Investment Rating - The investment rating for the company is "Buy" and is maintained [1] Core Views - The company is experiencing continuous improvement in profitability, with Q1 2025 revenue reaching 558 million yuan, a year-on-year increase of 44.08%, and net profit attributable to shareholders of 94 million yuan, up 73.80% year-on-year [4] - The company is expanding its product matrix across multiple digitalization fields, with rapid growth in new customer acquisition supporting future business growth [5] - The company is expected to achieve revenues of 2.61 billion, 3.50 billion, and 4.60 billion yuan in 2025, 2026, and 2027 respectively, with net profits of 450 million, 630 million, and 840 million yuan for the same years, maintaining a "Buy" rating [6] Company Overview - The latest closing price is 141.18 yuan, with a total market capitalization of 22.1 billion yuan and a total share capital of 1.57 billion shares [3] - The company has a debt-to-asset ratio of 17.7% and a price-to-earnings ratio of 45.71 [3] Financial Forecasts and Key Indicators - The company is projected to have a revenue growth rate of 40.04% in 2024, followed by 30.11% in 2025, and 34.19% in 2026 [8] - The expected net profit growth rates are 149.13% for 2024, 32.77% for 2025, and 40.46% for 2026 [8] - The earnings per share (EPS) is forecasted to be 2.17 yuan in 2024, increasing to 5.37 yuan by 2027 [8]

Investment Rating - The report maintains a "Buy" rating for the company [7][12]. Core Insights - The company is expected to achieve revenue of 6.278 billion yuan in 2024, representing a year-on-year growth of 23.67%, and a net profit of 1.030 billion yuan, reflecting a significant increase of 71.57% [2][10]. - The company has diversified its product offerings, with all five major business segments showing positive growth, particularly in optical components and film optical panels, which contributed significantly to the main revenue [2][3]. - The company has established a global presence with six production bases and is actively expanding its international market reach, enhancing its collaboration with major clients [3][5]. Financial Projections - Revenue projections for 2025, 2026, and 2027 are 7.346 billion yuan, 8.596 billion yuan, and 10.059 billion yuan, respectively, with corresponding net profits of 1.207 billion yuan, 1.416 billion yuan, and 1.664 billion yuan [7][10]. - The company's PE ratios for 2025, 2026, and 2027 are projected to be 22 times, 19 times, and 16 times, respectively [7][10]. Business Strategy - The company has acquired a 95.60% stake in Guangdong Aikesi, enhancing its strategic development in the AR/VR sector and positioning itself as a comprehensive optical solution provider [6][7]. - The company is transitioning from a product-driven model to a technology innovation-driven model, aiming to become a leading optical solution supplier [5][6].

Investment Rating - The report maintains a "Buy" rating for the company [7][12]. Core Insights - The company is expected to achieve revenue of 6.278 billion yuan in 2024, representing a year-on-year growth of 23.67%, and a net profit of 1.030 billion yuan, reflecting a significant increase of 71.57% [2][10]. - The company has diversified its product offerings, with all five major business segments showing positive growth. The optical components and film optical panel segments are the primary revenue contributors, accounting for 47% and 40% of total revenue, respectively [2][3]. - The company has established a global presence with six production bases and is actively expanding its international market reach [3][5]. - The acquisition of a 95.60% stake in Guangdong Aikesi enhances the company's capabilities in AR/VR and 3D visual recognition solutions, positioning it as a competitive player in the market [6][7]. Financial Performance - The company forecasts revenues of 7.346 billion yuan, 8.596 billion yuan, and 10.059 billion yuan for 2025, 2026, and 2027, respectively, with corresponding net profits of 1.207 billion yuan, 1.416 billion yuan, and 1.664 billion yuan [7][10]. - The projected price-to-earnings (P/E) ratios for 2025, 2026, and 2027 are 22 times, 19 times, and 16 times, respectively [7][10].

