Employment Data - In November 2025, the U.S. added 64,000 non-farm jobs, exceeding the expected increase of 50,000 and recovering from a loss of 105,000 jobs in October[15] - The unemployment rate rose to 4.6%, higher than the expected 4.4%[15] - Average hourly earnings increased by 3.5% year-on-year, slightly below the expected 3.6%[15] Economic Insights - The rise in unemployment is attributed to a "technical" disruption from the government shutdown, which temporarily inflated the unemployment figures due to forced leave of federal employees[2] - Private sector employment remains resilient, with the goods-producing sector adding 19,000 jobs, the highest since May 2025[2] - Retail data for October showed stability, with core retail sales growth exceeding expectations, indicating that consumer spending, which accounts for nearly 70% of U.S. GDP, is stabilizing[2] Federal Reserve Outlook - Despite the unexpected rise in unemployment, the Federal Reserve is likely to maintain a cautious approach to interest rate cuts in the short term[5] - The market anticipates two rate cuts in 2026, with probabilities of 44.1% in April and 34.5% in July, while the probability of pausing rate cuts in January 2026 stands at 73.4%[24] Labor Market Dynamics - The labor force participation rate increased to 62.5% in November, up from 62.4% in September, indicating a recovery in employment willingness among younger demographics[4] - The number of unemployed individuals rose by 228,000 in November, reflecting the impact of the government shutdown on temporary unemployment[4] Wage Growth and Inflation - Wage growth showed signs of slowing, with a month-on-month increase of only 0.1% in November, down from 0.4% in October[42] - Year-on-year wage growth also decreased to 3.5%, compared to 3.7% in October, suggesting reduced inflationary pressures[42]

Investment Rating - The report gives the company an "Accumulate" rating for the first time [6][14]. Core Viewpoints - The company is a leading domestic player in cardiac electrophysiology, providing a comprehensive three-dimensional cardiac electrophysiology solution, with strong technical barriers and a complete product matrix [4][14]. - The domestic electrophysiology market is experiencing rapid growth, driven by favorable policies and increasing demand for innovative technologies, with the company positioned to capture significant market share [2][3]. Company Overview - The company, established in 2010, focuses on innovative medical devices for electrophysiology intervention and ablation therapy, becoming the first domestic manufacturer to offer a complete solution in this field [21]. - The company has a stable shareholding structure, with no single shareholder holding more than 10%, ensuring balanced governance [24][27]. Financial Performance - The company is projected to achieve revenue of 413 million yuan in 2024, representing a year-on-year growth of 25.51%, and a net profit of 52 million yuan, with a staggering growth of 815.36% [5][14]. - Earnings per share (EPS) are forecasted to be 0.13, 0.18, and 0.26 yuan for 2025, 2026, and 2027 respectively, with corresponding price-to-earnings (P/E) ratios of 187, 131, and 88 [4][5]. Market Dynamics - The domestic electrophysiology market is expected to grow significantly, with the market share of foreign brands decreasing from 93% in 2020 to 72% in 2024, indicating a strong trend towards domestic substitution [2][3]. - The company has successfully participated in multiple provincial procurement programs, demonstrating its clinical recognition and market competitiveness [2][3]. Product Development - The company has a comprehensive product line covering two-dimensional and three-dimensional systems, with several products filling domestic gaps and matching foreign competitors in performance [3][4]. - The company is actively developing new products in emerging fields such as pulse ablation and renal artery treatment, enhancing its growth potential [3][12]. Profitability Forecast - The company’s revenue from catheter products is expected to grow at rates of 16.00%, 28.00%, and 27.00% from 2025 to 2027, driven by increased market penetration and procurement benefits [10]. - The gross margin is anticipated to stabilize above 60% starting in 2025, following the launch of high-margin new products [11].

