Economic Indicators - The Huachuang Macro WEI index rose to 6.05% as of January 4, 2026, an increase of 0.46 percentage points from the previous week[2] - Subway passenger volume in 26 cities increased by 6% year-on-year in the first week of January, while domestic flight operations averaged 12,400 flights per day, down 0.6% year-on-year[2] - Container throughput at Chinese ports rebounded slightly, with a week-on-week increase of 6.3% as of January 5, 2026, and a year-on-year increase of 7.7% over the past four weeks[2] Real Estate and Construction - Residential property sales in 67 cities saw a year-on-year decline of 43% in the first ten days of January, compared to a 24% decline in December 2025[3] - The average land premium rate in 100 cities fell to 0.45% as of January 4, 2026, down from 1.64% in December 2025[3] - Cement shipment rates dropped to 29% as of January 2, 2026, a decrease of 2.4 percentage points from the previous week[3] Commodity Prices - Gold prices rose to $4,473 per ounce, an increase of 3.6%, while copper prices reached $12,990 per ton, up 3.8%[2] - Crude oil prices increased, with WTI at $59.1 per barrel (up 3.1%) and Brent at $63.3 per barrel (up 4.3%)[2] - Lithium carbonate prices surged by 15.6% in the latest trading session[2] Financial Markets - The stock-bond Sharpe ratio difference stood at 4.25, indicating a high relative value for equities compared to bonds[9] - New special bond issuance in early January totaled 874 billion yuan, significantly higher than zero in the same period last year[4] - Interest rates fell post-year-end, with DR001 at 1.2727% and DR007 at 1.4727%, reflecting decreases of 5.98 and 50.94 basis points respectively since December 31, 2025[4]

Economic Data Review - In December, the ADP employment number in the U.S. was below expectations, with a growth of 41,000 jobs compared to an expected 50,000[9] - The October trade deficit narrowed to $29.4 billion, the lowest since mid-2009, with a previous deficit of $48.1 billion revised from $52.8 billion[9] - The ISM Services PMI rose significantly to 54.4 in December, exceeding the expected 52.2, while the ISM Manufacturing PMI fell to 47.9, indicating continued contraction[9] Employment Trends - The initial jobless claims rose to 208,000 in the week of January 3, up from 200,000 the previous week[24] - The continuing jobless claims increased to 1.914 million, compared to 1.858 million the prior week[24] - The number of job vacancies increased, with the INDEED job vacancy index averaging 104.8 in December, up from 103.1 in November[28] Consumer and Retail Activity - The Redbook retail sales year-on-year growth rate fell to 7.1% for the week of January 3, down from 7.6% the previous week[16] - The 30-year mortgage rate in the U.S. rose to 6.16% as of January 8, compared to 6.15% the previous week[19] - The MBA market composite index, reflecting mortgage applications, increased to 270.8, a 0.3% rise from the previous week[19] Financial Conditions - The Bloomberg Financial Conditions Index for the U.S. was 0.863 on January 9, up from 0.795 the previous week, indicating a loosening of financial conditions[35] - The offshore dollar liquidity showed improvement for the yen against the dollar, while the euro against the dollar deteriorated[39] - The 10-year U.S.-Eurozone government bond yield spread widened to 126.8 basis points, compared to 121.5 basis points the previous week[42]

宏观研究 证 券 研 究 报 告 【宏观快评】2025 年 12 月非农数据点评 10W！或是美国降息的就业分水岭 主要观点 ❖ 2025 年 12 月份非农数据简述 1、新增非农低于预期，前两个月数据明显下修。新增非农就业 5 万，预期 7 万。私人部门新增非农就业 3.7 万，预期 7.5 万。10-11 月新增就业合计下修 7.6 万。就业增长集中在教育保健服务（+4.1 万，前值+5.9 万）、休闲酒店（+4.7 万，前值-0.3 万）。零售、建筑、制造业、专业和商业服务等行业就业萎缩。 2、失业率意外回落，录得 4.4%（4.375%），预期 4.5%，前值 4.5%（4.536%）。 劳动参与率从 62.46%降至 62.40%，预期 62.4%。失业率下行主要源于就业增 长和供给小幅收缩，前者影响约 0.14 个百分点，后者影响约 0.03 个百分点。 3、时薪增速符合预期，但周工时下滑。私人行业时薪环比 0.3%，预期 0.3%， 同比增速 3.8%，预期 3.6%。周工时从 34.3 小时降至 34.2 小时，仍处于 2015 年以来的低位水平。周薪环比持平，并未增长（时薪是周薪和周工时的倒 ...

