Report Industry Investment Rating The document does not provide a specific industry investment rating. Core Viewpoints of the Report - Future investment strategy suggests balanced style allocation, emphasizing technology, non - banking, and consumption sectors. For stock funds, A - share market may have a spring "good start" with policy expectations, liquidity, and fundamentals improving. For bond funds, short - term negative factors are repaired, but mid - term structural optimization is incomplete. Money funds have no trend investment opportunities in the long - term low - interest environment [3][4]. - Last week, the A - share market continued its upward trend and had a good start, with satellite, AI application, and non - ferrous sectors performing well. The bond market declined, the US stock market reached a new high, and oil and gold prices rose due to geopolitical risks. Funds heavily invested in medical, semiconductor, and military sectors performed well [4][6][7]. Summary by Related Catalogs 1. Last Week's Market Review - **A - share Market**: Continued the upward trend and had a good start during 20260105 - 20260111. Satellite, AI application, and non - ferrous sectors were strong. The satellite sector's popularity and IPO benefits drove the military sector; AI company listings on the Hong Kong Stock Exchange boosted the AI application sector; the US military action in Venezuela affected non - ferrous metal supply and pushed up the sector. The Shanghai Composite Index rose 3.82% to 4120.43, and the Shenzhen Component Index rose 4.40% to 14120.15. The trading volume was 14.13 trillion yuan, with a daily average increase of about 1.56 trillion yuan compared to the previous week. Among industries, defense, media, non - ferrous, computer, and medical sectors led the increase [4][6][7]. - **Bond Market**: Declined as the strong A - share market suppressed it. The 1 - year Treasury yield dropped 5BP to 1.29%, and the 10 - year Treasury yield rose 3BP to 1.88%. Credit spreads narrowed. The ChinaBond Aggregate Net Price Index fell 0.24%, while the CSI Convertible Bond Index rose 4.45% [4][8]. - **Overseas Market**: The US stock market reached a new high, with the Dow Jones Industrial Average rising 2.32%, the S&P 500 rising 1.57%, and the Nasdaq rising 1.88%. European and most Asian markets also rose, except for the Hang Seng Index which fell 0.41%. The US dollar index rose 0.69%. Geopolitical risks from the US military action in Venezuela increased oil and gold prices [4][9]. 2. Last Week's Fund Market Review - **Stock Funds**: Rose 4.92%. Some funds heavily invested in medical, semiconductor, and military sectors performed well. Index funds related to satellite, semiconductor, and media themes did well [4][10][11]. - **Bond Funds**: Rose 0.29%. Partial - debt funds and convertible bond funds with semiconductor and computer in their equity allocation performed well. Among pure - debt funds, those mainly investing in high - grade credit bonds and medium - short - term bonds did better [4][10][11]. - **QDII Funds**: Equity QDII funds rose 2.62%, with funds mainly investing in medicine and semiconductor themes performing well. QDII bond funds rose 0.10% [4][10][12]. - **Money Funds**: Had an annualized yield of 1.58%. Different types of摊余成本法债 funds had different yields [11]. - **Gold ETF and Linked Funds**: Rose 2.85%. Commodity funds rose 2.64% [13]. 3. Future Investment Strategy - **Stock Market**: Policy expectations, liquidity, and fundamentals are expected to improve, and the A - share market may have a spring "good start". Industries with good prospects are technology, non - banking, and consumption. It is recommended to have a balanced style allocation and focus on these sectors [4][14][15]. - **Bond Market**: Short - term negative factors are repaired, but mid - term structural optimization is incomplete. It is recommended to focus on interest - rate bonds with flexible durations and products that mainly invest in high - grade and highly liquid credit bonds [4][15]. - **Money Market**: There are no trend investment opportunities in the long - term low - interest environment [4][15]. - **Commodity Market**: It is advisable to appropriately allocate gold ETFs for long - term and hedging investments [15]. 4. Latest Fund Market Developments - **QDII Quota**: Under the background of promoting inclusive finance, QDII quotas should be more used in public - offering products. Fund companies need to adjust the proportion of QDII quotas used in public - offering and private - placement products, reducing the private - placement quota ratio to within 20% by the end of 2027 and completing at least half of the adjustment by the end of 2026 [17]. - **Fund Sales Fee Regulations**: The official version of the regulations relaxes the redemption fee constraints for bond funds and fine - tunes the subscription and purchase fees. Bond ETFs may become important tools for liquidity management and trading by wealth management institutions. Wealth management funds may gradually increase their allocation to equity funds, with broad - based index funds and low - volatility "fixed - income +" products being more popular [18]. - **Newly Issued Funds**: 11 new funds were established last week, including 3 low - position ordinary FOF funds, 2 strong - equity hybrid funds, 2 stock ETFs, etc. The average subscription days were about 12 days, and the average raised share was 7.45 billion, with a total of 81.91 billion shares [19]. - **Upcoming Fund Dividends**: 99 funds will conduct equity registration in the coming week. The most notable is the Chang Sheng Aerospace and Marine Equipment A, with a dividend of 2.764 yuan per 10 shares [20].