Macro Insights - The internal economic momentum is weakening, and the policy window is gradually approaching, with a focus on stabilizing consumption and investment through counter-cyclical policies [2] - The "pig cycle" investment paradigm is shifting, with supply reduction in the pig industry driven by profit losses and policy adjustments, but the elasticity of pig prices is expected to be weaker than in previous cycles [3] - The healthcare negotiations in the U.S. are becoming a political tool, with significant implications for the capital markets, as budget agreements remain unresolved [4] Bond Market Analysis - Major economic indicators have further declined, with industrial production growth slowing year-on-year, while fixed asset investment shows an expanding decline [6] - The bond market is currently experiencing a relatively loose funding environment, and investors are advised to adopt a gradually optimistic outlook, with a forecast for the 10-year government bond yield to stabilize around 1.75% [6][5] Real Estate Sector - In the real estate market, new home transactions in 20 cities totaled 735,000 units, down 14.8%, with significant declines in cities like Beijing and Shenzhen [7] - The second-hand housing market showed a slight increase in transactions, with a total of 725,000 units sold, indicating a mixed performance across major cities [7] Company Research - Zhaoli Pharmaceutical is set to acquire a range of trace element injection assets, which will enhance its product structure and leverage synergies, with a favorable market outlook for these products [9] - The acquisition is expected to significantly boost the company's revenue and profit, with projected net profits for 2025-2027 at 655 million, 836 million, and 1.063 billion yuan, respectively, corresponding to PE ratios of 18, 14, and 11 times [9]

Consumption - In November 2025, the year-on-year growth rate of social retail sales was 1.3%, below the expected 2.9%, marking the lowest point since February 2023[3] - The decline in consumption is attributed to last year's "trade-in" policy raising the base, and a decrease in service consumption after the long holiday[2] - The retail sales of five categories involved in the "trade-in" policy saw a decline, with home appliances and furniture experiencing negative year-on-year growth[4] Investment - From January to November 2025, fixed asset investment showed a cumulative year-on-year decrease of 2.6%, worse than the expected decrease of 2.2%[5] - In November, the year-on-year decline in fixed asset investment was -11.1%, with manufacturing investment improving slightly to -4.4%[13] - Infrastructure investment continued to decline, with narrow and broad infrastructure showing year-on-year decreases of -9.7% and -12.0%, respectively[19] Real Estate - In November 2025, the year-on-year growth rate of national commodity housing sales fell to -26.1%, down from -25.1% in October[23] - Real estate development investment saw a significant decline, with a year-on-year decrease of -31.4% in November, reaching a low level[23] - The two-year compound growth rate for commodity housing sales area improved slightly, from -11.1% in October to -7.9% in November[23]

2025 年 12 月 15 日 总量研究 "猪周期"投资的新范式 ——《光大投资时钟》第二十七篇 作者 分析师：赵格格 执业证书编号：S0930521010001 0755-23946159 zhaogege@ebscn.com 稳定币：从数字美元到霸权上链 ——《大国 博弈》系列第八十八篇（2025-07-25） 特朗普为何加速推进 232 调查？——《大国 博弈》第八十七篇（2025-07-09） 关税大限将至，特朗普如何抉择？——《大 国博弈》系列第八十六篇（2025-07-03） 以斗争求合作，中方打到美方筹码底线—— 《大国博弈》系列第八十五篇（2025-05- 12） 中美会晤前哨观察：特朗普的交易底线—— 《大国博弈》系列第八十四篇（2025-05- 10） 关税互搏，中美谁的经济韧性更强？—— 《大国博弈》系列第八十二（2025-04- 09） 分析师：刘星辰 执业证书编号：S0930522030001 021-52523880 liuxc@ebscn.com 相关研报 黄金"狂欢"未歇，铜价能否共舞？—— 《光大投资时钟》系列报告第二十六篇 （2025-10-21） 黄金周：黄金上涨的三个 ...

