Meta 入局人形机器人，工程机械需求持续复苏 [Table_Industry] 机械行业 | | | ——机械行业周报 | [Table_Invest] 评级： | | 增持 | | --- | --- | --- | | | 上次评级: | 增持 | | [table_Authors] 肖群稀(分析师) | 徐乔威(分析师) | 刘麒硕(研究助理) | | | --- | --- | --- | --- | | 0755-23976830 | 021-38676779 | 0755-23976666 | | | xiaoqunxi027589@gtjas.com | xuqiaowei023970@gtjas.com | liuqishuo028693@gtjas.com | [Table_Report] 相关报告 | | 登记编号S0880522120001 | S0880521020003 | S0880123070153 | 机械行业《新时达发布定增预案，海尔集团或入 | 本报告导读： 上周（2025/2/10-2025/2/14）机械设备指数涨跌幅为+0.23%。关注人形机器人等 领域科技创新，工 ...

Investment Rating - The report does not explicitly state an investment rating for the industry or specific strategies [1]. Core Insights - The report tracks the performance of various quantitative asset allocation strategies, indicating that the domestic asset risk parity strategy achieved a return of 0.41% in January 2025, with the same return for the year to date [1][5]. - The macroeconomic indicators suggest a weak recovery in domestic demand, with a manufacturing PMI of 49.1% and a non-manufacturing PMI of 50.2% as of January 2025, indicating potential for growth [35][36]. - Inflation is projected to remain stable with a January CPI of 0.6% and a PPI of -1.8%, with expectations of a possible negative CPI in February [36]. Summary by Sections 1. Asset Performance Review - In January 2025, equity assets adjusted, with SHFE gold rising by 4.6%, and the Nanhua Commodity Index increasing by 2.54%. Conversely, the CSI 300 and CSI 1000 indices fell by 2.99% and 1.87%, respectively [7][8]. 2. Asset Correlation Tracking - The report highlights the importance of low correlation among assets for effective diversification. For January 2025, the correlation between the CSI 300 and the Nanhua Commodity Index was only 0.59, indicating potential for risk mitigation through diversification [10][12]. 3. Asset Allocation Strategy Tracking - The domestic asset BL strategy 1 recorded a return of 0.34% with a maximum drawdown of 0.39% and an annualized volatility of 0.48% since the beginning of 2025 [26][30]. - The domestic risk parity strategy achieved a return of 0.41% with a maximum drawdown of 0.12% and an annualized volatility of 0.22% [31]. - The macro factor-based asset allocation strategy yielded a return of 0.38% with similar drawdown and volatility metrics as the risk parity strategy [38]. 4. Macroeconomic Factors - The report assigns macroeconomic scores based on growth, inflation, interest rates, credit, exchange rates, and liquidity, with scores of 1, 0, -1, 0, 0, and 0, respectively, reflecting a cautious outlook on growth and inflation [35][38].

linyidan030085@gtjas.com 021-38038436 请参阅附注免责声明 1 90 00 67% 2024 ( ) 7.5% 0 10 20 30 40 50 00后 90后 80后 70后 70前 2023 2024 请参阅附注免责声明 2 犬 猫 2024 ( ) 3000 0 500 1000 1500 2000 2500 3000 3500 2018 2019 2020 2021 2022 2023 2024 2025E 2025 150% 60% - 请参阅附注免责声明 3 70% 50% 0 2000 4000 6000 8000 10000 12000 14000 2017 2018 2019 2020 2021 2022 2023 2024 犬 猫 53% 2024 60% 5 40% 20pct 2024 68% 2024H1 8.66 61.2% 9.74pct 60% 0.0% 5.0% 10.0% 15.0% 0% 50% 100% 2019 2020 2021 2022 2023 2024 线上占比 线下占比 线上增长率 线下增长率 | 品牌名称 | 品牌名称 ...

