% of companies in the North Exchange service consumption sector saw stock price increases, with the median stock price change being -2.41% [46] - Top performers included Qiule Seed Industry (+22.55%), Kangnong Seed Industry (+13.50%), and Oufu Egg Industry (+7.05%) [46][56]

Group 1: Transportation Industry - The express delivery sector shows resilient demand, with a "反内卷" (anti-involution) trend driving up prices and releasing profit elasticity for companies, indicating a favorable long-term competitive environment for e-commerce logistics [17] - The civil aviation sector is experiencing a significant recovery, with major airlines like China Southern and Hainan Airlines expected to turn profitable in 2025, while others like China Eastern and Air China are projected to reduce losses, setting a solid foundation for 2026 [8][9] - The shipping market is witnessing a "strong off-season" for dry bulk freight rates due to a combination of concentrated cargo volumes and tight capacity, with the Baltic Dry Index (BDI) showing a notable increase [13][14] Group 2: Energy Storage Industry - The National Development and Reform Commission and the National Energy Administration have issued a notice to improve the capacity pricing mechanism for power generation, which is expected to enhance the energy storage sector's growth [21] - By the end of 2025, China's cumulative installed capacity for energy storage is projected to reach 213.3 GW, a 54% year-on-year increase, with new energy storage technologies, particularly lithium-ion batteries, experiencing rapid growth [21] - A total of 13 core companies in the energy storage industry have been identified, including Changhong Energy and Haixi Communications, which are positioned to benefit from the expanding market [21] Group 3: Media and Internet Industry - Google's Project Genie has launched, allowing users to create and edit interactive 3D environments, indicating a significant advancement in AI applications within the media sector [25] - The AI gaming creation tool "TapTap Manufacturing" has been introduced, enabling creators to develop games without leaving the chat interface, showcasing the integration of AI in game development [25] - The competition for AI entry points is expected to continue evolving, with major platforms like WeChat and Douyin playing crucial roles in user education and ecosystem integration [25][28] Group 4: Food and Beverage Industry - Weidong Delicious is recognized as a leading player in the spicy snack sector, capitalizing on the trend of health-conscious consumption and maintaining a strong brand presence [4] - The company has established a robust multi-category product strategy, which is expected to drive growth and sustain high profitability [4] Group 5: Real Estate Industry - China Merchants Shekou is focusing on core cities and leveraging its state-owned enterprise advantages to optimize its asset structure and enhance its competitive position [4] - The company has proactively addressed historical burdens by writing down approximately 22.6 billion yuan in asset and credit impairments since 2020, positioning itself for future growth [4]

证券研究报告 汽车 行业定期报告 hyzqdatemark 2026 年 02 月 02 日 投资评级： 看好（维持） 李泽 SAC：S1350525030001 lize@huayuanstock.com 秦梓月 SAC：S1350525070008 qinziyue@huayuanstock.com 联系人 板块表现： 主机厂原材料涨价后续影响分析 ——汽车行业周报（20260126-20260201） 投资要点： 请务必仔细阅读正文之后的评级说明和重要声明 证券分析师 2025Q4 以来碳酸锂、六氟磷酸锂等电池原材料及铜、铝等价格均出现上涨。2020 年以来汽车原材料价格的明显上涨主要分为两轮：第一轮为 2020 年（尤其 2020Q3 开始）~2022 年（主要截至 2022Q1），彼时汽车主要原材料价格出现普涨，其中碳 酸锂涨幅居前，电池级碳酸锂价格由 2020Q3 的平均 4 万元/吨涨至 2022Q4 的平均 55 万元/吨；第二轮则为 2025Q4 以来的碳酸锂、六氟磷酸锂等电池原材料及铜、铝 等价格的上涨，2026 年 1 月 28 日电池级碳酸锂、六氟磷酸锂、LME 铜现货结算价、 中国铜 ...

