Investment Rating - The industry investment rating is "Outperform the Market" [1] Core Viewpoints - The ongoing conflict in the Middle East, particularly the Israel-Iran tensions, is expected to boost military trade, leading to a potential expansion in the military trade market [4][12] - The report highlights significant stock price increases for domestic military trade-related companies following the India-Pakistan conflict, with some companies seeing price increases exceeding 50% [5][17] - The military industry is anticipated to experience a turning point in orders as the "Centenary of the Army Building Goals" enters its second half, with new technologies and products expected to drive market growth [18] Summary by Sections Industry Overview - The closing index for the defense industry is 1524.38, with a 52-week high of 1712.48 and a low of 1113.62 [1] Market Performance - The military sector indices showed a weekly increase, with the CSI Military Index rising by 0.55% and the Shenwan Military Index increasing by 1.03%, while the broader market indices declined [19] - The top-performing stocks in the military sector for the week included Guorui Technology (+15.94%) and Chenxi Aviation (+13.98%) [21] Investment Recommendations - Two main investment themes are suggested: 1. Aerospace and "gap-filling" new focuses, including companies like Feilihua and YF Electronics [18] 2. New technologies, products, and market opportunities with greater elasticity, including companies like Aerospace Intelligence and Guorui Technology [18] Valuation Levels - As of June 13, 2025, the CSI Military Index stands at 10786.39, with a PE-TTM valuation of 104.83 and a PB valuation of 3.33, indicating that 80.24% of the time since January 1, 2014, the PE-TTM valuation has been lower than the current level [23][26]

行业相对指数表现 -13% -8% -3% 2% 7% 12% 17% 22% 27% 32% 37% 2024-06 2024-08 2024-11 2025-01 2025-04 2025-06 机械设备 沪深300 资料来源：聚源，中邮证券研究所 证券研究报告：机械设备|行业周报 发布时间：2025-06-17 行业投资评级 强于大市|维持 | 行业基本情况 | | | | --- | --- | --- | | 收盘点位 | | 1609.53 | | 52 | 周最高 | 1803.12 | | 52 | 周最低 | 1122.98 | 研究所 分析师:刘卓 SAC 登记编号:S1340522110001 Email:liuzhuo@cnpsec.com 分析师:傅昌鑫 SAC 登记编号:S1340524070004 Email:fuchangxin@cnpsec.com 近期研究报告 《Figure 机器人展示物流环境良好适 应性，泛化能力突破有望加速实际应 用》 - 2025.06.10 人形机器人行业周度报告 （2025.06.09-2025.06.15） 智能养老场景应用政策发布，长期看好人 ...

证券研究报告：宏观报告 研究所 分析师:李起 SAC 登记编号:S1340524110001 Email:liqi2@cnpsec.com 研究助理:高晓洁 SAC 登记编号:S1340124020001 Email:gaoxiaojie@cnpsec.com 近期研究报告 《出口展现韧性，中美谈判进展或促风 险偏好修复》 - 2025.06.10 宏观观点 海外宏观周报：美国通胀数据缓和 ⚫ 核心观点： 发布时间：2025-06-16 | 1 海外宏观数据跟踪 4 | | --- | | 1.1 上周重要事件与数据跟踪 4 | | 1.2 本周重要数据与事件 8 | | 2 美联储观点跟踪 9 | | 3 风险提示 9 | 周三公布的美国通胀数据好于预期，反映前期补库行为可能部分 抵消了关税影响。从分项上看，领先指标新租约租金数据指示，核心 服务中的住房分项预计仍将持续回落。同时，申领失业金人数上升显 示劳动力市场正在松动，或将进一步拖累核心服务 CPI 中的其他分 项。中东局势升级为油价带来一定的上行风险，但鉴于双方实力差距， 地缘政治对油价的影响可能偏向短期。美联储方面，尽管通胀风险缓 和，但关税影响或 ...