Investment Rating - The report maintains a "Buy" rating for Zhaoli Pharmaceutical (300181.SZ) with a current price of 17.18 yuan [1]. Core Views - The acquisition of the future pharmaceutical asset group is expected to enhance Zhaoli Pharmaceutical's performance by introducing new product lines and leveraging complementary advantages [5][8]. - The market for multi-trace element injection solutions is projected to grow, with significant demand in pediatric and adult nutrition support [6][7]. - The acquisition is valued at approximately 356 million yuan, corresponding to a PE ratio of about 6 times, indicating a favorable cost-benefit ratio [7][8]. Summary by Sections Company Overview - Zhaoli Pharmaceutical has a total share capital of 701 million shares and a market capitalization of 12.05 billion yuan [1]. - The stock has fluctuated between a low of 13.39 yuan and a high of 21.07 yuan over the past year [1]. Recent Developments - The company recently won a legal case against East China Pharmaceutical, which strengthens its market position [4]. - Zhaoli Pharmaceutical signed an agreement to acquire a multi-trace element injection asset group for 35.6 million yuan, which includes both marketed and research products [4][5]. Financial Performance - The asset group is expected to generate a net profit of approximately 60 million yuan in 2025, enhancing the company's profitability [7]. - The projected revenue for Zhaoli Pharmaceutical is expected to grow from 1.94 billion yuan in 2023 to 4.29 billion yuan by 2027, with a compound annual growth rate of 21.45% [9][13]. Market Potential - The overall market for multi-trace element injections is anticipated to reach around 1.8 billion yuan in 2024, with stable growth rates for existing products [6]. - The demand for these products is expected to continue rising, particularly in pediatric and adult critical care settings [6][11]. Valuation and Earnings Forecast - The report forecasts net profits of 655 million yuan in 2025, with a corresponding PE ratio of 18, indicating a positive outlook for the company's financial health [8][9]. - The company's return on equity (ROE) is projected to increase from 14.03% in 2023 to 28.18% by 2027, reflecting improved profitability [15].

分析师：赵格格 执业证书编号：S0930521010001 0755-23946159 zhaogege@ebscn.com 分析师：刘星辰 执业证书编号：S0930522030001 021-52523880 liuxc@ebscn.com 2025 年 12 月 15 日 总量研究 "猪周期"投资的新范式 ——《光大投资时钟》第二十七篇 作者 相关研报 黄金"狂欢"未歇，铜价能否共舞？—— 《光大投资时钟》系列报告第二十六篇 （2025-10-21） 黄金周：黄金上涨的三个新变量——《光大 投资时钟》系列报告第二十五篇（2025- 10-08） 美国政府停摆：可能性与市场影响——《大 国博弈》系列第八十九篇（2025-09-25） 稳定币：从数字美元到霸权上链 ——《大国 博弈》系列第八十八篇（2025-07-25） 特朗普为何加速推进 232 调查？——《大国 博弈》第八十七篇（2025-07-09） 关税大限将至，特朗普如何抉择？——《大 国博弈》系列第八十六篇（2025-07-03） 以斗争求合作，中方打到美方筹码底线—— 《大国博弈》系列第八十五篇（2025-05- 12） 中美会晤前哨观察：特朗 ...

1. Report Industry Investment Rating No relevant content provided. 2. Core View of the Report The report systematically reviews the evolution of financial time - series forecasting models and constructs a prediction model for China's 10 - year treasury bond yield using a long - short - term memory (LSTM) neural network with historical time series as the single input variable, initially exploring the application of this deep - learning model in the fixed - income quantitative field [10]. 3. Summary by Relevant Catalog 3.1 Financial Time - Series Forecasting and Neural Network Models 3.1.1 Evolution of Financial Time - Series Forecasting Models Financial time - series forecasting has gone through three main development stages: traditional econometric models, traditional machine - learning models, and deep - learning models. Traditional econometric models have clear forms and strong interpretability but struggle to depict nonlinear and complex dynamic relationships. Traditional machine - learning models can perform nonlinear fitting and automatic feature screening but need manual feature extraction. Deep - learning models can automatically extract features from raw data and capture complex long - term time - series patterns, adapting well to the complex characteristics of financial time series [11][12]. 3.1.2 Neural Network Models and LSTM Models Neural network models are machine - learning models imitating the connection structure of human brain neurons. Recurrent neural networks (RNN) and their variants, such as LSTM, are designed for processing sequence data. LSTM solves the long - term dependence problem of traditional RNN through a "gating mechanism" and memory units, enhancing robustness to irregular data and being suitable for bond yield prediction [13][18]. 3.2 Treasury Bond Yield Prediction Based on Stacked LSTM Model 3.2.1 Stacked LSTM Model Stacked LSTM connects multiple LSTM layers in sequence, having advantages in long - sequence processing and multi - dimensional feature extraction, more suitable for complex time - series forecasting in financial scenarios [23]. 3.2.2 Construction of Treasury Bond Yield Prediction Model The report uses a classic and robust architecture of three - layer stacked LSTM + Dropout regularization to build a neural network model for predicting the 10 - year treasury bond yield. It only uses the historical time series of the 10 - year treasury bond yield as a single variable for prediction. The data is from the beginning of 2021 to December 12, 2025. After data processing and sample construction, a medium - complexity LSTM neural network model with about 130,000 adjustable parameters is built. The optimal model is obtained at the 27th training iteration, with an average absolute error of 1.43BP for the test set. The predicted yield on December 19, 2025, is 1.8330%, slightly lower than 1.8396% on December 12, 2025 [2][24][30]. 3.3 Follow - up Optimization Directions - Optimize model design: Adjust and optimize the design related to time windows, data processing, network architecture, and training strategies [3][36]. - Input multi - dimensional variables: Expand input variables from a single yield sequence to multi - dimensional variables such as macro, market, and sentiment to make the model more in line with economic logic and capture more comprehensive information [3][36]. - Build hybrid models: Combine the LSTM model with traditional econometric models or other machine - learning models to build hybrid models like ARIMAX - LSTM and CNN - LSTM - ATT, enhancing prediction accuracy [3][36]. - Introduce a rolling back - testing mechanism: Use a rolling time - window back - testing mechanism to update the model dynamically and make continuous predictions, improving the model's adaptability to market changes [3][36].