Group 1 - The report emphasizes that high-quality projects can be acquired at low prices, while projects with higher operational risks should be considered after the disclosure of operational data [3][7]. - The REITs market has entered a favorable development phase characterized by both supply and demand growth, with a focus on quality operational entities to share in market development benefits [3][7]. - As of January 10, preliminary operational data for the second half of the year shows that the industrial park and warehousing sectors are stabilizing, with no significant decline in occupancy rates under the price-for-volume policy [7]. Group 2 - The report highlights that the China REITs total return index increased by 1.89% to 1028.93 during the week from December 31, 2025, to January 9, 2026, with significant gains in new infrastructure and consumer REITs [5][6]. - The performance of various sectors during the past week showed new infrastructure leading with a 4.41% increase, followed by consumer REITs at 3.17%, and industrial parks at 3.16% [5][6]. - The report notes that the REITs market experienced a positive start to the year, contrasting with the poor performance of the bond market, indicating a recovery in previously depressed sectors [6][7].

Investment Rating - The report rates the mechanical industry as "Overweight" [5] Core Insights - The mechanical equipment index increased by 5.98% during the week of January 5 to January 9, 2026, outperforming the CSI 300 index, which rose by 2.79% [8] - China has submitted applications for 203,000 new satellites covering 14 satellite constellations, indicating a significant expansion in the commercial space sector [5] - The CES 2026 showcased advancements in humanoid robots, with companies like Upward and Boston Dynamics unveiling new models, highlighting the industry's shift towards diversification and automation [5] Summary by Sections Weekly Market Summary - The mechanical equipment sector's performance was ranked 10th among 31 first-level industries, with a weekly increase of 5.98% [8] - The mechanical industry index has risen by 53.09% since the beginning of 2025, compared to a 24.57% increase in the CSI 300 index [10] Key Macro Data - The manufacturing PMI for December 2025 was reported at 50.1%, indicating stable growth in the sector [15] - The production index and order index for December 2025 were 50.8% and 51.7%, respectively, suggesting positive trends in manufacturing activity [21] Sub-industry Data Summary Engineering Machinery Industry - Excavator sales in December 2025 reached 23,095 units, a year-on-year increase of 19.2% [36] Machine Tool and Industrial Robot Industry - Industrial robot production in November 2025 was 70,188 units, reflecting a year-on-year growth of 20.6% [41] Rail Transit Industry - The cumulative production of EMUs from January to November 2025 was 1,722 units, with November production showing a year-on-year increase of 24.1% [45] Oilfield Equipment Industry - The global active drilling rig count was 1,813 units as of November 2025, with Brent crude oil averaging $63.34 per barrel on January 9, 2026 [53] Semiconductor Equipment Industry - Semiconductor sales in November 2025 reached $75.28 billion, with a month-on-month increase of 3.53% [76] Key Company Earnings Forecast - The report recommends several companies for investment, including: - Humanoid Robots: Hengli Hydraulic, Changying Precision, and others [5] - Chip Equipment: Keri Technology [5] - Commercial Aerospace: Plit [5] - AI Infrastructure: Ice Wheel Environment, Hanzhong Precision, and others [5] - Engineering Machinery: Sany Heavy Industry, XCMG, and others [5] - Export Chain: Honghua Digital Science, Juxing Technology, and others [5]