Quantitative Models and Construction Methods GRU Model - **Model Name**: GRU - **Model Construction Idea**: The GRU model is used to mine volume and price information, and this report explores its ability to incorporate financial information[2][14]. - **Model Construction Process**: - **Data Range**: 20130101-20250430, all market stocks (excluding Beijing Stock Exchange)[16] - **Input**: Each stock has one sample at the end of each month, containing volume and price information for the past 240 trading days, including 7 fields: opening price, highest price, lowest price, closing price, trading volume, trading amount, and turnover rate. Each field is standardized using z-score for 240 values[16]. - **Prediction Target**: Next month's return rate standardized by cross-section (opening price at the beginning of the month to closing price at the end of the month)[16]. - **Training Set**: Samples from the past 6 years, divided into training and validation sets in a 4:1 ratio according to time sequence[16]. - **Training Method**: Rolling training every month, early stopping if the loss function does not decrease for 10 consecutive rounds[16]. - **Model Evaluation**: The GRU model can simultaneously mine volume and price information and financial information. The high-frequency processing of financial information improves the model results to some extent[2][18]. - **Model Testing Results**: - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Model Name**: GRU with Financial Information - **Model Construction Idea**: Incorporating financial information into the GRU model to improve its performance[4][24]. - **Model Construction Process**: - **Simple Splicing of Financial Information**: Financial data is calculated as TTM value according to the latest available quarterly report for each trading day, then spliced into new columns. The matrix containing volume and price information and fundamental information is standardized and input into the GRU network[25]. - **Adjusted Financial Information**: Assuming the TTM value of financial indicators grows steadily at the quarterly growth rate, the daily adjustment formula for TTM values is: $$ \mathrm{DFTTM}_{\mathrm{q1}}={\frac{\mathrm{FactorTTM}_{\mathrm{q1}}-\mathrm{FactorTTM}_{\mathrm{q0}}}{a b s\big(\mathrm{FactorTTM}_{\mathrm{q0}}\big)}} $$ $$ \mathrm{Factort} = \mathrm{FactorTTMq} + \mathrm{abs(FactorTTMq)} \times \left(\frac{90}{1}\right) $$ where t is the trading day, q is the financial report period (March 31, June 30, September 30, December 31)[36][38]. - **Model Evaluation**: Incorporating financial information improves the overall performance of the baseline model, especially before 2022. However, after 2023, the improvement is weaker or even negative[4][35][42]. - **Model Testing Results**: - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Model Name**: GRU with Simplified Financial Information - **Model Construction Idea**: Simplifying the financial indicators to only include important ones like net profit TTM and market value[45]. - **Model Construction Process**: - **Simplified Financial Information**: Only retaining important indicators like net profit TTM and market value, and incorporating them into the GRU model[45]. - **Model Evaluation**: Simplifying the financial indicators improves the overall performance of the model, especially before 2022. After 2023, the improvement is weaker but still positive[45][55]. - **Model Testing Results**: - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model - **Model Name**: Mixed Frequency Model (barra5d + daily GRU) - **Model Construction Idea**: Combining long-term and short-term prediction capabilities by integrating barra5d and daily GRU models[56][65]. - **Model Construction Process**: - **Input**: Combining the daily GRU model with the barra5d model, which is trained on 240-minute intraday data to predict the next 1-5 days' returns[56][65]. - **Model Evaluation**: The mixed frequency model significantly improves the performance of the barra5d model, especially after October 2024. Adding fundamental information further stabilizes the annual excess performance[65][72][80]. - **Model Testing Results**: - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78] Model Backtesting Results GRU Model - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model (barra5d + daily GRU) - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78]

