Investment Rating - The report does not provide specific investment ratings for sub-industries such as power station equipment, electrical equipment, power supply equipment, and new energy power systems [2]. Core Insights - The overall industry strategy indicates that energy storage and lithium batteries are expected to continue exceeding expectations, with price increases and AI+ providing flexibility. Emphasis is placed on the enhanced pricing power in the mid-to-upstream segments, suggesting that it is a favorable time for investment, focusing on the certainty of leading companies and the flexibility of upstream suppliers [3][6]. - The new energy vehicle supply chain is entering a new upward cycle, with strong pricing power in lithium battery segments benefiting companies like CATL, Hunan Youneng, Tianci Materials, and others. Recent data shows global lithium battery production reached 236.4 GWh in November 2025, a year-on-year increase of 44.6% [3][4]. - Solid-state batteries are highlighted as a key focus for 2026, with companies like Xiamen Tungsten and Putailai expected to benefit from advancements in production and cost control [4]. Summary by Sections Energy Storage and Lithium Batteries - The lithium battery sector is experiencing sustained high demand, with significant production increases projected. For instance, global energy storage battery production is expected to reach 960 GWh in 2026, up from 620 GWh in 2025, marking a 55% increase [3][36]. - A recent agreement between Zhongxin Innovation and Shengxin Lithium Energy to secure a five-year supply of 200,000 tons of lithium salt reflects the importance of upstream supply security in the lithium battery industry [3]. Solid-State Batteries - The solid-state battery segment is anticipated to see production ramp-up, with a focus on mass production processes and cost control. Recent IPO efforts by companies like Weilan New Energy indicate growing interest in this technology [4]. Photovoltaic and Energy Storage - The photovoltaic and energy storage sectors are expected to see gradual improvements in market conditions. Recent collaborations, such as between CATL and Siyuan Electric, aim to enhance energy storage capacity [5][6]. - The European energy storage market is projected to grow significantly, with 2,356 storage projects totaling 170.92 GW capacity identified, indicating a shift towards chemical energy storage solutions [5][26]. AI and New Energy - The integration of AI in the new energy sector is emphasized, with companies like Youbixun and Keda Li benefiting from advancements in robotics and AI technology [7]. - The report highlights a significant transformation in electricity pricing mechanisms, moving from government-set prices to market-driven pricing, which will impact energy storage investors and electricity users [24][25].

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of industries on a daily basis, using the Shenwan First-Level Industry Index as the benchmark[3] - **Model Construction Process**: The model calculates the crowding levels of various industries based on daily data. It identifies industries with high crowding levels (e.g., military and building materials) and low crowding levels (e.g., banking, computing, and media). The model also tracks changes in crowding levels over time to highlight significant variations, such as the large changes observed in the building decoration and real estate sectors[3] - **Model Evaluation**: The model provides actionable insights into industry crowding trends, helping investors identify potential opportunities and risks in specific sectors[3] 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to identify potential arbitrage opportunities in ETF products by calculating the Z-score of premium rates[4] - **Model Construction Process**: The model employs a rolling calculation of the Z-score for the premium rates of ETF products. The Z-score is used to determine whether an ETF is overvalued or undervalued relative to its historical premium rate distribution. This helps in identifying ETFs with potential arbitrage opportunities while also warning of potential pullback risks[4] - **Model Evaluation**: The model is effective in screening ETF products for arbitrage opportunities and provides a systematic approach to risk management[4] --- Model Backtesting Results 1. Industry Crowding Monitoring Model - **Key Observations**: - High crowding levels were observed in the military and building materials industries, while banking, computing, and media showed low crowding levels[3] - Significant changes in crowding levels were noted in the building decoration and real estate sectors[3] 2. Premium Rate Z-Score Model - **Key Observations**: - The model identified ETFs with potential arbitrage opportunities based on their premium rate Z-scores, though specific numerical results were not disclosed in the report[4] --- Quantitative Factors and Construction Methods No specific quantitative factors were explicitly mentioned in the report. --- Factor Backtesting Results No specific factor backtesting results were explicitly mentioned in the report. --- Additional Notes - The report primarily focuses on the construction and application of quantitative models for industry crowding monitoring and ETF product screening. It does not delve into individual quantitative factors or their backtesting results. - The models provide valuable insights for identifying market trends and potential investment opportunities, but specific numerical backtesting metrics (e.g., IR or Sharpe ratios) were not provided.

