2025 年 04 月 07 日 行业周报 看好/维持 医药 医药 板块持续跑赢大盘，关注对等关税下医药供应链影响（附 OSA 专题研究）(2025.03.31-2025.04.06) 走势比较 OSA 存在未满足需求，GLP-1RA 药物对于 OSA 治疗具有显著效果。阻 塞性睡眠呼吸暂停（obstructive sleep apnea，OSA）是指由于气道变窄 或阻塞而导致睡眠期间的呼吸中断，与高血压的发生发展密切相关，可合 并多种代谢异常、内分泌激素紊乱等，导致心脑血管事件发生风险明显增 加。根据流行病学研究，目前中国成年人中 OSA 患者有 1.76 亿，在糖尿 病及肥胖患者中更为常见，住院 T2DM 患者 OSA 的患病率在 60%以上，肥 胖的 T2DM 患者 OSA 的患病率可达 86%，90%以上 BMI 超过 40 kg/m2 的肥 胖症患者合并 OSA。减重是 OSA 重要的治疗方式，GLP-1RA 药物对于 OSA 治疗有显著效果，礼来的替尔泊肽的 3 期临床 SURMOUNT OSA 研究结果显 示，在肥胖合并中重度 OSA 患者中，与安慰剂相比，替尔泊肽显著降低了 受试者 AHI（呼 ...

2025 年 04 月 07 日 投资策略 估值与盈利周观察——4 月第 1 期：市场分化，红利上成长下 ◼ 上证综合指数走势（近三年） (20%) (12%) (4%) 4% 12% 20% 24/3/18 24/5/29 24/8/9 24/10/20 24/12/31 25/3/13 证券分析师：张冬冬 市场表现：稳定、微盘股、红利表现最好；创业板指、深圳成指、成 长表现最弱。 行业表现：公用事业、医药生物、农林牧渔涨幅前三，计算机、电力 设备、家用电器表现最弱。 相对估值：创业板指/沪深 300 相对 PE 下降，创业板值/沪深 300 相 对 PB 下降。 全 A ERP：ERP 小幅上升，处于一倍标准差附近。 指数 PEG & PB-ROE: 红利 PEG 值最小，科创 50 风格 PB-ROE 值最小。 大类行业估值：原材料、设备制造、工业服务、交通运输、消费、科 技历史估值皆处于 50%以下，金融地产估值高于 50%历史分位。 一级行业估值：各行业估值分化，非银金融、煤炭、公用事业、交通 运输、农林牧渔估值处于近一年低位。 热门概念：科技板块热度较高，东数西算、华为鸿蒙、机器人、中字 头央企等 ...

金 金融工程点评 [Table_Message]2025-04-07 医药生物指数趋势跟踪模型效果点评 [Table_Author] 证券分析师：刘晓锋 电话：13401163428 E-MAIL：liuxf@tpyzq.com 执业资格证书编码：S1190522090001 研究助理：孙弋轩 电话：18910596766 E-MAIL：sunyixuan@tpyzq.com 一般证券业务登记编码：S1190123080008 模型概述 结果评估： 区间年化收益：6.44% 波动率（年化）：24.71% 夏普率：0.26 最大回撤：22.65% 指数期间总回报率：-19.00% 融 工 程 点 评 ◼ 设计原理：模型假定标的价格走势具有很好的局部延续性，标的价格永远处 于某一趋势中，出现反转行情的持续时间明显小于趋势延续的时间，若出现 窄幅盘整的情况，亦假设其延续之前的趋势。当处于大级别的趋势之中时， 给定较短时间的观察窗口，走势将延续观察窗口内的局部趋势。而当趋势发 生反转时，在观察窗口始末位置的价格变动方向会明显超出随机波动造成的 趋势背离范围，从而排除随机波动的影响。虽然指数本身在实际中进行双向 操作有 ...