Quantitative Models and Construction GRU Model - **Model Name**: GRU baseline model [2][3][14] - **Model Construction Idea**: The GRU model is designed to extract information from historical price and volume data to predict future returns. It serves as a baseline to evaluate the impact of adding financial data [14][15]. - **Model Construction Process**: - **Data Range**: All A-share stocks (excluding Beijing Stock Exchange) from 2013-01-01 to 2025-04-30 [16]. - **Input Features**: Past 240 trading days' price and volume data, including open price, high price, low price, close price, trading volume, turnover, and turnover rate. Each feature is standardized using z-score [16]. - **Prediction Target**: Next month's standardized return (from the opening price at the beginning of the month to the closing price at the end of the month) [16]. - **Training**: Rolling training with a 4:1 split between training and validation sets over the past six years. Early stopping is applied if the loss function does not decrease for 10 consecutive iterations [16]. - **Portfolio Construction**: Enhanced portfolio based on the CSI 1000 index, with constraints on stock weight deviation (1%), style deviation (within 0.1 standard deviation), and industry deviation (1%). Monthly rebalancing with a turnover rate of 50% per side [18]. - **Model Evaluation**: The GRU model demonstrates stable performance in extracting price-volume information, achieving consistent excess returns across years [19]. GRU Model with Financial Data - **Model Name**: GRU with financial data [4][24][25] - **Model Construction Idea**: Incorporates financial data into the GRU model to enhance its ability to predict future returns by combining price-volume and fundamental information [14][24]. - **Model Construction Process**: - **Financial Data**: Includes 20 fields from income statements, such as revenue, cost of goods sold, management expenses, R&D costs, and net profit. Data is converted to TTM (trailing twelve months) values [24][25]. - **Integration**: Financial data is appended to the price-volume matrix, standardized, and input into the GRU model [25]. - **Adjustment**: To address frequency mismatches, financial data is adjusted daily based on the assumption of stable TTM growth rates. The adjustment formula is: $$ \text{Factor}_{t} = \text{Factor}_{\text{TTM}_{q}} + \text{abs}(\text{Factor}_{\text{TTM}_{q}}) \cdot \frac{90}{\text{days in quarter}} $$ where \( t \) is the trading day and \( q \) is the financial reporting quarter [36][38]. - **Model Evaluation**: Adding financial data improves performance before 2023 but weakens it afterward. Adjusting financial data enhances overall performance, especially in earlier years [42][45]. Mixed-Frequency GRU Model - **Model Name**: Mixed-frequency GRU model (barra5d + daily GRU) [5][56][65] - **Model Construction Idea**: Combines long-term and short-term prediction capabilities by integrating daily and intraday GRU models [56][65]. - **Model Construction Process**: - **Daily GRU**: Trained on 240 trading days of daily data to predict monthly returns [16]. - **Intraday GRU (barra5d)**: Trained on 240 minutes of intraday data to predict 5-day returns, neutralized for Barra style factors [56]. - **Integration**: The two models are combined to leverage their complementary strengths [65]. - **Model Evaluation**: The mixed-frequency model significantly improves stability and excess returns, addressing weaknesses in individual models [67][68]. Mixed-Frequency GRU with Financial Data - **Model Name**: Mixed-frequency GRU with financial data (barra5d + daily GRU + financial data) [5][73][74] - **Model Construction Idea**: Enhances the mixed-frequency model by incorporating selected financial data to improve stability and performance across years [73][74]. - **Model Construction Process**: - **Financial Data Selection**: Only key financial indicators, such as net profit TTM and market capitalization, are retained to avoid redundancy [45]. - **Integration**: Financial data is appended to the mixed-frequency model, following the same adjustment process as the GRU with financial data model [36][38]. - **Model Evaluation**: The addition of financial data further stabilizes annual excess returns and improves overall performance metrics [77][80]. --- Model Backtesting Results GRU Baseline Model - **Excess Annualized Return**: 8.75% [19][23] - **IR**: 2.25 [19][23] - **Maximum Drawdown**: 4.71% [19][23] GRU with Financial Data - **Excess Annualized Return**: 6.86% [32][33] - **IR**: 1.46 [32][34] - **Maximum Drawdown**: 6.14% [32][34] GRU with Adjusted Financial Data - **Excess Annualized Return**: 7.76% [41][44] - **IR**: 1.65 [41][44] - **Maximum Drawdown**: 5.40% [41][44] GRU with Selected Financial Data - **Excess Annualized Return**: 9.97% [51][52] - **IR**: 1.93 [51][52] - **Maximum Drawdown**: 5.70% [51][52] Mixed-Frequency GRU Model - **Excess Annualized Return**: 11.32% [68][69] - **IR**: 2.42 [68][69] - **Maximum Drawdown**: 8.19% [68][69] Mixed-Frequency GRU with Financial Data - **Excess Annualized Return**: 11.82% [77][78] - **IR**: 2.39 [77][78] - **Maximum Drawdown**: 5.70% [77][78]