- The "Buy Intention Factor" showed strong performance at the beginning of the year, with intraday high-frequency skewness factor, intraday downside volatility proportion factor, post-opening buy intention proportion factor, post-opening buy intention strength factor, post-opening large order net buy proportion factor, post-opening large order net buy strength factor, intraday return factor, end-of-day trading proportion factor, average single outflow amount proportion factor, and large order push-up factor all being evaluated[5][6][9] - The "Multi-Granularity Factor" showed average performance, with GRU(10,2)+NN(10) factor, GRU(50,2)+NN(10) factor, multi-granularity model (5-day label) factor, and multi-granularity model (10-day label) factor being evaluated[5][6][9] - The "AI Enhanced Portfolio" had a poor start to the year, with significant drawdowns observed in the weekly rebalanced CSI 500 AI enhanced wide constraint portfolio, CSI 500 AI enhanced strict constraint portfolio, CSI 1000 AI enhanced wide constraint portfolio, and CSI 1000 AI enhanced strict constraint portfolio[5][6][9] Quantitative Factors and Construction Methods 1. **Factor Name: Intraday High-Frequency Skewness Factor** - **Construction Idea**: Measures the skewness of intraday returns to capture the asymmetry in return distribution[5][6] - **Construction Process**: Calculated using high-frequency data to determine the skewness of returns within a trading day[5][6] - **Evaluation**: Demonstrated strong performance at the beginning of the year[5][6] 2. **Factor Name: Intraday Downside Volatility Proportion Factor** - **Construction Idea**: Measures the proportion of downside volatility in intraday returns[5][6] - **Construction Process**: Calculated using high-frequency data to determine the proportion of downside volatility within a trading day[5][6] - **Evaluation**: Showed moderate performance[5][6] 3. **Factor Name: Post-Opening Buy Intention Proportion Factor** - **Construction Idea**: Measures the proportion of buy intentions after market opening[5][6] - **Construction Process**: Calculated using high-frequency data to determine the proportion of buy intentions after the market opens[5][6] - **Evaluation**: Demonstrated strong performance at the beginning of the year[5][6] 4. **Factor Name: Post-Opening Buy Intention Strength Factor** - **Construction Idea**: Measures the strength of buy intentions after market opening[5][6] - **Construction Process**: Calculated using high-frequency data to determine the strength of buy intentions after the market opens[5][6] - **Evaluation**: Showed moderate performance[5][6] 5. **Factor Name: Post-Opening Large Order Net Buy Proportion Factor** - **Construction Idea**: Measures the proportion of net buy orders of large size after market opening[5][6] - **Construction Process**: Calculated using high-frequency data to determine the proportion of net buy orders of large size after the market opens[5][6] - **Evaluation**: Demonstrated weak performance[5][6] 6. **Factor Name: Post-Opening Large Order Net Buy Strength Factor** - **Construction Idea**: Measures the strength of net buy orders of large size after market opening[5][6] - **Construction Process**: Calculated using high-frequency data to determine the strength of net buy orders of large size after the market opens[5][6] - **Evaluation**: Showed weak performance[5][6] 7. **Factor Name: Intraday Return Factor** - **Construction Idea**: Measures the return within a trading day[5][6] - **Construction Process**: Calculated using high-frequency data to determine the return within a trading day[5][6] - **Evaluation**: Demonstrated strong performance at the beginning of the year[5][6] 8. **Factor Name: End-of-Day Trading Proportion Factor** - **Construction Idea**: Measures the proportion of trading activity at the end of the day[5][6] - **Construction Process**: Calculated using high-frequency data to determine the proportion of trading activity at the end of the day[5][6] - **Evaluation**: Showed strong performance[5][6] 9. **Factor Name: Average Single Outflow Amount Proportion Factor** - **Construction Idea**: Measures the proportion of average single outflow amounts[5][6] - **Construction