Group 1 - The report emphasizes the necessity of establishing a diversified payment system for innovative drugs and medical devices in China, as the current single medical insurance payment model cannot meet the demand for high-priced innovative therapies [3][8][9] - According to the report, the market size for innovative drugs and medical devices in China is expected to reach 1.62 trillion yuan in 2024, representing a 16% year-on-year growth, with personal cash expenditure accounting for 49% and medical insurance fund expenditure approximately 44% [3][8] - The report highlights that the commercial health insurance sector in China is projected to reach a premium scale of 977.3 billion yuan in 2024, but it still faces challenges in terms of operational mechanisms and payment convenience [3][8][9] Group 2 - The report reviews the performance of the pharmaceutical sector from April 21 to April 25, noting that only three out of six sub-industries recorded positive returns, with the medical services and chemical pharmaceuticals leading the gains at +3.38% and +2.71% respectively [10] - The report indicates that the valuation levels for the chemical pharmaceuticals and biological products sectors are the highest at 70.86 times and 60.16 times respectively, while the lowest are for traditional Chinese medicine and pharmaceutical commerce at 29.79 times and 20.75 times [10][15] - The report tracks several industry hotspots, including the establishment of a biological sample library in Hangzhou, the pilot program for pre-hospitalization medical insurance payments in Beijing, and the recent policy allowing the import of cow bile for traditional Chinese medicine production [18][21][22]

发布时间：2025-04-30 研究所 分析师:肖承志 SAC 登记编号:S1340524090001 Email:xiaochengzhi@cnpsec.com 近期研究报告 《年报效应边际递减，右侧买入信号 触发——微盘股指数周报 20250427》 - 2025.04.27 《动量波动分化，低波高涨占优—— 中邮因子周报 20250427》 - 2025.04.27 《OpenAI 发布 GPT-4.1，智谱发布 证券研究报告：金融工程报告 GLM-4-32B-0414 系列——AI 动态汇总 20250421》 - 2025.04.23 《国家队交易特征显著，短期指数仍 交易补缺预期，TMT 类题材仍需等待— —行业轮动周报 20250420》 - 2025.04.21 《小市值强势，动量风格占优——中 邮因子周报 20250420》 - 2025.04.21 《基本面与量价共振，如遇回调即是 买点——微盘股指数周报 20250420》 - 2025.04.21 《Meta LIama 4 开源，OpenAI 启动先 锋计划——AI 动态汇总 20250414》 - 2025.04.15 《融资盘被动 ...

Core Viewpoint - A total of 828 stocks reported their latest shareholder numbers as of April 20, with 392 stocks showing a decline compared to the previous period, indicating a trend of decreasing shareholder engagement in certain companies [1][3]. Group 1: Shareholder Numbers - Among the 828 stocks, 392 experienced a decline in shareholder numbers, with 10 stocks showing a decrease of over 10% [3]. - The stock with the largest decline in shareholder numbers was Ha Sanlian, which saw a drop of 26.12% to 41,802 shareholders [3][4]. - Other notable declines included Shanshui Bide with a 25.96% decrease and Fengle Seed Industry with a 16.39% decrease [4][5]. Group 2: Stock Performance - The average decline for concentrated chip stocks from April 1 was 3.14%, underperforming the Shanghai Composite Index, which fell by 1.70% [2]. - Among the stocks with a decline in shareholder numbers, Xin Nuowei had the highest increase in stock price, rising by 12.15% since April 1 [2][3]. - The latest concentrated chip stocks showed an average increase of 4.37% since April 11, with top performers including Xin Han New Materials, Hengbo Shares, and Weike Technology, which rose by 69.04%, 59.41%, and 51.51% respectively [3][4]. Group 3: Industry Insights - The concentrated chip stocks were primarily found in the machinery equipment, pharmaceutical biology, and basic chemical industries, with 50, 34, and 34 stocks respectively [3]. - The performance of concentrated chip stocks indicates a potential shift in investor focus towards specific sectors, particularly those showing strong earnings growth [4].