Investment Rating - The report suggests a positive outlook for cloud service providers due to Deepseek's reduction in hardware computing power demand, indicating a new growth momentum in the short term for local deployment in large enterprises and specific industries [1] Core Insights - Deepseek aims to enhance unit computing efficiency by tenfold through algorithm optimization, significantly lowering model training and inference costs. The training cost for the 671B DeepSeek V3 is $5.576 million (approximately 40.7 million RMB), which is only 7% of Llama 3's cost, while OpenAI's ChatGPT-4o training costs range from $78 million to $100 million, requiring thousands of NVIDIA H100 chips. In contrast, DeepSeek V3 utilizes the NVIDIA H800, a specialized AI chip with reduced performance [1] - The technological revolution of Deepseek introduces a new paradigm in large model development, employing innovative architectures like MoE and MLA for efficient inference and cost-effective training. The dynamic sparse expert network design allows the model to utilize less than 4% of neural network parameters during inference, and the FP8 low-precision training framework reduces energy consumption by 80% while maintaining model stability [2] - Open-source models like DeepSeek are expected to play a crucial role in the AI era, similar to how Android transformed the mobile internet. This will reshape the industry ecosystem, accelerating the development of upper-layer applications and unifying lower-layer systems, thereby enhancing collaboration across software, hardware, and supply chains [3] Summary by Sections - **Investment Outlook**: The report highlights the potential for local domestic inference computing to explode, alongside the expansion of new foundational software like vector databases [1] - **Technological Innovations**: Deepseek's introduction of reinforcement learning-driven paradigms and self-evolving training mechanisms significantly reduces the data annotation requirements for efficient training, showcasing a systematic disruption of the "computing power arms race" [2] - **Market Impact**: By reducing reliance on high-end imported chips, Deepseek provides a viable technological path for domestic enterprises, boosting confidence in the development of self-researched computing power chips [3]

Investment Rating - The report maintains an "Overweight" rating for the industry [1] Core Insights - A new perspective on assessing physical volume rhythm through the industry chain balance sheet has been established, which helps explain and predict the rhythm of physical volume demand, indicating a potential stabilization point in year-on-year physical volume by the second half of 2025 [1][2] - The construction industry has not yet shown signs of a right-side balance sheet contraction point as of Q3 2024, but it is reasonable to infer that the debt levels are near stabilization, with major enterprises' debt ratios recovering to around 75%, suggesting that key behavioral change points in the industry are approaching [2] - The demand is highly sensitive to funding, with a focus on downstream capital injection being crucial for future demand elasticity, particularly in infrastructure financing and real estate sales [3] Summary by Sections Industry Overview - The industry is transitioning to a balance sheet contraction model, with real estate enterprises leading this trend since 2021, which has been a primary factor in the decline of physical volume in 2022-2023 [1] - Infrastructure entities are expected to start their balance sheet contraction in 2024, which will significantly impact physical volume [1] Financial Performance - The construction materials sector is anticipated to show a favorable base effect in revenue starting from Q3 2024, with leading companies in the sector expected to demonstrate improved financial performance post-2025H2 due to industry optimization and competitive advantages [3] Future Outlook - The key inflection point for the current cycle of physical volume decline is projected to occur in the second half of 2025, with potential early signs observable from the first half of 2025 [2]

核能周报|欧洲商业核能联盟成立 摘要: 产业政策及动态 产业研究中心 | ર્ | 陈磊(分析师) | | --- | --- | | ల | 021-38038037 | | ಡಿ | chenlei022459@gtjas.com | | 登记编号 | S0880522060001 | | ર્ | 王浩(分析师) | | 8 | 0755-23976068 | | ಶಿ | wanghao013539@gtjas.com | | 登记编号 | S0880513090004 | 具备颁发建造许可证的条件。(来源:生态环境部) 鲍雁辛(分析师) 0755-23976830 baoyanxin@gtjas.com 登记编号 S0880513070005 在期回顾 欣界能源获海目星、亿航智能增资,三方持续深 化产业链合作 -- 低空产业跟踪 03 期 2025.02.16 【AI产业跟踪】李飞飞团队创新的极简测试拓展 方法,极低成本构建比肩 ol-preview 的推理模型 s1-32B 2025.02.13 【智能车产业跟踪】小鹏 MONA M03 Max 版将 于二李度交付,文远知行发布无人物流车 2025 ...

债券研究 /[Table_Date] 2025.02.17 | | | 债 ——REITs 周报 本报告导读： 过去一周 REITs 指数整体上行，产权类 REITs 表现相对较好，其中仓储类和产业园 类 REITs 领涨，水利和保障房类 REITs 表现相对一般。 投资要点： | 板块补涨行情延续 | [Table_Authors] | 刘玉(分析师) | | --- | --- | --- | | | | 021-38038263 | | ——REITs 周报 | | liuyu024100@gtjas.com | | | 登记编号 | S0880523050002 | | 本报告导读： | | | | 过去一周 REITs 指数整体上行，产权类 REITs 表现相对较好，其中仓储类和产业园 | | 汤志宇(研究助理) | | 类 REITs 领涨，水利和保障房类 REITs 表现相对一般。 | | 021-38031036 | | 投资要点： | | tangzhiyu028695@gtjas.com | | | 登记编号 | S0880123070149 | [Table_Report] 相关报告 新 ...

