Quantitative Model and Construction - **Model Name**: Utility Index Trend Tracking Model [3] - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, always following a certain trend. Reversal periods are significantly shorter than trend continuation periods. In cases of narrow-range consolidation, the model assumes the continuation of the previous trend. When observing a large-scale trend, a short observation window is used to capture 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 random noise. [4] - **Model Construction Process**: 1. Calculate the difference between the closing price on day T and day T-20, denoted as `del`. 2. Calculate the volatility (`Vol`) from day T-20 to day T (excluding day T). 3. 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 (long/short) corresponds to the sign of `del`. 4. If the absolute value of `del` is less than or equal to `N` times `Vol`, the current trend direction is assumed to continue, matching the direction on day T-1. 5. For stock markets with higher volatility compared to bond markets, `N` is set to 1 for tracking. 6. Combine the returns from both long and short directions to evaluate the final strategy 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, and $ \bar{P} $ is the average price over the observation window. [4] - **Model Evaluation**: The model is not suitable for direct application to the Utility Index due to its inability to achieve significant cumulative returns and its poor adaptability during periods of continuous market fluctuations, leading to sustained drawdowns. [5] Model Backtesting Results - **Annualized Return**: -16.67% [4] - **Annualized Volatility**: 15.99% [4] - **Sharpe Ratio**: -1.04 [4] - **Maximum Drawdown**: 32.10% [4] - **Total Return of Index During Period**: -0.35% [4]

Group 1 - The report indicates that the overall market liquidity has strengthened, with a net inflow of funds amounting to 46.46 billion yuan, despite a decrease in trading activity, as the total trading volume for the week was 4.54 trillion yuan, down from the previous week [8][9][11] - The net outflow of margin financing reached 193.49 billion yuan, while the trading volume of margin financing accounted for 8.75% of the total A-share trading volume [27][34] - The issuance scale of new equity funds was 4.87 billion yuan for IPOs and 69.67 billion yuan for refinancing, indicating a mixed demand for capital [36][39] Group 2 - The report highlights a significant net withdrawal of funds from the open market, totaling 5019 billion yuan, leading to a decrease in the yield spread between 10-year and 1-year government bonds [11][12] - The yield on 10-year government bonds decreased by 8 basis points, while the yield on 1-year government bonds fell by 5 basis points, resulting in a narrowing of the yield curve [11][12] - The market anticipates a 51% probability that the Federal Reserve will not cut interest rates in May, reflecting a shift in market expectations [19] Group 3 - The report notes a decline in turnover rates across major indices, with a corresponding decrease in trading volumes, indicating reduced trading activity among institutional investors [20][22] - The top five sectors where equity funds increased their positions included pharmaceuticals, banking, food and beverage, agriculture, and public utilities, while the sectors with the largest reductions included power equipment, household appliances, electronics, automotive, and non-ferrous metals [23][24] - The total number of ETF shares increased by 269.7 billion, with the broad-based index A500 ETF receiving the most inflow of funds [28][31]

Investment Rating - The report recommends a "Buy" rating for multiple companies in the pharmaceutical sector, including Junshi Biosciences, Hualing Pharmaceutical-B, Aorite, Tonghe Pharmaceutical, and others [3]. Core Insights - The pharmaceutical sector has outperformed the market, with a 1.20% increase, surpassing the CSI 300 index by 2.57 percentage points. Sub-sectors such as innovative drugs, new medical infrastructure, and pharmacies performed well, while pharmaceutical outsourcing, medical devices, and hospitals lagged behind [6][36]. - There is a significant unmet need for Obstructive Sleep Apnea (OSA) treatment, with GLP-1RA drugs showing remarkable efficacy. The FDA approved Tirzepatide as the first and only prescription drug for treating moderate to severe OSA in adults with obesity [5][26]. Summary by Sections Industry Perspective and Investment Recommendations - OSA is linked to various health issues, including hypertension, and has a high prevalence among adults in China, with 176 million affected. The prevalence of hypertension among OSA patients is notably high [16][17]. - Investment strategies should focus on innovative drugs, particularly in the context of increased liquidity and risk appetite in the market. The upcoming AACR and ASCO meetings are expected to catalyze interest in biotech innovations [30][31]. Industry Performance - The pharmaceutical sector's performance is highlighted, with innovative drugs and medical infrastructure leading the gains. The overall industry P/E ratio stands at 26.88, with a premium of 30.38% compared to the broader A-share market [36]. Company Dynamics - Notable company updates include: - Fuyuan Pharmaceutical reported a revenue of 3.446 billion yuan for 2024, a 3.17% increase year-on-year [37]. - Jingxin Pharmaceutical announced a share buyback totaling approximately 350 million shares [37]. - Heng Rui Medicine received approval for a new indication for its innovative drug, indicating ongoing development and regulatory progress [37].

Group 1 - The market shows a divergence in performance, with stable, micro-cap, and dividend stocks performing the best, while the ChiNext Index and Shenzhen Component Index lag behind [11][13] - The utility, pharmaceutical, and agriculture sectors saw the highest gains, while the computer, power equipment, and home appliance sectors performed the weakest [13][14] - The relative PE of the ChiNext Index to the CSI 300 has decreased, indicating a decline in growth stock valuations compared to blue-chip stocks [18] Group 2 - The overall valuation of the A-share market has shown a slight increase, remaining near one standard deviation from the mean, indicating a high allocation value for A-shares [20] - The PEG perspective suggests that dividend and financial stocks have the lowest PEG values, indicating a higher allocation value, while the PB-ROE perspective shows that the Sci-Tech 50 and growth styles have the lowest PB-ROE values, suggesting a lower premium for growth [21] - The overall valuation of major indices has declined, with the consumer sector showing relatively low valuations compared to historical levels [26][28] Group 3 - The valuation of various industries is differentiated, with non-bank financials, coal, utilities, transportation, and agriculture at near one-year lows [35] - The consumer sector's PB-ROE is currently low, indicating potential investment opportunities [42] - The technology sector is currently experiencing high valuation levels, with concepts like "East Data West Computing," Huawei Harmony, and robotics at elevated historical valuation percentiles [44] Group 4 - Profit expectations across industries have been generally revised downwards, with the agriculture sector seeing the largest upward adjustment and the real estate sector experiencing the largest downward adjustment [47]

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]