Quantitative Models and Construction Methods - **Model Name**: Light Industry Manufacturing Index Trend Tracking Model **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, always following a certain trend. Reversal trends are shorter in duration compared to trend continuation. In cases of narrow-range consolidation, the model assumes the continuation of the previous trend. When observing a large-scale trend, the price movement within a short observation window will extend the local trend. Reversals are identified when price changes at the start and end of the observation window exceed the range caused by random fluctuations, eliminating the impact of randomness[3][4] **Model Construction Process**: - Calculate the difference `del` between the closing price on day T and day T-20 - Compute the volatility `Vol` for the period from day T-20 to day T (excluding day T) - If the absolute value of `del` exceeds N times `Vol`, the current price is considered to have exited the original oscillation range and formed a trend. The trend direction corresponds to the sign of `del`. If not, the trend direction is assumed to continue as per day T-1 - For stock markets with higher volatility, N is set to 1 for tracking - Combine the returns from both long and short directions to evaluate the strategy's overall performance Formula: $ del = P_{T} - P_{T-20} $ $ Vol = \sqrt{\frac{1}{20} \sum_{i=1}^{20} (P_{T-i} - \bar{P})^2} $ where $ P_{T} $ is the closing price on day T, $ P_{T-20} $ is the closing price on day T-20, and $ \bar{P} $ is the average price over the observation period[3][4] **Model Evaluation**: The model achieved high returns during the tracking period but experienced significant drawdowns in the early and middle stages. It is not suitable for direct application to the Light Industry Manufacturing Index[4] Model Backtesting Results - **Light Industry Manufacturing Index Trend Tracking Model**: - Annualized Return: 17.68%[3] - Annualized Volatility: 24.10%[3] - Sharpe Ratio: 0.73[3] - Maximum Drawdown: 24.90%[3] - Total Return of Index During Period: -16.46%[3]

- The industry crowding monitoring model is constructed to monitor the crowding level of Shenwan first-level industry indices daily[3] - The Z-score model is used to build a signal screening model for related ETF products, providing potential arbitrage opportunities[4] Model Construction Process - Industry crowding monitoring model: The model monitors the crowding level of Shenwan first-level industry indices daily, identifying industries with high and low crowding levels[3] - Z-score model: The model calculates the Z-score of the premium rate for ETF products, identifying potential arbitrage opportunities and warning of potential pullback risks[4] Model Evaluation - Industry crowding monitoring model: The model effectively identifies industries with significant crowding levels, providing valuable insights for investment decisions[3] - Z-score model: The model is useful for identifying potential arbitrage opportunities in ETF products, but investors should be cautious of potential pullback risks[4] Model Testing Results - Industry crowding monitoring model: The model identified high crowding levels in the pharmaceutical, environmental protection, and steel industries, while communication and media industries had low crowding levels[3] - Z-score model: The model provided signals for potential arbitrage opportunities in various ETF products, including the Real Estate ETF, Gold ETF, Green Power ETF, and Biopharmaceutical ETF[15] Factor Construction Process - Industry crowding factor: The factor is constructed based on the daily monitoring of the crowding level of Shenwan first-level industry indices[3] - Premium rate Z-score factor: The factor is constructed by calculating the Z-score of the premium rate for ETF products[4] Factor Evaluation - Industry crowding factor: The factor is effective in identifying industries with significant crowding levels, providing valuable insights for investment decisions[3] - Premium rate Z-score factor: The factor is useful for identifying potential arbitrage opportunities in ETF products, but investors should be cautious of potential pullback risks[4] Factor Testing Results - Industry crowding factor: The factor identified high crowding levels in the pharmaceutical, environmental protection, and steel industries, while communication and media industries had low crowding levels[3] - Premium rate Z-score factor: The factor provided signals for potential arbitrage opportunities in various ETF products, including the Real Estate ETF, Gold ETF, Green Power ETF, and Biopharmaceutical ETF[15]