Quantitative Models and Construction Methods GAN_GRU Model - **Model Name**: GAN_GRU - **Model Construction Idea**: The GAN_GRU model utilizes Generative Adversarial Networks (GAN) for processing volume-price time series features and then uses the GRU model for time series feature encoding to derive the stock selection factor[2][9]. - **Model Construction Process**: 1. **GRU Model**: - **Basic Assumptions**: The GRU+MLP neural network stock return prediction model includes 18 volume-price features such as closing price, opening price, trading volume, turnover rate, etc[10][13][15]. - **Training Data and Input Features**: All stocks' past 400 days of 18 volume-price features, sampled every 5 trading days. The feature sampling shape is 40*18, using the past 40 days of volume-price features to predict the cumulative return of the next 20 trading days[14]. - **Training and Validation Set Ratio**: 80%:20%[14]. - **Data Processing**: Extreme value removal and standardization in the time series for each feature within the 40 days, and cross-sectional standardization at the stock level[14]. - **Model Training Method**: Semi-annual rolling training, i.e., training the model every six months and using it to predict the returns for the next six months. Training dates are June 30 and December 31 each year[14]. - **Stock Selection Method**: Select all stocks in the cross-section, excluding ST and stocks listed for less than six months[14]. - **Training Sample Selection Method**: Exclude samples with empty labels[14]. - **Hyperparameters**: batch_size is the number of stocks in the cross-section, optimizer Adam, learning rate 1e-4, loss function IC, early stopping rounds 10, maximum training rounds 50[14]. - **Model Structure**: Two GRU layers (GRU(128, 128)) followed by MLP layers (256, 64, 64). The final output predicted return pRet is used as the stock selection factor[18]. 2. **GAN Model**: - **Introduction**: GANs consist of a generator and a discriminator. The generator aims to generate realistic data, while the discriminator aims to distinguish between real and generated data[19]. - **Generator**: - **Loss Function**: $$L_{G}\,=\,-\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$$ where \(z\) represents random noise (usually Gaussian distributed), \(G(z)\) represents the data generated by the generator, and \(D(G(z))\) represents the probability that the discriminator judges the generated data as real[20][21]. - **Training Process**: Generate noise data, convert noise data to generated data using the generator, calculate generator loss, and update generator parameters through backpropagation[21][22]. - **Discriminator**: - **Loss Function**: $$L_{D}=-\mathbb{E}_{x\sim P_{d a t a}(x)}[\log\!D(x)]-\mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$$ where \(x\) is real data, \(D(x)\) is the probability that the discriminator judges the real data as real, and \(D(G(z))\) is the probability that the discriminator judges the generated data as real[23]. - **Training Process**: Sample real data, generate fake data, calculate discriminator loss, and update discriminator parameters through backpropagation[24][25]. - **GAN Training Process**: Alternately train the generator and discriminator until convergence[25][26]. 3. **GAN Feature Generation Model Construction**: - **LSTM Generator + CNN Discriminator**: To retain the time series nature of the input features, the LSTM model is used as the generator. The CNN model is used as the discriminator to match the two-dimensional volume-price time series features[29][30][33]. - **Feature Generation Process**: Input original volume-price time series features (Input_Shape=(40,18)), output volume-price time series features processed by LSTM (Input_Shape=(40,18))[33]. Model Evaluation - **Evaluation**: The GAN_GRU model effectively combines GAN and GRU to process and encode volume-price time series features, providing a robust stock selection factor[2][9]. Model Backtest Results - **GAN_GRU Model**: - **IC Mean**: 11.73%[37][38] - **Annualized Excess Return**: 24.89%[37][38] - **Latest IC**: 0.22% (as of April 28, 2025)[37][38] - **IC Mean in the Past Year**: 11.44%[37][38] - **Annualized Return**: 36.06%[38] - **Annualized Volatility**: 23.80%[38] - **Information Ratio (IR)**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Turnover Rate**: 0.83[38] - **ICIR**: 0.90[38] Quantitative Factors and Construction Methods GAN_GRU Factor - **Factor Name**: GAN_GRU - **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model, which processes volume-price time series features using GAN and encodes them using GRU[2][9]. - **Factor Construction Process**: The factor is generated by the GAN_GRU model, which includes the steps of feature processing by GAN and encoding by GRU as described in the model construction process[2][9][33]. - **Factor Evaluation**: The GAN_GRU factor shows strong performance in stock selection, with high IC values and significant excess returns[2][9]. Factor Backtest Results - **GAN_GRU Factor**: - **IC Mean**: 11.73%[37][38] - **Annualized Excess Return**: 24.89%[37][38] - **Latest IC**: 0.22% (as of April 28, 2025)[37][38] - **IC Mean in the Past Year**: 11.44%[37][38] - **Annualized Return**: 36.06%[38] - **Annualized Volatility**: 23.80%[38] - **Information Ratio (IR)**: 1.66[38] - **Maximum Drawdown**: 27.29%[38] - **Turnover Rate**: 0.83[38] - **ICIR**: 0.90[38]