Investment Rating - The investment rating for the company is "Buy" and is maintained [2][5]. Core Views - The company reported a 2024 annual revenue of 34.226 billion yuan, a year-on-year increase of 25.55%, and a net profit attributable to shareholders of 3.903 billion yuan, up 9.14% year-on-year [5]. - In Q1 2025, the company experienced a revenue decline of 2.59% year-on-year, with a net profit drop of 33.65% year-on-year, attributed to pressure on product prices [5]. - The company is expected to benefit from the gradual release of its production capacity and a share buyback plan, which reflects management's confidence in long-term growth [5]. Company Overview - The latest closing price is 20.86 yuan, with a total market capitalization of 44.3 billion yuan [4]. - The company has a debt-to-asset ratio of 29.6% and a price-to-earnings ratio of 11.33 [4]. Financial Performance - For 2024, the company achieved a revenue of 342 billion yuan, with projected revenues of 348 billion yuan in 2025, 385 billion yuan in 2026, and 408 billion yuan in 2027 [6]. - The net profit attributable to shareholders is projected to be 38.87 billion yuan in 2025, 44.62 billion yuan in 2026, and 50.45 billion yuan in 2027, with corresponding EPS of 1.83, 2.10, and 2.38 yuan [6][7]. - The company’s EBITDA is expected to be 77.99 billion yuan in 2025, increasing to 93.41 billion yuan by 2027 [6]. Market Position and Strategy - The company is recognized as a low-cost leader in the coal chemical industry, with ongoing projects aimed at enhancing production capabilities [5]. - The management's decision to initiate a share buyback of 200 to 300 million yuan indicates strong confidence in the company's future [5].

-17% -12% -7% -2% 3% 8% 13% 18% 23% 28% 2024-06 2024-08 2024-10 2025-01 2025-03 2025-06 社会服务 沪深300 证券研究报告：社会服务|点评报告 行业投资评级 强于大市|维持 | 行业基本情况 | | --- | | 收盘点位 | | 8037.22 | | --- | --- | --- | | 52 | 周最高 | 9343.57 | | 52 | 周最低 | 5985.5 | 行业相对指数表现（相对值） 资料来源：聚源，中邮证券研究所 研究所 交运数据：小个位数增长。据中国交通报数据，2025 年 5 月 31 日至 6 月 2 日（端午节假期期间），预计全社会跨区域人员流动量累 计 6.57 亿人次，日均 2.19 亿人次，同比去年端午增长 3.0%。其中， 铁路客运量日均同比增长2.3%，公路人员流动量日均同比增长3.14%， 水路客运量日均同比下降 1.65%，民航客运量日均同比增长 1.22%。 出入境：小个位数增长。据国家移民管理局数据，今年端午节假 期全国边检机关共计保障 590.7 万人次中外人员出入境， ...