Investment Rating - The investment rating for the company is "Buy" and is maintained [1] Core Views - The company has shown strong growth in its simulation and power management platforms, with a capacity utilization rate remaining at full load. In Q1 2025, the sales revenue reached 541 million USD with a gross margin of 9.2%, aligning with guidance. The overall performance continues the trend from 2024, with steady sales growth and ongoing product structure optimization [4] - The revenue contributions from various technology platforms in Q1 2025 include 130 million USD from embedded non-volatile memory, 42.9 million USD from standalone non-volatile memory, 163 million USD from power devices, 66.8 million USD from logic and RF, and 137 million USD from analog and power management, with year-on-year growth rates of 9.3%, 38.0%, 13.5%, 4.0%, and 34.8% respectively. The analog and power management platform is highlighted as a key growth area due to strong technical capabilities and increasing demand related to AI applications [4] - The company anticipates maintaining this growth trend throughout the remainder of the year, which is a key reason for ongoing capacity expansion [4] Financial Performance - The company expects to achieve revenues of 166.90 billion CNY, 193.79 billion CNY, and 220.65 billion CNY for the years 2025, 2026, and 2027 respectively, with corresponding net profits of 5.61 billion CNY, 8.61 billion CNY, and 13.72 billion CNY. The current stock price corresponds to a price-to-book ratio (PB) of 1.82, 1.79, and 1.74 for the years 2025 to 2027 [6] - The projected revenue growth rates are 16.00%, 16.11%, and 13.86% for the years 2025, 2026, and 2027 respectively, with a significant recovery expected in net profit growth rates of 47.36%, 53.49%, and 59.40% for the same years [9][12]

Quantitative Models and Construction Diffusion Index Model - **Model Name**: Diffusion Index Model [6][26] - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance. It selects industries with positive momentum for rotation. [26] - **Model Construction Process**: - The model calculates a diffusion index for each industry, which reflects the proportion of stocks within the industry exhibiting upward momentum. - Industries are ranked based on their diffusion index values, and the top industries are selected for portfolio allocation. [6][27] - **Model Evaluation**: The model has shown strong performance in capturing trends during momentum-driven markets but struggles during market reversals or when trends shift to mean-reversion. [26] - **Testing Results**: - 2025 YTD excess return: -0.44% [25][30] - June 2025 excess return: 1.20% [30] - Weekly average return: 0.21%, excess return over equal-weighted industry index: 0.37% [30] GRU Factor Model - **Model Name**: GRU Factor Model [7][32] - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry rotation opportunities. [37] - **Model Construction Process**: - The model uses minute-level price and volume data as input features. - A GRU neural network is trained to predict industry factor scores, which are then used to rank industries for rotation. [37] - **Model Evaluation**: The model performs well in short-term trading environments but faces challenges in long-term trend-following scenarios, especially during extreme market conditions. [37] - **Testing Results**: - 2025 YTD excess return: -4.13% [32][35] - June 2025 excess return: 0.00% [35] - Weekly average return: 0.42%, excess return over equal-weighted industry index: 0.58% [35] --- Backtesting Results of Models Diffusion Index Model - **YTD Excess Return**: -0.44% [25][30] - **June 2025 Excess Return**: 1.20% [30] - **Weekly Average Return**: 0.21% [30] - **Weekly Excess Return**: 0.37% [30] GRU Factor Model - **YTD Excess Return**: -4.13% [32][35] - **June 2025 Excess Return**: 0.00% [35] - **Weekly Average Return**: 0.42% [35] - **Weekly Excess Return**: 0.58% [35] --- Quantitative Factors and Construction GRU Industry Factor - **Factor Name**: GRU Industry Factor [7][33] - **Factor Construction Idea**: The factor is derived from GRU neural network outputs, representing the relative attractiveness of industries based on high-frequency trading data. [37] - **Factor Construction Process**: - The GRU model processes minute-level trading data to generate factor scores for each industry. - Industries