Investment Rating - The report maintains a "Buy" recommendation for the transportation industry, particularly focusing on the low-altitude economy and drone sectors [2]. Core Insights - The report emphasizes the growing importance of drones in modern warfare, highlighting their low cost and high efficiency, with global military drone spending expected to grow from $14.9 billion in 2025 to $28.6 billion by 2034, at a CAGR of 7.5% [4][8]. - It suggests a focus on companies involved in drone manufacturing and military applications, such as Aerospace Rainbow and Zhongyun Drone, as well as industrial drone representatives like Zongheng Co. and Green Energy Huichong [4][13]. - The report also discusses the urgent need for anti-drone systems, estimating a global market potential of $63 billion by 2025, with a CAGR of 11-13% from 2025 to 2030 [15][16]. Summary by Sections Industry Overview - The transportation industry includes 121 listed companies with a total market capitalization of ¥34,170.25 billion, representing 2.69% of the overall market [2]. - The absolute performance of the industry over the past 12 months is 9.0%, while relative performance has decreased by 16.9% [2]. Drone Warfare and Anti-Drone Systems - Drones are increasingly recognized as a new force in modern warfare, with significant applications in recent conflicts [5][6]. - The report highlights the successful test flights of new drone models, such as the Rainbow-7 and the "Jiutian" drone, showcasing advancements in China's drone capabilities [11][12]. - The anti-drone market is driven by both military and civilian needs, with a focus on protecting critical infrastructure [15][16]. Aviation Industry and Engine Manufacturing - The report identifies the aviation engine sector as a critical area for investment, noting that China is still in the early stages of developing its civil aviation engines, with a market penetration of less than 1% [19][20]. - It recommends focusing on the commercial aviation engine supply chain, including key players like Aero Engine Corporation of China and its suppliers [21][24]. Market Performance - The Huachuang Transportation Low Altitude 60 Index increased by 7.6% over the past week and year, outperforming major indices like the CSI 300 [28][30]. - Notable stock performances include Aerospace Electronics, which saw a 35% increase, and Haige Communication, which rose by 27% [33][34]. Investment Recommendations - The report suggests a multi-faceted approach to investment, focusing on various segments of the low-altitude economy, including manufacturers, supply chains, and digital infrastructure [38][39]. - Key companies to watch include Wan Feng Ao Wei, Xie Rui, and Zhongyun Drone, among others, as they are positioned to benefit from the growth in low-altitude applications and infrastructure [39][46].

Investment Rating - The report maintains a "Recommended" rating for the mechanical industry, with a focus on AI equipment and consumables, as well as engineering machinery [1]. Core Insights - The mechanical industry is expected to benefit from the acceleration of AI applications, particularly in high-performance servers and GPU demand, driven by the rapid iteration of AI models and smart hardware [7]. - The excavator market is projected to exceed expectations in both domestic and international sales, with a forecasted 17% year-on-year growth in 2025, supported by government policies and infrastructure projects [7]. - The report emphasizes the potential for a new recovery cycle in the equipment industry, driven by monetary and fiscal policy support, and suggests focusing on key companies across various segments [7]. Summary by Sections Key Company Earnings Forecast, Valuation, and Investment Ratings - Companies such as 汇川技术 (Inovance Technology), 法兰泰克 (Falan Tech), and 信捷电气 (Xinjie Electric) are rated as "Strong Buy" with projected EPS growth and favorable PE ratios [2][8]. - For example, 汇川技术 is expected to have an EPS of 2.11元 in 2025, with a PE ratio of 37.13, indicating strong growth potential [2]. Industry and Company Investment Views - The report highlights the AI equipment and consumables sector as a key area for investment, with significant growth expected in the PCB market driven by AI infrastructure needs [9]. - The engineering machinery sector is also highlighted, with companies like 三一重工 (Sany Heavy Industry) and 徐工机械 (XCMG) expected to benefit from increased domestic demand and international market recovery [7][9]. Key Data Tracking - The report provides macroeconomic data indicating a total market capitalization of 70,956.73 billion yuan for the mechanical industry, with 636 listed companies [4]. - The mechanical sector has shown strong performance, with a 5.7% increase in the sector index over the past week, outperforming major indices [11][14].