Investment Rating - The report maintains a "Buy" rating for the company [5] Core Insights - The company is a leading global Fabless chip supplier, expanding its product lines in storage, MCU, niche DRAM, sensors, and analog chips, benefiting from a recovery in downstream demand and opportunities arising from the exit of overseas competitors from niche markets [2][3] - The NOR Flash market is expanding due to AI-driven demand, with the company achieving a market share of 18.5%, ranking second globally and first in mainland China [3][26] - The exit of major overseas manufacturers from the DDR3 market has created significant opportunities for domestic manufacturers in the niche DRAM sector, with the company poised to capture market share [4][45] Summary by Sections I. Global Leading Fabless Chip Supplier - The company has a diversified layout in "sensing, storage, computing, control, and connectivity," continuously deepening its product offerings since its establishment in 2005 and listing in 2016 [7][9] II. AI-Driven Expansion of NOR Flash Market - The global NOR Flash market is projected to grow from $2.8 billion in 2024 to $4.2 billion by 2029, driven by AI applications in consumer electronics and automotive sectors [23][26] - The company has achieved a significant market share in NOR Flash, benefiting from the exit of major competitors and the increasing demand for AI-enabled devices [3][26] III. Optimized Supply Landscape for Niche DRAM - The company is well-positioned in the niche DRAM market, with a product line that includes DDR4 and LPDDR4, expected to see significant growth due to the exit of major players from the DDR3 market [4][45] - The global niche DRAM market is anticipated to grow from $8.5 billion in 2024 to $13.2 billion by 2029, driven by demand from industrial control, AI applications, and automotive electronics [43][45] IV. Profit Forecast and Investment Recommendations - Revenue projections for 2025-2027 are estimated at 94.23 billion, 119.46 billion, and 149.62 billion yuan, with corresponding net profits of 17.76 billion, 25.29 billion, and 31.68 billion yuan, reflecting strong growth rates [5][11]

- The industry crowding monitoring model was constructed to monitor the crowding level of Shenwan primary industry indices daily. The model identifies industries with high crowding levels, such as military, non-ferrous metals, and building materials, while industries like banking, computers, and media exhibit lower crowding levels. The model also tracks changes in crowding levels, highlighting significant variations in sectors like home appliances and environmental protection[3] - The Z-score premium rate model was developed to screen ETF products for potential arbitrage opportunities. This model uses rolling calculations to identify ETFs with significant deviations from their intrinsic value, providing signals for potential investment opportunities while warning of possible risks of price corrections[4] - The Z-score premium rate model's construction process involves calculating the Z-score of the premium rate for each ETF product. The formula for Z-score is: $ Z = \frac{(P - \mu)}{\sigma} $ where $ P $ represents the premium rate, $ \mu $ is the mean premium rate, and $ \sigma $ is the standard deviation of the premium rate. This calculation helps identify ETFs with significant deviations from their average premium rate[4] - The Z-score premium rate model is evaluated as a useful tool for identifying arbitrage opportunities in ETF products, but it requires caution due to potential risks associated with price corrections[4] - The industry crowding monitoring model is considered effective for tracking daily crowding levels and identifying significant changes in industry crowding dynamics, aiding in investment decision-making[3] - The Z-score premium rate model's testing results are not explicitly provided in the report[4]

Investment Rating - The industry rating is optimistic, expecting overall returns to exceed the CSI 300 index by more than 5% in the next six months [11] Core Insights - The report highlights that the real estate sector is experiencing a positive market trend, with the Shanghai Composite Index and Shenzhen Composite Index rising by 0.53% and 1.04% respectively on December 24, 2025 [3] - The report notes significant individual stock performances, with top gainers including Hualian Holdings (up 9.95%) and Daming City (up 5.78%), while notable decliners include Zhongtian Services (down 3.06%) and Sanxiang Impression (down 2.05%) [4] - Recent policy adjustments in Beijing aim to optimize housing purchase conditions, including reduced social security or tax payment requirements for non-local families and support for multi-child families in purchasing additional properties [7][8] Market Performance - On December 24, 2025, the real estate sector index rose by 0.67%, indicating a positive sentiment in the market [3] - Chengdu successfully sold seven residential land parcels for a total of 1.494 billion yuan, while Zhengzhou sold three residential plots for a total of 1.025 billion yuan, reflecting active land transaction activities [5][6] Policy Changes - The Beijing government has implemented new housing purchase policies effective December 24, 2025, which include easing restrictions for non-local families and enhancing support for families with multiple children [7][8]

Quantitative Models and Construction Methods 1. Model Name: Industry Crowding Monitoring Model - **Model Construction Idea**: This model is designed to monitor the crowding levels of industries on a daily basis, focusing on the Shenwan First-Level Industry Index. It identifies industries with high or low crowding levels and tracks changes in crowding over time[3]. - **Model Construction Process**: The model calculates the crowding level of each industry based on specific metrics (not detailed in the report). It then ranks industries by their crowding levels and highlights those with significant changes in crowding. For example, the report notes that the military and retail industries had high crowding levels, while the computer industry had relatively low levels. Additionally, it tracks main fund flows into and out of industries over recent trading days[3]. - **Model Evaluation**: The model provides actionable insights into industry crowding trends, helping investors identify potential opportunities or risks in specific sectors[3]. 2. Model Name: Premium Rate Z-Score Model - **Model Construction Idea**: This model is used to screen ETF products for potential arbitrage opportunities by calculating the Z-score of their premium rates. It also serves as a warning signal for potential price corrections in ETFs[4]. - **Model Construction Process**: The model involves rolling calculations of the Z-score for the premium rates of various ETFs. The Z-score is calculated as: $ Z = \frac{(P - \mu)}{\sigma} $ where $ P $ is the current premium rate, $ \mu $ is the mean premium rate over a rolling window, and $ \sigma $ is the standard deviation of the premium rate over the same window. ETFs with extreme Z-scores are flagged as potential arbitrage opportunities or correction risks[4]. - **Model Evaluation**: The model is effective in identifying ETFs with significant deviations from their historical premium rates, providing opportunities for arbitrage or risk management[4]. Model Backtesting Results 1. Industry Crowding Monitoring Model - No specific numerical backtesting results are provided for this model in the report[3]. 2. Premium Rate Z-Score Model - No specific numerical backtesting results are provided for this model in the report[4]. Quantitative Factors and Construction Methods No specific quantitative factors are detailed in the report. Factor Backtesting Results No specific backtesting results for factors are detailed in the report.