are ranked based on their factor scores, and the top industries are selected for portfolio allocation. [37] - **Factor Evaluation**: The factor effectively captures short-term trading signals but may underperform in broader market trends or during periods of concentrated market themes. [37] - **Testing Results**: - Top industries by factor score (as of June 13, 2025): Steel (2.42), Construction (1.47), Transportation (0.85), Real Estate (0.59), Utilities (-0.01), Oil & Gas (-1.52) [7][33] - Bottom industries by factor score: Food & Beverage (-49.88), Comprehensive Finance (-33.65), Consumer Services (-25.42), Media (-21.94), Automotive (-20.34), Non-Banking Finance (-18.36) [33] Diffusion Index Factor - **Factor Name**: Diffusion Index Factor [6][27] - **Factor Construction Idea**: The factor measures the proportion of stocks within an industry showing upward momentum, serving as a proxy for industry strength. [6] - **Factor Construction Process**: - Calculate the diffusion index for each industry based on the percentage of stocks with positive momentum. - Rank industries by their diffusion index values to identify the strongest performers. [6][27] - **Factor Evaluation**: The factor is effective in identifying momentum-driven industries but may lag during market reversals. [26] - **Testing Results**: - Top industries by diffusion index (as of June 13, 2025): Comprehensive Finance (1.0), Non-Banking Finance (0.997), Banking (0.97), Media (0.953), Computing (0.936), Retail (0.93) [6][27] - Bottom industries by diffusion index: Coal (0.166), Oil & Gas (0.297), Food & Beverage (0.323), Utilities (0.604), Real Estate (0.629), Building Materials (0.657) [27] --- Backtesting Results of Factors GRU Industry Factor - **Top Industries by Factor Score**: Steel (2.42), Construction (1.47), Transportation (0.85), Real Estate (0.59), Utilities (-0.01), Oil & Gas (-1.52) [7][33] - **Bottom Industries by Factor Score**: Food & Beverage (-49.88), Comprehensive Finance (-33.65), Consumer Services (-25.42), Media (-21.94), Automotive (-20.34), Non-Banking Finance (-18.36) [33] Diffusion Index Factor - **Top Industries by Diffusion Index**: Comprehensive Finance (1.0), Non-Banking Finance (0.997), Banking (0.97), Media (0.953), Computing (0.936), Retail (0.93) [6][27] - **Bottom Industries by Diffusion Index**: Coal (0.166), Oil & Gas (0.297), Food & Beverage (0.323), Utilities (0.604), Real Estate (0.629), Building Materials (0.657) [27]

- The report tracks style factors including profitability, volatility, and momentum, which showed strong long positions, while nonlinear market capitalization, valuation, and leverage factors demonstrated strong short positions[3][16] - Barra style factors include Beta (historical beta), market capitalization (logarithm of total market capitalization), momentum (mean of historical excess return series), volatility (weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility), nonlinear market capitalization (third power of market capitalization style), valuation (inverse of price-to-book ratio), liquidity (weighted turnover rates across monthly, quarterly, and yearly periods), profitability (weighted combination of analyst forecast earnings-price ratio, inverse cash flow ratio, and inverse trailing twelve-month PE ratio), growth (weighted combination of earnings growth rate and revenue growth rate), and leverage (weighted combination of market leverage, book leverage, and debt-to-asset ratio)[15] - GRU factors demonstrated strong multi-directional performance across various stock pools, with models like barra5d showing particularly strong positive returns[4][5][7] - GRU long-only portfolio outperformed the CSI 1000 index with excess returns ranging from 0.06% to 0.95% this week, while the barra5d model achieved a year-to-date excess return of 7.75%[8][30][31]

证券研究报告：固定收益报告 福建属于传统意义上的好区域，不在重点省份的名单之列，经济 财政实力未处于第一梯队，但好在债务负担也偏轻。从 2024 的数据 表现来看，福建单论经济财政实力并不算特别突出，只能属于"第二 梯队"。福建省 2024 年的 GDP 为 57,761.02 亿元，一般预算收入 2,329.73 亿元，政府性基金收入为 1,787.51 亿元。但福建省的杠杆 程度同样不算高，2024 年福建政府债务结构分布较为均衡，从债务绝 对规模来看，与贵州省较为接近。 发布时间：2025-06-16 研究所 分析师:梁伟超 SAC 登记编号:S1340523070001 Email:liangweichao@cnpsec.com 分析师:李书开 SAC 登记编号:S1340524040001 Email:lishukai@cnpsec.com 近期研究报告 《杠杆可以更积极点——流动性周报 20250615》 - 2025.06.16 信用周度观点 漳州：境外债的机会有多大？ ——信用周报 20250616 ⚫ 漳州：境外债的机会有多大？ 漳州在福建地级市排序中属于"独一档"的中等偏上的水平，经 济财 ...