Investment Rating - The report maintains a "Buy" rating for the non-ferrous metals sector, highlighting the initiation of a spring rally with significant price increases in metals [2]. Core Views - The spring rally is believed to have started, with aluminum prices showing strong elasticity. As of January 9, the SHFE aluminum closing price was 24,385 CNY/ton, a 6.4% increase from December 31, 2025. The report anticipates that aluminum prices may rise further due to rigid supply constraints and increasing demand in new sectors [3][4]. - The report emphasizes the positive outlook for the electrolytic aluminum sector, predicting average profits to exceed 7,500 CNY/ton, supported by improved cash flow and stable profitability among companies [4]. - A strike at the Mantoverde copper mine in Chile could impact copper production, potentially exacerbating supply tightness in 2026 [5]. Summary by Sections Industrial Metals - **Aluminum Market**: The report notes a significant increase in aluminum prices and a rise in profits, driven by supply constraints and new demand areas. The global aluminum inventory remains low, providing strong support for prices [3]. - **Copper Market**: The report highlights a rise in copper inventories and recommends several companies in the copper sector, including Zijin Mining and Western Mining [6]. New Energy Metals and Minor Metals - **Cobalt Market**: The report indicates that cobalt exports from the Democratic Republic of Congo are delayed, leading to a potential price increase. The average price of electrolytic cobalt rose to 460,000 CNY/ton, a 1.1% increase from December 31, 2025 [7][12]. - **Company Performance**: Huayou Cobalt's 2025 earnings forecast exceeds market expectations, with a projected net profit increase of 40.8% to 55.2% year-on-year [14]. Industry Data - **Market Performance**: The non-ferrous metals sector has shown strong absolute and relative performance over the past year, with a 110.2% increase over 12 months [9]. - **Stock Market Data**: The total market capitalization of the sector is approximately 457.86 billion CNY, with 126 listed companies [8].

- The report introduces a "Total Heat Index" indicator, defined as the sum of browsing, self-selection, and click counts for individual stocks, normalized by their market share on the same day, and multiplied by 10,000. The indicator range is [0,10000][7] - A rotation strategy is constructed based on the weekly heat index change rate (MA2). At the end of each week, the strategy buys the broad-based index with the highest MA2 change rate. If the "Others" group has the highest change rate, the strategy remains in cash. The annualized return since 2017 is 8.74%, with a maximum drawdown of 23.5%. In 2026, the strategy achieved a return of 7.9%[13][16] - A concept-based strategy is developed by selecting the top 5 concepts with the highest weekly heat index change rate. Stocks within these concepts are filtered by excluding the bottom 20% in terms of market capitalization. Two portfolios are constructed: the "TOP" portfolio holds the top 10 stocks with the highest heat index within each concept, while the "BOTTOM" portfolio holds the bottom 10 stocks. The "BOTTOM" portfolio historically achieved an annualized return of 15.71% with a maximum drawdown of 28.89%. In 2026, the "BOTTOM" portfolio returned 6.4%[32][34]

Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends[1][13] - **Construction Process**: The model analyzes the trading volume of various broad-based indices to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in capturing short-term market movements[1][13] 2. Model Name: Feature Dragon Tiger List Institutional Model - **Construction Idea**: This model uses institutional trading data from the Dragon Tiger List to predict market trends[1][13] - **Construction Process**: The model analyzes the trading activities of institutions listed on the Dragon Tiger List to generate buy or sell signals[1][13] - **Evaluation**: The model is useful for understanding institutional trading behavior and its impact on the market[1][13] 3. Model Name: Feature Volume Model - **Construction Idea**: This model uses specific volume characteristics to predict market trends[1][13] - **Construction Process**: The model analyzes specific volume patterns to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in identifying significant volume changes that precede market movements[1][13] 4. Model Name: Intelligent Algorithm CSI 300 Model - **Construction Idea**: This model uses intelligent algorithms to predict the CSI 300 index trends[1][13] - **Construction Process**: The model employs machine learning algorithms to analyze historical data and generate buy or sell signals for the CSI 300 index[1][13] - **Evaluation**: The model leverages advanced algorithms to improve prediction accuracy[1][13] 5. Model Name: Intelligent Algorithm CSI 500 Model - **Construction Idea**: This model uses intelligent algorithms to predict the CSI 500 index trends[1][13] - **Construction Process**: The model employs machine learning algorithms to analyze historical data and generate buy or sell signals for the CSI 500 index[1][13] - **Evaluation**: The model leverages advanced algorithms to improve prediction accuracy[1][13] 6. Model Name: Limit Up and Down Model - **Construction Idea**: This model uses the occurrence of limit up and down events to predict market trends[1][13] - **Construction Process**: The model analyzes the frequency and context of limit up and down events to generate buy or sell signals[1][13] - **Evaluation**: The model is effective in capturing extreme market movements[1][13] 7. Model Name: Up and Down Return Difference Model - **Construction Idea**: This model uses the difference between upward and downward returns to predict market trends[1][13] - **Construction Process**: The model calculates the difference between upward and downward returns to generate buy or sell signals[1][13] - **Evaluation**: The model provides insights into market momentum and potential reversals[1][13] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model uses calendar-based patterns to predict market trends[1][13] - **Construction Process**: The model analyzes historical data to identify recurring calendar-based patterns and generate buy or sell signals[1][13] - **Evaluation**: The model is useful for identifying seasonal trends in the market[1][13] 9. Model Name: Long-term Momentum Model - **Construction Idea**: This model uses long-term momentum to predict market trends[1][14] - **Construction Process**: The model analyzes long-term price momentum to generate buy or sell signals[1][14] - **Evaluation**: The model is effective in capturing long-term market trends[1][14] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Construction Idea**: This model combines multiple factors to predict market trends[1][15] - **Construction Process**: The model integrates various indicators and models to generate a comprehensive buy or sell signal[1][15] - **Evaluation**: The model provides a holistic view of the market by combining multiple factors[1][15] 11. Model Name: A-Share Comprehensive Guozheng 2000 Model - **Construction Idea**: This model combines multiple factors to predict the Guozheng 2000 index trends[1][15] - **Construction Process**: The model integrates various indicators and models to generate a comprehensive buy or sell signal for the Guozheng 2000 index[1][15] - **Evaluation**: The model provides a holistic view of the market by combining multiple factors[1][15] 12. Model Name: Turnover Rate Inverse Volatility Model - **Construction Idea**: This model uses the inverse relationship between turnover rate and volatility to predict market trends[1][16] - **Construction Process**: The model analyzes the turnover rate and its inverse relationship with volatility to generate buy or sell signals[1][16] - **Evaluation**: The model is effective in identifying periods of high market uncertainty[1][16] Model Backtesting Results 1. Volume Model - **Indicator Value**: All broad-based indices are bullish[1][13] 2. Feature Dragon Tiger List Institutional Model - **Indicator Value**: Bullish[1][13] 3. Feature Volume Model - **Indicator Value**: Bullish[1][13] 4. Intelligent Algorithm CSI 300 Model - **Indicator Value**: Bullish[1][13] 5. Intelligent Algorithm CSI 500 Model - **Indicator Value**: Bullish[1][13] 6. Limit Up and Down Model - **Indicator Value**: Bullish[1][13] 7. Up and Down Return Difference Model - **Indicator Value**: All broad-based indices are bullish[1][13] 8. Calendar Effect Model - **Indicator Value**: Neutral[1][13] 9. Long-term Momentum Model - **Indicator Value**: Some broad-based indices are bullish[1][14] 10. A-Share Comprehensive Weapon V3 Model - **Indicator Value**: Bullish[1][15] 11. A-Share Comprehensive Guozheng 2000 Model - **Indicator Value**: Bullish[1][15] 12. Turnover Rate Inverse Volatility Model - **Indicator Value**: Bearish[1][16]