Group 1: Major Asset Tracking - The bond market shows a mixed performance with long-term bonds rising and short-term bonds slightly declining. The market has stabilized near the six-month line, indicating potential for allocation [16] - The stock market saw the Shanghai Composite Index rise by 0.38%, with a correction in overly pessimistic sentiment. The pharmaceutical and controllable nuclear fusion sectors experienced significant gains [19] - The foreign exchange market reported the onshore RMB against the USD at 7.2684, up 165 basis points from the previous close, indicating a strong support level around 7.1 [26] - The commodity market saw the Wenhua Commodity Index increase by 0.8%, led by gains in oil, coal, and construction materials, while feed, oils, and live pig sectors declined [33] Group 2: Important Policies and News - Domestic policies include support for insurance companies to establish private equity funds for long-term stock market investments, reflecting a push for financial innovation [36] - The March Caixin China Manufacturing PMI rose to 51.2, the highest in four months, indicating continued expansion in manufacturing activities [36] - Internationally, the Australian central bank maintains a cautious outlook on monetary policy, emphasizing the importance of returning inflation to target levels [39]

Investment Rating - The steel industry is rated as Neutral, indicating that the expected overall return in the next six months will be between -5% and 5% relative to the CSI 300 index [2][10]. Core Insights - The steel industry experienced an overall decline on April 1, 2025, with the Shanghai Composite Index increasing by 0.38% while the Shenzhen Component and ChiNext indices saw slight decreases [2]. - Tangshan's 26 steel enterprises have all completed environmental certifications, leading the nation in this regard. The total loan balance for the steel industry in Tangshan exceeds 160 billion yuan, with green loans accounting for 150 billion yuan [5]. - Jiangsu Province's steel industry revenue reached 1.58 trillion yuan in 2024, with high-end special steel accounting for over 35% for the first time. Jiangsu is undergoing a strategic transformation from "scale-driven" to "value-driven" [5]. Market Performance - The top three gainers in the steel sector include Hengxing Technology (+10.13%), Honghai Technology (+5.96%), and Jiuli Special Materials (+5.15%). The top three decliners are Hangang Co. (-3.37%), Zhongnan Co. (-1.11%), and Shagang Co. (-1.05%) [3]. - Futures data shows slight fluctuations in steel products, with rebar down by 0.09% and iron ore up by 1.86% [4]. Industry Data - Current prices for steel products are as follows: iron ore at 766.43 yuan/ton, rebar at 3209.35 yuan/ton, and hot-rolled coil at 3373.27 yuan/ton [4]. - The iron ore Platts index indicates prices of 116.25 for 65% powder, 88.2 for 58% powder, and 103.85 for 62% powder [4]. Company Announcements - The Panzhihua Steel Group's lime kiln denitrification project commenced operations on March 18, 2025, significantly reducing nitrogen oxide emissions [6][8].

Investment Rating - The industry rating is optimistic, expecting an overall return exceeding 5% above the CSI 300 index in the next six months [7] Core Insights - The Indian API (Active Pharmaceutical Ingredients) support plan has not met expectations, with delays in capacity release [1] - The Indian government initiated the API PLI (Production-Linked Incentive) plan and API park plan in 2020, with a total expected expenditure of 69.4 billion INR (approximately 795 million USD) to promote domestic production of 53 APIs [2] - Progress on the construction of API parks in Gujarat, Himachal Pradesh, and Andhra Pradesh is slow, with an extended deadline to Q1 2026 [3] - The overall implementation of the API PLI plan has been unsatisfactory, with insufficient fund utilization over three consecutive years [4] - The release of new API capacity in India will take additional time, with a potential realization of capacity release around 2027-2028, while Chinese API companies maintain a comparative advantage [4] Summary by Sections Industry Overview - The Indian API support plan's implementation has been less effective than anticipated, leading to a delay in capacity release [1] - The construction of three API parks is progressing slowly, with a revised completion timeline set for March 2026 [3] Financial Insights - The total financial assistance allocated to the three states for API park construction is 43.71 billion INR for Gujarat, 26.05 billion INR for Himachal Pradesh, and 35.73 billion INR for Andhra Pradesh, with varying levels of fund utilization [3] Investment Opportunities - Investment opportunities in the API sector include companies expanding into formulation and CDMO (Contract Development and Manufacturing Organization) fields, those with a high proportion of new product business, and companies with significant potential for earnings recovery due to inventory destocking [6]