Report Industry Investment Rating No relevant information provided. Core Viewpoints of the Report - The main economic indicators in November 2025 further declined, with the year - on - year growth rate of industrial added value, the cumulative year - on - year growth rate of fixed - asset investment, and the year - on - year growth rate of total retail sales of consumer goods all showing a downward trend. However, the month - on - month growth rate of industrial added value increased, and the month - on - month decline of fixed - asset investment narrowed. [1][2] - In the bond market, investors should gradually become more optimistic about the bond market. The expected fluctuation center of the 10Y Treasury bond yield is 1.75%. In the long term, convertible bonds are still relatively high - quality assets, but attention should be paid to the structure. [3] Summary by Relevant Catalogs Event - On December 15, 2025, the National Bureau of Statistics released the economic data for November 2025, including the year - on - year growth rate of industrial added value above a designated size of 4.8%, the cumulative year - on - year decline of fixed - asset investment from January to November of 2.6%, and the year - on - year growth rate of total retail sales of consumer goods in November of 1.3%. [1][6][9] Comment Scale - above industrial production: year - on - year growth rate decreased but month - on - month growth rate increased - In November 2025, the year - on - year growth rate of industrial added value above a designated size was 4.8%, a 0.1 - percentage - point decrease from October. The month - on - month growth rate was + 0.44%, an increase from October. [2][6] - Among the three major categories, the year - on - year growth rate of the mining industry increased, while those of the manufacturing industry and the production and supply of electricity, heat, gas, and water decreased. [2][6] January - November fixed - asset investment: cumulative year - on - year decline widened, but the month - on - month decline in November narrowed - From January to November 2025, the cumulative year - on - year growth rate of fixed - asset investment was - 2.6%, with the decline widening. The month - on - month growth rate in November was - 1.03%, with the decline narrowing. [2][13] - The cumulative year - on - year growth rates of real estate, manufacturing, and general infrastructure investment from January to November all decreased, and the year - on - year growth rates of the three sub - items in November were all weak. [17] Total retail sales of consumer goods: year - on - year growth rate continued to decline, and the month - on - month growth rate was weaker than the seasonal average - In November 2025, the year - on - year growth rate of total retail sales of consumer goods was 1.3%, a decrease from the previous month. The month - on - month growth rate was - 0.42%, weaker than the seasonal average and lower than the same - period levels in 2023 and 2024. [2][18] - The year - on - year growth rates of different types of consumer goods all decreased in November compared with the previous month. [2][18] Bond Market Views Interest - rate bonds - Since August 2025, the yield of Treasury bonds has shown obvious differentiation. The short - end yield has fluctuated little and declined steadily, while the long - end yield, especially the 30 - year yield, has been on an upward trend, and the Treasury bond yield curve has steepened significantly. [3][22] - With the current loose capital situation and the weak fundamental trend, investors should gradually become more optimistic about the bond market, and the expected fluctuation center of the 10Y Treasury bond yield is 1.75%. [3][22] Convertible bonds - Since the beginning of 2025 (as of December 12), the change rate of the CSI Convertible Bond Index was + 16.5%, and the change rate of the CSI All - Share Index was + 21.8%. The performance of the convertible bond market was weaker than that of the equity market. [3][31] - Against the background of the slow - bull expectation of the equity market and the pattern where the demand in the convertible bond market is stronger than the supply and difficult to change, convertible bonds are still relatively high - quality assets in the long term, and more attention should be paid to the structure. [3][31]