Group 1: Inflation Overview - In December 2025, CPI increased year-on-year from 0.7% to 0.8%, while core CPI remained stable at 1.2%[2] - PPI narrowed its year-on-year decline from -2.2% to -1.9%[2] - The GDP deflator index for Q4 2025 is expected to be around -0.4%, with earlier quarters at -0.8%, -1.2%, and -1%[2] Group 2: CPI Analysis - The cumulative CPI increase for 2025 is 0.8%, a significant recovery compared to the average -0.1% in 2023-24[5] - Food prices rose by 1.1% in 2025, driven by increases in fruits, vegetables, and beef[5] - Gold jewelry prices surged by 68.5%, contributing to the overall CPI improvement[5] Group 3: PPI Trends - In the first half of 2025, PPI experienced a monthly average decline of -0.3%, compared to -0.2% in 2023 and 2024[6] - The second half of 2025 saw PPI stabilize with a monthly average of 0%, indicating a recovery in various industry chains[6] - Factors influencing PPI include global recession fears due to U.S. tariff policies and ongoing adjustments in the domestic real estate market[6] Group 4: 2026 Outlook - CPI is projected to rise by approximately 0.8% in 2026, with a technical adjustment of 0.1 percentage points due to base effects[10] - PPI is expected to decline by about -1%, with an upward adjustment of 0.4 percentage points due to price increases in the non-ferrous sector[10] - Potential upward risks for CPI include increased consumer subsidies and improved service supply in the economy[10]

Summary of Key Points Core Viewpoints - The fourth quarter of 2025 saw mixed performance across different indices, with the Growth Index rising by 5.03% and the Shanghai Composite Index increasing by 2.22% [1] - Most sectors within the CITIC first-level industries showed positive returns, particularly the Oil & Petrochemical sector, which rose by 16.97%, and the Defense & Military sector, which increased by 16.74% [1][10] - Timing models in the fourth quarter demonstrated the ability to achieve absolute positive returns, with several models performing notably well [1] Sector Performance - The Oil & Petrochemical sector had a closing price of 3,424.25 with a quarterly increase of 16.97% [11] - The Defense & Military sector closed at 11,864.34, reflecting a quarterly rise of 16.74% [11] - Other sectors with significant gains included Nonferrous Metals (15.63%), Communications (14.72%), and Consumer Services (8.45%) [11][10] Fund Performance - Balanced mixed funds outperformed others with an average return of 1.22%, while stock funds showed a decline of 1.71% [14] - A total of 715 new public funds were established in Q4 2025, raising a total of 2,784.53 billion, with mixed funds raising 997.56 billion [14] Investment Themes - The report emphasizes the importance of utilizing historical timing, sector rotation, and stock selection models to identify future investment opportunities [5][6] - The focus for Q1 2026 is on sectors such as Construction Materials, Automotive, and Electronics [3] Timing Strategies - The report outlines various timing models, including short-term models like the Price-Volume Resonance Model and the Low-Volatility Blade Model, which aim to capture market trends and rebounds [15][16] - The Long-term Momentum Swing Model has shown a 7.01% annualized return since June 2008, indicating its effectiveness in long-term market analysis [43][44] Comprehensive Models - The Comprehensive Weapon V3 Model integrates multiple timing strategies and has achieved an annualized return of 29.55% since February 2015 [46] - The Smart Algorithm Timing Model for the CSI 300 Index has demonstrated a remarkable annualized return of 35.42% since January 2014, showcasing its superior performance compared to the index itself [49][50]