Investment Rating - The report maintains a "Buy" rating for the company [2][6]. Core Views - The company is expected to see a performance recovery throughout the year, with a projected EPS of 0.54, 0.61, and 0.67 for 2025-2027, respectively [2][10]. - The target price is set at 12.42 CNY, based on a 23x P/E ratio for 2025 [2][6]. Financial Performance Summary - For 2023, the company reported a revenue of 7,980 million CNY, which is a decrease of 24.8% year-on-year. The revenue is expected to recover to 6,835 million CNY in 2025, reflecting a growth of 12.0% [4][10]. - The net profit attributable to the parent company for 2023 was 1,258 million CNY, down 42.9% year-on-year, with a forecasted recovery to 1,010 million CNY in 2025, representing a growth of 309.1% [4][10]. - The gross margin is projected to be 62.2% in 2025, slightly improving from 60.9% in 2024 [4][10]. Business Segment Performance - In 2024, the medical device segment generated revenue of 33.3 billion CNY, down 9.5% year-on-year, while the structural heart disease business showed strong growth with a 44.0% increase [10]. - The pharmaceutical segment's revenue in 2024 was 17.6 billion CNY, a decline of 42.3% due to price governance impacts [10]. - The company is focusing on innovation and internationalization, with new products in the cardiovascular field and ongoing clinical trials for innovative drugs [10].

业绩环比修复，海内外市场同步拓展 ——海泰新光 2024 年报&2025 年一季报点评 核心观点 盈利预测与投资建议 ⚫ 根据 2024 年报及 2025 年一季报，我们下调收入、毛利率假设，调整 2025-27 年每 股收益预测分别为 1.51/1.79/2.23（原预测 25-26 年为 2.09/2.58）元，根据可比公 司给予 25 年 30 倍 PE，对应的目标价为 45.30 元，维持"买入"评级。 风险提示 大客户依赖风险、地缘政治风险、国内市场拓展不及预期风险、下游需求波动风险 等。 公司主要财务信息 | | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | | 营业收入(百万元) | 471 | 443 | 565 | 691 | 868 | | 同比增长 (%) | -1.3% | -5.9% | 27.5% | 22.4% | 25.6% | | 营业利润(百万元) | 161 | 153 | 206 | 241 | 302 | | 同比增长 (%) | -22.0% | -5.0% ...

25Q1 环比改善，看好全年业绩修复 ——乐普医疗 2024 年报&2025 年一季报点评 核心观点 盈利预测与投资建议 伍云飞 wuyunfei1@orientsec.com.cn 执业证书编号：S0860524020001 香港证监会牌照：BRX199 ⚫ 根据 24 年报及 25 年一季报，我们下调收入及毛利率预测，我们预测公司 2025-2027 年 EPS 分别为 0.54/0.61/0.67 元（原预测 25-26 年 EPS 为 0.75、0.90 元）。根据可 比公司 2025 年平均估值，给予公司 2025 年 23 倍市盈率，对应目标价为 12.42 元， 维持给予"买入"评级。 风险提示 ⚫ 研发进展和新产品商业化不及预期；集采或者市场竞争加剧带来的冲击；大额计提 减值风险。 公司主要财务信息 | | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | | 营业收入(百万元) | 7,980 | 6,103 | 6,835 | 7,378 | 7,968 | | 同比增长 (%) | ...