Quantitative Models and Construction Methods - **Model Name**: Sparse Auto Encoder (SAE) **Model Construction Idea**: The model aims to compress high-dimensional features into low-dimensional sparse coding while ensuring the reconstructed features retain most of the original information. It also incorporates autoregressive loss and sparsity penalties to enhance robustness and reduce overfitting [7][8][9] **Model Construction Process**: 1. **Encoding**: Compress input features into sparse coding $ \text{code}_{i}=\text{Encoder}(x_{i}) $ Here, $ x_{i} $ represents input features, and $ \text{code}_{i} $ is the compressed sparse coding [8] 2. **Decoding**: Reconstruct features from sparse coding $ \hat{x}_{i}=\text{Decoder}(code_{i}) $ $ \hat{x}_{i} $ represents reconstructed features, which should closely resemble $ x_{i} $ [8] 3. **Prediction**: Predict future index returns using hidden layer features $ \hat{y}_{i}=\text{Predictor}(res_{i}) $ $ \hat{y}_{i} $ represents the predicted future returns [8] 4. **Loss Function**: Combines prediction error, reconstruction error, and sparsity penalty $$ Loss=\frac{1}{N}\sum\nolimits_{i=1}^{N}\left(\mathcal{J}(y_{i},{\hat{y}}_{i})+\lambda_{1}\mathcal{L}\left(x_{i},{\hat{x}}_{i}\right)+\lambda_{2}SparseLoss(code_{i})\right) $$ $ \mathcal{J} $ measures prediction error, $ \mathcal{L} $ measures reconstruction error, and $ SparseLoss $ applies sparsity penalties using KL divergence or vector norms [9][12] **Evaluation**: The model effectively selects features, enhances robustness, and learns the "true" patterns of index movements [11] - **Wavelet Transform for Noise Reduction** **Construction Idea**: Decompose time-series data into multiple components to isolate noise and retain meaningful information [19][20] **Construction Process**: 1. Select parent wavelet $ \varphi $ and mother wavelet $ \psi $ $ \varphi_{jk}=2^{-j/2}\varphi(2^{-j}-k) $ $ \psi_{jk}=2^{-j/2}\psi(2^{-j}-k) $ Parent wavelet captures low-frequency trends, while mother wavelet captures high-frequency fluctuations [19][20] 2. Reconstruct time-series data using wavelet coefficients $$ x(t)=\sum\nolimits_{k}s_{j,k}\varphi_{j,k}+\sum\nolimits_{k}d_{j,k}\psi_{j,k}+\ldots+\sum\nolimits_{k}d_{1,k}\psi_{1,k} $$ Coefficients $ S_{J,k} $ and $ d_{j,k} $ are calculated as: $ S_{J,k}=\int\varphi_{J,k}x(s)ds $ $ d_{j,k}=\int\psi_{J,k}x(s)ds $ [20] **Evaluation**: Reduces overfitting risks by filtering out noise and retaining meaningful components [21] Model Backtesting Results - **SAE Model** **Performance on CSI 500 Index**: - Multi-strategy annualized return: 43.86% - Long-only annualized return: 23.30% - Short-only annualized return: 16.68% - Sharpe ratio: 2.07 (multi-strategy), 1.39 (long-only), 1.28 (short-only) - Maximum drawdown: -14.00% (multi-strategy), -16.04% (long-only), -14.30% (short-only) [29][33][34] **Performance on CSI 1000 Index**: - Multi-strategy annualized return: 51.21% - Long-only annualized return: 26.00% - Short-only annualized return: 20.01% - Sharpe ratio: 1.41 (long-only), 1.27 (short-only) - Maximum drawdown: -22.08% (long-only), -19.85% (short-only) [43][46][47] **Performance on CSI 2000 Index**: - Multi-strategy annualized return: 32.40% - Long-only annualized return: 32.56% - Sharpe ratio: 1.62 (long-only) - Maximum drawdown: -25.59% (long-only) [55][56] **Performance on CSI All Share Index**: - Multi-strategy annualized return: 18.74% - Long-only annualized return: 18.83% - Sharpe ratio: 1.26 (long-only) - Maximum drawdown: -16.95% (long-only) [55][56] Quantitative Factors and Construction Methods - **Input Features** **Construction Idea**: Use common technical indicators and derived metrics from daily K-line data as model inputs [16][18] **Construction Process**: 1. **Technical Indicators**: - RSI: $ RSI=(N\text{-day absolute closing price increase})/(N\text{-day absolute closing price decrease}) $ - OBV: $ OBV=\text{sum of closing price change signs} \times \text{turnover rate} $ - MACD: $ DIF=12\text{-day EMA}-26\text{-day EMA} $ $ DEA=DIF\text{'s 9-day EMA} $ $ MACD=DIF-DEA $ [16][17] 2. **Derived Metrics**: Rolling averages, relative positions of moving averages, volatility metrics, and other derived indicators [16][18] **Evaluation**: The feature set is comprehensive but not optimized, as no additional filtering was applied to avoid overfitting [18] Factor Backtesting Results - **RSI, OBV, MACD** **Performance**: Incorporated into the SAE model, contributing to the overall strategy performance across indices [16][18] Key Observations - The SAE model performs better on smaller-cap indices like CSI 2000 and CSI 1000 compared to CSI 500, indicating its effectiveness in smaller market segments [62] - Multi-strategy returns are balanced between long and short positions, with no significant bias toward either direction [42][54] - The model's robustness and sparsity design mitigate overfitting risks and enhance generalization across different market conditions [11][21] - Setting appropriate thresholds for signal generation improves strategy stability and reduces transaction costs [66]