- Industry crowding monitoring model is constructed to monitor the crowding level of Shenwan first-level industry indices daily[3] - The Z-score model is used to build ETF product screening signal models, providing potential arbitrage opportunities and warning of potential pullback risks[4] Model Backtesting Results - Industry crowding monitoring model, steel, environmental protection, and mechanical equipment have high crowding levels, while food and beverage, real estate, and building materials have low crowding levels[3] - Z-score model, ETF products with potential arbitrage opportunities include integrated circuit ETF, Southeast Asia technology ETF, and medical ETF[12] ETF Product Daily Net Inflow/Outflow - Broad-based ETF: A500 Index ETF (+7.91 billion yuan), CSI 1000 ETF (+7.10 billion yuan), A500 Index ETF (+7.02 billion yuan)[6] - Industry theme ETF: Securities ETF (+3.69 billion yuan), Brokerage ETF (+2.34 billion yuan), Robot ETF (+2.06 billion yuan)[6] - Style strategy ETF: Free cash flow ETF (+1.13 billion yuan), Dividend low volatility ETF (+0.90 billion yuan), Dividend low volatility 50 ETF (+0.90 billion yuan)[6] - Cross-border ETF: Hong Kong Stock Connect Internet ETF (+11.93 billion yuan), Hong Kong Stock Connect Technology 30 ETF (+5.95 billion yuan), Hang Seng Technology ETF (+1.45 billion yuan)[6] ETF Product Daily Net Outflow - Broad-based ETF: A500 ETF Fund (-3.38 billion yuan), CSI 500 ETF (-2.84 billion yuan), CSI 300 ETF Huaxia (-1.66 billion yuan)[6] - Industry theme ETF: Bank ETF Long (-2.67 billion yuan), Chemical ETF (-0.71 billion yuan), Semiconductor Equipment ETF (-0.59 billion yuan)[6] - Style strategy ETF: Dividend ETF Fund (-1.68 billion yuan), Dividend ETF E Fund (-0.30 billion yuan), Dividend low volatility ETF Xinhua (-0.19 billion yuan)[6] - Cross-border ETF: Hang Seng Medical ETF (-2.10 billion yuan), Dividend ETF Hong Kong stocks (-1.39 billion yuan), Hong Kong Stock Connect Innovative Drug ETF (-0.87 billion yuan)[6]

Investment Rating - The investment rating for the company is "Buy/Maintain" with a target price of 17.68, compared to the last closing price of 13.90 [1]. Core Insights - The company's profitability continues to improve, with a positive outlook for the coming year. The 2024 annual report indicates a revenue of 3.476 billion, a year-on-year increase of 10.41%, and a net profit of 625 million, up 36.77% year-on-year. The fourth quarter saw a revenue of 1.112 billion, a 21.57% increase year-on-year, and a net profit of 192 million, up 41.03% year-on-year [4][5]. Summary by Sections Financial Performance - In 2024, the company achieved a total revenue of 34.76 billion, with a net profit of 6.25 billion. The fourth quarter's revenue was 11.12 billion, with a net profit of 1.92 billion. The company also distributed a total dividend of 6.1 billion, representing a payout ratio of 97.62% [4][5]. - The company’s revenue from various product lines in 2024 was as follows: Chinese seasoning (1.771 billion, +16.6%), hot pot base (1.265 billion, +3.5%), and sausages and cured meats (329 million, +11.0%) [4][5]. - The company’s gross margin improved by 1.9 percentage points to 39.8%, while the sales expense ratio decreased by 2.4 percentage points to 13.0% [4][5]. Future Outlook - For 2025, the company aims for a revenue and profit growth of no less than 15%. The projected revenues for 2025-2027 are 39.89 billion, 44.35 billion, and 49.23 billion, with corresponding net profits of 7.2 billion, 8.2 billion, and 9.0 billion [5][7]. - The company plans to accelerate product innovation and expand its distribution channels, with new products expected to contribute to growth. The acquisition of online companies is aimed at enhancing online marketing advantages [5][7]. Valuation Metrics - The projected PE ratios for 2025, 2026, and 2027 are 21, 18, and 17, respectively. The target price is based on a 26x PE for 2025 earnings [5][7].