Process**: Calculated using high-frequency data to determine the proportion of average single outflow amounts[5][6] - **Evaluation**: Demonstrated moderate performance[5][6] 10. **Factor Name: Large Order Push-Up Factor** - **Construction Idea**: Measures the impact of large orders on price increases[5][6] - **Construction Process**: Calculated using high-frequency data to determine the impact of large orders on price increases[5][6] - **Evaluation**: Showed moderate performance[5][6] 11. **Factor Name: GRU(10,2)+NN(10) Factor** - **Construction Idea**: Combines GRU and neural network models to capture complex patterns in data[5][6] - **Construction Process**: Utilizes GRU with 10 units and 2 layers, followed by a neural network with 10 units[5][6] - **Evaluation**: Demonstrated average performance[5][6] 12. **Factor Name: GRU(50,2)+NN(10) Factor** - **Construction Idea**: Combines GRU and neural network models to capture complex patterns in data[5][6] - **Construction Process**: Utilizes GRU with 50 units and 2 layers, followed by a neural network with 10 units[5][6] - **Evaluation**: Showed weak performance[5][6] 13. **Factor Name: Multi-Granularity Model (5-Day Label) Factor** - **Construction Idea**: Uses multi-granularity approach to capture patterns over different time frames[5][6] - **Construction Process**: Trained using a 5-day label to capture short-term patterns[5][6] - **Evaluation**: Demonstrated average performance[5][6] 14. **Factor Name: Multi-Granularity Model (10-Day Label) Factor** - **Construction Idea**: Uses multi-granularity approach to capture patterns over different time frames[5][6] - **Construction Process**: Trained using a 10-day label to capture longer-term patterns[5][6] - **Evaluation**: Showed weak performance[5][6] Factor Backtest Results 1. **Intraday High-Frequency Skewness Factor**: IC -0.007, e^(-rank mae) 0.312, long-short return 0.29%, long-only excess return 0.99%, monthly win rate 1/1[9][10] 2. **Intraday Downside Volatility Proportion Factor**: IC -0.001, e^(-rank mae) 0.313, long-short return 0.22%, long-only excess return 0.95%, monthly win rate 1/1[9][10] 3. **Post-Opening Buy Intention Proportion Factor**: IC 0.032, e^(-rank mae) 0.324, long-short return 1.04%, long-only excess return -0.41%, monthly win rate 0/1[9][10] 4. **Post-Opening Buy Intention Strength Factor**: IC 0.027, e^(-rank mae) 0.323, long-short return 0.65%, long-only excess return 0.62%, monthly win rate 1/1[9][10] 5. **Post-Opening Large Order Net Buy Proportion Factor**: IC -0.006, e^(-rank mae) 0.306, long-short return -0.52%, long-only excess return -0.53%, monthly win rate 0/1[9][10] 6. **Post-Opening Large Order Net Buy Strength Factor**: IC 0.004, e^(-rank mae) 0.308, long-short return -0.07%, long-only excess return -0.66%, monthly win rate 0/1[9][10] 7. **Intraday Return Factor**: IC 0.037, e^(-rank mae) 0.328, long-short return 1.77%, long-only excess return 1.89%, monthly win rate 1/1[9][10] 8. **End-of-Day Trading Proportion Factor**: IC 0.084, e^(-rank mae) 0.334, long-short return 2.67%, long-only excess return 1.35%, monthly win rate 1/1[9][10] 9. **Average Single Outflow Amount Proportion Factor**: IC 0.013, e^(-rank mae) 0.319, long-short return 0.45%, long-only excess return 0.14%, monthly win rate 1/1[9][10] 10. **Large Order Push-Up Factor**: IC -0.007, e^(-rank mae) 0.327, long-short return 0.22%, long-only excess return 0.43%, monthly win rate 1/1[9][10] 11. **GRU(10,2