Report Information - Report Type: Fixed Income Report - Release Date: June 16, 2025 - Analysts: Liang Weichao, Cui Chao [2] Investment Rating - No industry investment rating is provided in the report. Core Views - High - frequency economic data shows production differentiation, with the steel industry chain in decline and some sectors like asphalt, semi - steel tires, and PTA seeing an uptick. The real estate market is experiencing a marginal decline, and price trends are also diverse. Shipping indices are at relatively high levels. Short - term focus should be on new growth - stimulating policies, the real estate market recovery, and US tariff policy changes [2][31] Summary by Directory Production - In the week of June 13, the utilization rate of coke oven capacity decreased by 0.97 pct, blast furnace operating rate dropped by 0.15 pct, and rebar production decreased by 10.89 tons. The operating rate of petroleum asphalt increased by 0.2 pct, PX operating rate decreased by 0.3 pct, PTA operating rate increased by 0.26 pct, full - steel tire operating rate decreased by 2.23 pct, and semi - steel tire operating rate increased by 4.12 pct [3][9][10] Demand - In the week of June 8, the transaction area of commercial housing decreased, the inventory - to - sales ratio increased, land transaction area declined, and the premium rate of residential land transactions decreased. Movie box office decreased by 110 million yuan compared to the previous week. Automobile manufacturers' daily average retail sales decreased by 52,500 vehicles, and daily average wholesale sales decreased by 117,800 vehicles. In the week of June 13, the SCFI dropped by 6.79%, CCFI rose by 7.63%, and BDI increased significantly by 20.51% [3][13][15][18] Prices - In the week of June 13, Brent crude oil prices rose by 11.67% to $74.23 per barrel, coking coal futures prices dropped by 0.71% to 773.5 yuan per ton, LME copper, aluminum, and zinc futures prices changed by - 0.24%, + 2.10%, - 1.35% respectively, and domestic rebar futures prices fell by 0.70%. The overall price of agricultural products decreased, with pork, eggs, vegetables, and fruits changing by - 0.98%, - 3.58%, - 0.46%, - 0.13% respectively compared to the previous week [3][21][22][24] Logistics - In the week of June 14, subway passenger volume in Beijing and Shanghai increased, domestic flight volume increased while international flight volume decreased, and the peak congestion index in first - tier cities rebounded. Beijing's subway passenger volume increased by 261,400 person - times with a weekly change of 2.82%, Shanghai's increased by 795,700 person - times with a weekly change of 8.67%. Domestic (excluding Hong Kong, Macao, and Taiwan) flight volume increased by 58 flights with a weekly change of 0.47%, international flight volume decreased by 69.43 flights with a weekly change of - 3.78%, and the first - tier city peak congestion index increased by 0.14 with a weekly change of 9.05% [4][26][28] Summary - Steel production declined, and crude oil prices increased significantly. High - frequency economic data shows production differentiation, a marginal decline in the real estate market, diverse price trends, and relatively high shipping indices. Short - term focus should be on new growth - stimulating policies, the real estate market recovery, and US tariff policy changes [31]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The diffusion index is used to monitor the critical points of market changes and predict potential turning points in the micro-cap stock index[5][35] - **Model Construction Process**: The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window. For example, if the horizontal axis is 0.95 and the vertical axis is 15 days, a value of 0.19 indicates that if all constituent stocks drop by 5% after 5 days, the diffusion index value will be 0.19. The formula and methodology are not explicitly provided but rely on historical and predictive data of constituent stocks[35][37] - **Model Evaluation**: The model is effective in identifying market turning points and provides actionable signals for left-side and right-side trading strategies[5][36] 2. Model Name: First Threshold Method (Left-Side Trading) - **Model Construction Idea**: This method triggers a sell signal when the diffusion index reaches a predefined threshold, indicating a left-side trading opportunity[5][39] - **Model Construction Process**: The first threshold method triggered a sell signal on May 8, 2025, when the diffusion index reached 0.9850[39] - **Model Evaluation**: It is suitable for early identification of market risks but may lead to premature signals in volatile markets[5][39] 3. Model Name: Delayed Threshold Method (Right-Side Trading) - **Model Construction Idea**: This method delays the sell signal until the diffusion index reaches a lower threshold, providing a more conservative approach[5][41] - **Model Construction Process**: The delayed threshold method triggered a sell signal on May 15, 2025, when the diffusion index reached 0.8975[43] - **Model Evaluation**: It reduces the risk of false signals but may miss early opportunities[5][43] 4. Model Name: Dual Moving Average Method (Adaptive Trading) - **Model Construction Idea**: This method uses two moving averages to adapt to market trends and trigger trading signals[5][44] - **Model Construction Process**: The dual moving average method triggered a sell signal on June 11, 2025[44] - **Model Evaluation**: It balances between early and delayed signals, making it suitable for dynamic market conditions[5][44] --- Model Backtesting Results 1. Diffusion Index Model - Current value: 0.70[35] - Predicted low point: Next Wednesday, with a potential cumulative decline of over 6%[5][36] 2. First Threshold Method - Triggered sell signal: May 8, 2025, at 0.9850[39] 3. Delayed Threshold Method - Triggered sell signal: May 15, 2025, at 0.8975[43] 4. Dual Moving Average Method - Triggered sell signal: June 11, 2025[44] --- Quantitative Factors and Construction Methods 1. Factor Name: PE_TTM Inverse Factor - **Factor Construction Idea**: Measures the inverse of the price-to-earnings ratio based on trailing twelve months (TTM) earnings[4][15] - **Factor Construction Process**: Rank IC for the week: 0.187 (historical average: 0.018)[4][15] - **Factor Evaluation**: Demonstrates strong predictive power for the week, significantly outperforming its historical average[4][15] 2. Factor Name: Profitability Factor - **Factor Construction Idea**: Evaluates the profitability of constituent stocks[4][15] - **Factor Construction Process**: Rank IC for the week: 0.177 (historical average: 0.023)[4][15] - **Factor Evaluation**: Shows robust performance, exceeding historical averages[4][15] 3. Factor Name: Unadjusted Stock Price Factor - **Factor Construction Idea**: Tracks the raw stock price without adjustments for splits or dividends[4][15] - **Factor Construction Process**: Rank IC for the week: 0.13 (historical average: -0.017)[4][15] - **Factor Evaluation**: Positive performance, reversing its historical negative trend[4][15] 4. Factor Name: Dividend Yield Factor - **Factor Construction Idea**: Measures the dividend yield of stocks[4][15] - **Factor Construction Process**: Rank IC for the week: 0.118 (historical average: 0.022)[4][15] - **Factor Evaluation**: Consistently outperforms its historical average[4][15] 5. Factor Name: PB Inverse Factor - **Factor Construction Idea**: Measures the inverse of the price-to-book ratio[4][15] - **Factor Construction Process**: Rank IC for the week: 0.116 (historical average: 0.034)[4][15] - **Factor Evaluation**: Strong weekly performance, well above historical norms[4][15] --- Factor Backtesting Results Top 5 Factors (Weekly Rank IC) 1. PE_TTM Inverse Factor: 0.187[4][15] 2. Profitability Factor: 0.177[4][15] 3. Unadjusted Stock Price Factor: 0.13[4][15] 4. Dividend Yield Factor: 0.118[4][15] 5. PB Inverse Factor: 0.116[4][15] Bottom 5 Factors (Weekly Rank IC) 1. 1-Year Volatility Factor: -0.188[4][15] 2. 10-Day Free Float Turnover Factor: -0.166[4][15] 3. Residual Volatility Factor: -0.143[4][15] 4. Trading Volume Factor: -0.142[4][15] 5. 10-Day Total Market Cap Turnover Factor: -0.139[4][15]

Market Performance Review - The A-share market experienced slight declines, with most major indices falling, except for the ChiNext Index, which was the only major index to rise, primarily driven by blue-chip stocks [3][12] - The performance of the A-share market was influenced by external political events, including the Israel-Iran conflict, which heightened global market risk aversion and led to significant increases in gold and oil prices [3][16] - The overall market did not establish a new trading theme, continuing the pattern of new consumption stocks rising and then retreating, alongside the valuation recovery of innovative pharmaceuticals [3][16] A-share High-Frequency Data Tracking - The personal investor sentiment index showed slight recovery, with a 7-day moving average of 4.6% as of June 14, up from -4.0% on June 7, indicating a shift from persistent pessimism to a more normalized trading phase [4][17] - The financing transaction volume in the A-share market has seen a notable decline, reflecting a decrease in investor enthusiasm, although there was a slight net inflow this week [20] - The current state of industry rotation is characterized by high speed and low intensity, suggesting a market environment prone to sideways movement [21][23] Future Market Outlook and Investment Views - The report anticipates continued market volatility, with external factors such as US tariffs and the Israel-Iran conflict potentially causing further impacts on the A-share market [4][30] - The A-share market may see upward movement during the internal policy window in July, with expectations for stimulus policies in consumption and real estate sectors [4][30] - The recommendation is to focus on dividend stocks with good value, particularly in sectors like banking, transportation, and utilities, while waiting for clearer internal demand stimulus policies to catalyze traditional consumption trades [5][30]