1. Report Industry Investment Rating No relevant information provided. 2. Core View of the Report The report systematically reviews the evolution of financial time - series prediction models and constructs a prediction model for China's 10 - year Treasury bond yield using the Long Short - Term Memory (LSTM) neural network, with historical time - series as the single input variable, to explore the application of this deep - learning model in the fixed - income quantitative field [10]. 3. Summary by Directory 3.1 Financial Time - Series Prediction and Neural Network Models - **Evolution of Financial Time - Series Prediction Models**: Financial time - series prediction has gone through three main stages: traditional econometric models (e.g., ARIMA, GARCH), traditional machine - learning models (e.g., SVM, RF), and deep - learning models. Traditional econometric models have clear forms but struggle with nonlinear and complex dynamic relationships. Traditional machine - learning models can perform nonlinear fitting but need manual feature extraction. Deep - learning models can automatically extract features and capture long - term patterns, with RNN and its variants like LSTM being mainstream methods [11][12]. - **Neural Network Models and LSTM Models**: Neural network models mimic the human brain's neuron connection structure. RNN is designed for sequence data but has issues with long - term memory. LSTM solves the long - term dependency problem of RNN through a "gating mechanism" and memory units, enhancing robustness to irregular data. It is suitable for bond yield prediction due to its ability to handle long - term time series and filter noise [13][17][18]. 3.2 Treasury Bond Yield Prediction Based on Stacked LSTM Model - **Stacked LSTM Model**: Stacked LSTM connects multiple LSTM layers sequentially, offering advantages in long - sequence processing and multi - dimensional feature extraction, making it more suitable for complex financial time - series prediction [23]. - **Construction of Treasury Bond Yield Prediction Model**: - **Data Processing and Sample Construction**: The data is the yield of the 10 - year Treasury bond from the beginning of 2021 to December 12, 2025. First - order differences are calculated and standardized. Samples are constructed with the first - order differences of the past 60 trading days as input features and the first - order differences of the next week as the prediction target. The samples are divided into a training set (72%), a validation set (8%), and a test set (20%) [27]. - **Model Design and Evaluation**: The model architecture consists of LSTM, Dropout, and Dense layers. The training strategy involves 200 iterations with an early - stopping mechanism. Evaluation metrics include MSE, MAE, and RMSE [28][29]. - **Model Results**: A medium - complexity LSTM neural network model with about 130,000 adjustable parameters is built. The optimal model is obtained at the 27th iteration, and the early - stopping mechanism is triggered at the 77th iteration. The average absolute error for the test set is 1.43BP. The 10 - year Treasury bond yield is predicted to decline from December 15 - 19, 2025, with the predicted value on December 19, 2025, being 1.8330%, slightly lower than 1.8396% on December 12, 2025 [30]. 3.3 Follow - up Optimization Directions - **Model Design Optimization**: Adjust and optimize relevant designs such as time windows, data processing, network architecture, and training strategies [3][36]. - **Input Multi - Dimensional Variables**: Expand input variables from a single yield sequence to multi - dimensional variables such as macroeconomic, market, and sentiment variables to make the model more in line with economic logic and capture more comprehensive information [3][36]. - **Construct Hybrid Models**: Combine the LSTM model with traditional econometric models or other machine - learning models to build hybrid models like ARIMAX - LSTM and CNN - LSTM - ATT, leveraging different model advantages and improving prediction accuracy [3][36]. - **Introduce Rolling Back - testing Mechanism**: Use a rolling time - window back - testing mechanism to update the model dynamically and make continuous predictions, enhancing the model's adaptability to market changes [3][36].