Investment Rating - The report maintains a "Buy" rating for the company, with a target price of 22.77 CNY based on a 69 times price-to-earnings ratio for 2025 [2][5]. Core Views - The company's performance is under short-term pressure but is expected to grow in the medium to long term. The revenue forecast for 2025-2027 has been adjusted, with earnings per share projected at 0.33, 0.61, and 1.01 CNY respectively [1][2]. - The company reported a revenue of 721 million CNY for 2024, a decrease of 5.2% year-on-year, and a net profit of 108 million CNY, down 32.4% year-on-year. The first quarter of 2025 saw a revenue of 185 million CNY, a decline of 2.35% [9]. - The company is facing increased competition in the two-sex health sector, leading to a 20.87% decline in revenue from this segment. However, there is significant growth potential in the gastrointestinal medication market, with new product registrations expected to boost revenue [9]. Financial Summary - The company's financial performance shows a projected revenue increase from 721 million CNY in 2024 to 1.52 billion CNY in 2027, reflecting a compound annual growth rate of 31.9% [4][11]. - The gross margin is expected to improve from 56.7% in 2024 to 71.7% in 2027, while the net profit margin is projected to rise from 15.0% to 28.2% over the same period [4][11]. - The company's earnings per share are forecasted to increase from 0.25 CNY in 2024 to 1.01 CNY in 2027, indicating strong growth potential [4][11].

业绩环比修复，海内外市场同步拓展 ——海泰新光 2024 年报&2025 年一季报点评 风险提示 大客户依赖风险、地缘政治风险、国内市场拓展不及预期风险、下游需求波动风险 等。 公司主要财务信息 | | 2023A | 2024A | 2025E | 2026E | 2027E | | --- | --- | --- | --- | --- | --- | | 营业收入(百万元) | 471 | 443 | 565 | 691 | 868 | | 同比增长 (%) | -1.3% | -5.9% | 27.5% | 22.4% | 25.6% | | 营业利润(百万元) | 161 | 153 | 206 | 241 | 302 | | 同比增长 (%) | -22.0% | -5.0% | 34.7% | 17.2% | 25.3% | | 归属母公司净利润(百万元) | 146 | 135 | 183 | 216 | 269 | | 同比增长 (%) | -20.2% | -7.1% | 34.9% | 18.1% | 24.8% | | 每股收益（元） | 1.21 | 1.12 | 1.51 | 1. ...

市场表现上，基金调研股中，近5日上涨的有16只，涨幅居前的有集智股份、珠城科技、曼卡龙等，涨 幅为22.30%、11.13%、9.65%；下跌的有26只，跌幅居前的有川仪股份、普联软件、菲菱科思等，跌幅 为11.75%、11.59%、10.10%。 数据宝统计，基金参与调研股中，近5日资金净流入的有15只，集智股份近5日净流入资金6754.69万 元，主力资金净流入最多；净流入资金较多的还有双汇发展、珠城科技等，净流入资金分别为6107.39 万元、4218.00万元。 业绩方面，基金调研公司中，42家公司已经公布了一季报，净利润同比增幅最高的是联化科技、完美世 界，净利润增长幅度为1747.04%、1115.29%。（数据宝） 4月29日基金调研公司一览 | 代码 | 简称 | 基金家数 | 最新收盘价（元） | 近5日涨跌幅（%） | 行业 | | --- | --- | --- | --- | --- | --- | | 600885 | 宏发股份 | 25 | 33.10 | 2.26 | 电力设备 | | 301191 | 菲菱科思 | 22 | 78.42 | -10.10 | 通信 | | 68 ...