2025 年 12 月 22 日 行业点评报告 中性/维持 11 月销售延续承压，开发投资走弱 2025 年 11 月统计局房地产数据点评 走势比较 (30%) (18%) (6%) 6% 18% 30% 24/11/18 25/1/29 25/4/11 25/6/22 25/9/2 25/11/13 子行业评级 | 和运营 | | | --- | --- | | 房 地 产 开 发 房地产服务 | 无评级 无评级 | 相关研究报告 <<太平洋房地产日报（20251114）：常 州新北区两宗宅地成交>>--2025- 11-16 <<太平洋房地产日报（20251113）：宁 波慈溪一宗涉宅地块出让>>--2025- 11-14 <<城投控股 2025 年三季报点评：利 润实现扭亏为赢，待结算资源丰 富>>--2025-11-13 房地产 房地产 证券分析师：徐超 电话：18311057693 E-MAIL：xuchao@tpyzq.com 分析师登记编号：S1190521050001 证券分析师：戴梓涵 电话：18217681683 E-MAIL：daizh@tpyzq.com 分析师登记编号：S119052 ...

- The report introduces an **industry crowding monitoring model** to track the crowding levels of Shenwan primary industry indices on a daily basis. The model identifies industries with high crowding levels (e.g., agriculture, military, retail) and low crowding levels (e.g., computers, home appliances, petrochemicals, pharmaceuticals) based on the previous trading day's data. It also highlights significant changes in crowding levels for industries like non-bank financials, real estate, and social services[3] - A **Z-score premium model** is constructed to screen ETF products for potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations in premium rates, which may indicate arbitrage potential or risks of price corrections[4] - The report provides detailed data on **ETF fund flows**, categorizing them into broad-based ETFs, industry-themed ETFs, style-strategy ETFs, and cross-border ETFs. For example, broad-based ETFs saw a net inflow of 163.36 billion yuan in a single day, with the top inflows being the CSI A500 ETF (+32.82 billion yuan), CSI A500 ETF (Southern) (+26.32 billion yuan), and CSI 300 ETF (+20.51 billion yuan)[5][6]

- The report introduces an **industry crowding monitoring model** to track the crowding levels of Shenwan primary industry indices on a daily basis. The model identifies industries with high crowding levels (e.g., agriculture, military, building materials) and low crowding levels (e.g., computers, home appliances, media) based on the previous trading day's data. It also highlights significant changes in crowding levels for industries such as automobiles, petrochemicals, and social services[3] - A **Z-score premium model** is constructed to screen ETF products for potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations in premium rates, which may indicate arbitrage opportunities or potential risks of price corrections[4] - The report provides detailed data on **ETF fund flows**, categorizing them into broad-based ETFs, industry-themed ETFs, style-strategy ETFs, and cross-border ETFs. For example, broad-based ETFs saw a net inflow of 60.55 billion yuan in a single day, with the top inflows being the CSI A500 ETF (+10.42 billion yuan), CSI A500 ETF South (+10.23 billion yuan), and STAR 50 ETF (+8.62 billion yuan)[5] - The report highlights **industry crowding levels** over the past 30 trading days, presenting a heatmap that shows the relative crowding levels of various industries. For instance, industries like public utilities, agriculture, and military defense exhibit high crowding levels, while industries like computers and media show relatively low levels[9] - The report identifies **key ETF trading signals**, recommending attention to specific ETFs such as the CSI 1000 Enhanced ETF, Chuangzhongpan 88 ETF, and Medical Device ETF based on their potential for investment opportunities[11]

- The report constructs an industry congestion monitoring model to monitor the congestion levels of Shenwan first-level industry indices on a daily basis[3] - The ETF product screening signal model is built based on the premium rate Z-score model, which provides potential arbitrage opportunities through rolling calculations[4] - The industry congestion monitoring model indicates that the congestion levels of communication, military, and building materials were high on the previous trading day, while the congestion levels of computers and automobiles were relatively low[3] - The ETF product screening signal model suggests caution regarding the potential pullback risk of the identified targets[4]