证券研究报告：电力|点评报告 发布时间：2025-06-04 行业投资评级 强于大市 |维持 行业基本情况 | 收盘点位 | | 3180.98 | | --- | --- | --- | | 52 | 周最高 | 3359.79 | | 52 | 周最低 | 2868.51 | 行业相对指数表现（相对值） 2024-05 2024-08 2024-10 2024-12 2025-03 2025-05 -13% -10% -7% -4% -1% 2% 5% 8% 11% 14% 17% 电力 沪深300 资料来源：聚源，中邮证券研究所 研究所 分析师:杨帅波 SAC 登记编号:S1340524070002 Email:yangshuaibo@cnpsec.com 南方区域电力市场运行规则进一步完善，看好虚拟 电厂对绿电运营商的赋能 l 投资要点 事件：2025 年 5 月 23 日，国家能源局南方监管局发布《南方区 域电力市场运行规则（试行，2025 年 V1.0 版征求意见稿）》。 l 区域电力市场规则进一步完善 1.1 电能量交易_中长期(1)提升交易便捷度：缩短交易周期，提 高交易频次，实现周、多日、逐 ...

证券研究报告：电力|点评报告 发布时间：2025-06-04 强于大市 |维持 行业基本情况 | 收盘点位 | | 3180.98 | | --- | --- | --- | | 52 | 周最高 | 3359.79 | | 52 | 周最低 | 2868.51 | 行业相对指数表现（相对值） 2024-05 2024-08 2024-10 2024-12 2025-03 2025-05 -13% -10% -7% -4% -1% 2% 5% 8% 11% 14% 17% 电力 沪深300 资料来源：聚源，中邮证券研究所 研究所 分析师:杨帅波 SAC 登记编号:S1340524070002 Email:yangshuaibo@cnpsec.com 行业投资评级 1.2 电能量交易_现货（1）阻塞盈余：采用节点电价结算所产生 的阻塞盈余，可按规则分配给经营主体（2）出清价格：节点电价暂由 系统电能价格与阻塞价格两部分构成。 2 阻塞盈余的分配：初期可采用分配方式处理阻塞盈余，待条件 成熟时，可通过市场化方式拍卖输电权，由输电权拥有者获取相应的 阻塞收入。 3 结算规则（1）公式 1（中长期差价结算）：发电侧电 ...

Investment Rating - The industry investment rating is "Outperform the Market" and is maintained [2] Core Viewpoints - The agricultural sector index increased by 1.79%, ranking 4th among 31 primary industries, driven by policy expectations [4][12] - Supply pressure in the pig market remains significant, with policies aimed at stabilizing pig prices due to concerns about future price declines [5][20] - The white feather chicken market shows stable chick prices, with a focus on domestic breeding opportunities amid import uncertainties [27] Summary by Sections Market Performance - The agricultural sector index rose 1.79%, outperforming the Shanghai Composite Index, which fell by 0.03% [12] - Among sub-sectors, planting and fruit processing led the gains, while the breeding sector experienced fluctuations due to policy expectations [13] Breeding Industry Chain Tracking Pig Market - As of June 2, the average price of live pigs was 14.41 yuan/kg, remaining stable compared to the previous week [5][17] - Supply pressures are expected to continue due to an increase in breeding sows and high market weights of pigs [5][20] - Profitability for self-bred pigs is around 36 yuan per head, while purchased piglets incur losses of 84 yuan per head [18] - The industry anticipates a narrow fluctuation in pig prices for 2025, with controlled supply growth [21] White Feather Chicken - As of June 3, chick prices were stable at 3.00 yuan per chick, with meat chicken prices at 3.68 yuan/kg [27] - The industry is currently well-supplied, with high levels of parent stock, despite uncertainties in imports due to avian influenza [27] Planting Industry Chain Tracking - Sugar prices have adjusted downwards, with white sugar priced at 6090 yuan/ton as of June 3, a decrease of 65 yuan/ton [30] - The price of Brazilian soybeans was 3640 yuan/ton, reflecting a 2.0% decrease [30] - Cotton prices showed slight fluctuations, priced at 14550 yuan/ton, down 0.1% [30] - Corn prices experienced a minor decline, averaging 2332 yuan/ton [32]