Group 1 - The report indicates that trading activity has increased, with the CSI 1000 index leading the gains among broad market indices [1][4] - Valuations across indices have risen, with the CSI 1000 showing a significant increase of 7.4 percentage points in PE-TTM historical percentiles and 9.6 percentage points in PB-LF historical percentiles [4][5] - In terms of industry valuations, the home appliance sector leads in PE valuation increases, while the coal sector leads in PB valuation increases, with coal rising by 7.9 percentage points [4][5] Group 2 - The report highlights that sentiment indicators show a rise in trading activity, with turnover rates and transaction volumes increasing across indices, particularly the SSE 50, which saw a turnover rate increase of 159.5% and a transaction volume increase of 69.8% [4][31] - Margin trading balances have also increased, reaching 2.62 trillion yuan as of January 8, 2026, a rise of 3.15% compared to December 31, 2025 [4][32] - The report notes a slight decrease in the equity risk premium (ERP), which stands at 3.96%, down by 0.20 percentage points from December 31, 2025 [4][28]

Investment Rating - The report does not explicitly provide an investment rating for the AI industry Core Insights - The AI industry is experiencing significant advancements, with multiple companies releasing innovative products and models, indicating a robust growth trajectory in the sector [1][5][9][14][18] - The Chinese government is actively promoting AI integration in manufacturing, aiming to establish a leading position in AI technology and applications by 2027 [5] - Major companies like Lenovo and Tencent are launching new AI models and platforms, enhancing the capabilities and applications of AI in various fields [8][15][18] Summary by Sections 1. AI Industry Dynamics - The Ministry of Industry and Information Technology and other departments have issued guidelines for the "Artificial Intelligence + Manufacturing" initiative, targeting the development of core AI technologies and industry applications by 2027 [5] - In 2026, over 30 national standards related to data in AI will be introduced, focusing on areas like intelligent agents and embodied intelligence [6] - The Sichuan provincial government has launched a plan to develop a national digital economy innovation zone, aiming to enhance digital infrastructure and promote the growth of digital industries [7] 2. AI Application Insights - LuKe announced the global launch of the "Air Charging AI Smart Lock V7 Max" at CES 2026, featuring technology for continuous power supply [10] - Yushun released training videos for its humanoid robot Unitree H2, showcasing advanced capabilities [11] - DeepTing released the world's first dual-wheeled outdoor companion robot Rovar, designed for various outdoor scenarios [12] - Rokid introduced the "Style" AI glasses at CES 2026, featuring a dual-chip architecture and 4K video capabilities [13] 3. AI Large Model Insights - Alibaba Tongyi has released and open-sourced the Qwen3-VL-Embedding and Qwen3-VL-Reranker models, designed for multimodal information retrieval and understanding [14] - Tencent launched the HY-Motion1.0 model, a significant advancement in 3D character animation generation [15] - Xiaopeng unveiled its second-generation VLA model, marking a shift towards practical applications of physical AI technology [16][17] - Lenovo introduced the Lenovo Qira personal super intelligent agent, emphasizing cross-device functionality and personalized services [18] 4. Technology Frontiers - Alibaba Cloud launched a new multimodal interaction development kit, integrating various foundational models for diverse applications [19] - Tsinghua University published a paper on an AI-driven drug virtual screening platform, significantly improving screening speed and accuracy [20] - Chinese scientists made progress in cancer immunotherapy, developing a protein-targeting degradation technology [21] - A team from the University of Science and Technology of China achieved advancements in heterogeneous quantum communication networks [22]