Investment Rating - The report suggests a potential valuation recovery for the liquor sector, indicating that recent stock price movements have largely reflected market pessimism, with expectations of policy improvements supporting this outlook [1][2]. Core Insights - The liquor industry is experiencing a stable fundamental environment, with the "opening red" performance in 2025 aligning with expectations and a general post-festival stability in operations. The report anticipates a decline in the overall liquor market by less than 10%, with sales revenue expected to drop more than volume due to price declines [2]. - Recent macroeconomic policies have become more positive, with a focus on boosting consumption as a priority for domestic demand expansion. Initiatives such as the issuance of consumption vouchers are expected to stimulate related sectors, particularly dining [1]. - The report highlights that the liquor industry is currently facing high inventory levels, prompting companies to focus on inventory reduction and channel profitability. Major brands have begun controlling inventory and adjusting sales strategies to manage cash flow [2]. Summary by Sections - **Market Performance**: The liquor sector's performance during the "opening red" period in 2025 was in line with expectations, with a projected decline in market size of less than 10%. Mid to low-end liquor products are outperforming high-end counterparts [2]. - **Policy Environment**: Recent government policies are aimed at enhancing consumer spending, with specific measures to support income growth and consumption capabilities. This is expected to positively influence the liquor market [1]. - **Valuation and Stock Performance**: As of February 12, stock prices for major liquor brands have adjusted to reflect pessimistic market expectations, with valuations for key companies like Kweichow Moutai and Wuliangye at relatively low levels, suggesting potential for recovery [2].

Group 1: Economic Theory and Challenges - The "Balassa-Samuelson effect" indicates that differences in actual exchange rates and inflation levels between two countries are fundamentally determined by productivity differences in their tradable sectors, particularly manufacturing[1] - Higher productivity in the tradable sector leads to higher price levels and stronger currency purchasing power[1] - Despite significant productivity improvements in China, the country faces low price levels and exchange rate stability pressures, indicating a disconnect from classical theory[1] Group 2: Wage Transmission Issues - The transmission of productivity gains to wages, especially in the service sector, is crucial for the "Balassa-Samuelson effect" to hold[2] - Labor compensation in East Asian countries, including China, is generally lower than in Western countries, hindering the transmission of productivity to wages[2] - Since 2017, the growth rate of labor compensation in China has slowed, with a decline noted in 2021, despite significant productivity gains in manufacturing[2] Group 3: Fiscal Policy Implications - The allocation of fiscal funds is more important than the scale of spending; effective transmission of "productivity-wage-inflation" is essential for the "Balassa-Samuelson effect" to be realized[3] - Current market expectations for increased fiscal deficits and stimulus may reflect traditional investment-focused fiscal thinking, which may not yield the desired economic impact[3] Group 4: Risk Considerations - There are discrepancies between international and historical experiences compared to China's unique circumstances, which may affect the applicability of the findings[4] - Inconsistencies in statistical definitions across international comparative data may also pose challenges to the analysis[4]

Investment Rating - The report recommends investment in DeepSeek R1, highlighting its algorithm optimization that reduces performance requirements for computing chips, thus accelerating the adaptation of domestic computing power [1]. Core Insights - DeepSeek R1 is an open-source, low-cost solution that addresses the core bottleneck of advanced processes, facilitating the growth of domestic computing power. It offers excellent cost-performance ratio, matching the performance of o1 models and is expected to lead the domestic AI ecosystem [2]. - The demand for inference drives the expansion of total computing power, with ASIC chips taking on more inference tasks. DeepSeek R1's high inference performance is expected to promote the prosperity of inference chip clusters and AI applications, with long-term investments in AI infrastructure shifting towards inference computing [2]. - The performance of the distilled small model in DeepSeek R1 is impressive, accelerating the deployment of edge AI and significantly enhancing hardware experience, thus opening up growth opportunities for edge hardware [3].