Investment Rating - The report assigns an "Accumulate" rating for the industrial gas industry [1]. Core Insights - The average price of liquid argon continues to show a year-on-year increase, while rare gases are experiencing low-level fluctuations in weekly prices. Notable events include the opening of Qidong Jinhong and Nanda Optoelectronics' plan to increase its stake in its Ulanqab subsidiary to enhance its electronic specialty gas business [2][4]. Summary by Sections Price Trends - Liquid argon has an average price of 1191 RMB/ton, reflecting a 1.43% decrease week-on-week but a significant 113.84% increase year-on-year. Other gases show varied trends: - Liquid oxygen: 335 RMB/ton, down 3.18% week-on-week, down 8.7% year-on-year - Liquid nitrogen: 363 RMB/ton, down 1% week-on-week, down 5% year-on-year - Rare gases such as high-purity helium and xenon also show declines in price [4][5]. Production Capacity - The average operating load rate for China's industrial gas sector is reported at 66.73%, which is a decrease of 1.88 percentage points week-on-week [6]. Key Events - The opening ceremony of Qidong Jinhong took place on January 10, 2026, aimed at providing high-quality gas products and solutions for key industries in the region. Additionally, Nanda Optoelectronics plans to invest 77.6 million RMB to acquire an additional 16.17% stake in its Ulanqab subsidiary, increasing its ownership to 91.05% [4][5]. Recommended Stocks - The report recommends stocks such as Hangyang Co., Ltd. and Shaangu Power, with related stocks including Zhengfan Technology, Fostar, and Zhongtai Co., Ltd. [4].

Quantitative Models and Construction Methods - **Model Name**: CSI 300 Enhanced Portfolio **Model Construction Idea**: The model aims to enhance the performance of the CSI 300 Index by leveraging quantitative strategies to generate excess returns over the benchmark index[5][9][15] **Model Construction Process**: The portfolio is constructed by applying quantitative stock selection and weighting methodologies to the CSI 300 Index constituents. The process involves identifying stocks with favorable factor exposures and optimizing the portfolio to maximize risk-adjusted returns while maintaining a low tracking error relative to the benchmark[9][15] **Model Evaluation**: The model demonstrated strong performance with positive excess returns over the benchmark index, indicating effective factor utilization and portfolio construction[15] - **Model Name**: CSI 500 Enhanced Portfolio **Model Construction Idea**: Similar to the CSI 300 Enhanced Portfolio, this model focuses on enhancing the performance of the CSI 500 Index by employing quantitative strategies[5][9][15] **Model Construction Process**: The portfolio is built by selecting stocks from the CSI 500 Index based on quantitative factors and optimizing the portfolio to achieve excess returns while controlling tracking error[9][15] **Model Evaluation**: The model underperformed the benchmark index during the observed period, suggesting potential challenges in factor effectiveness or market conditions[15] - **Model Name**: CSI 1000 Enhanced Portfolio **Model Construction Idea**: This model targets the CSI 1000 Index, aiming to generate excess returns through quantitative enhancements[5][9][15] **Model Construction Process**: The portfolio construction involves selecting stocks from the CSI 1000 Index using quantitative factors and optimizing the portfolio for risk-adjusted returns and low tracking error[9][15] **Model Evaluation**: The model showed a slight underperformance relative to the benchmark index, indicating room for improvement in factor application or portfolio optimization[15] - **Model Name**: GARP Portfolio **Model Construction Idea**: The GARP (Growth at a Reasonable Price) portfolio combines growth and valuation factors to identify stocks with strong growth potential at reasonable valuations[32] **Model Construction Process**: Stocks are selected based on a combination of growth metrics (e.g., earnings growth) and valuation metrics (e.g., price-to-earnings ratio). The portfolio is then optimized to balance growth and valuation exposures[32] **Model Evaluation**: The portfolio achieved positive excess returns over the CSI 300 Index, demonstrating the effectiveness of the GARP strategy in the observed period[32] - **Model Name**: Small-Cap Growth Portfolio **Model Construction Idea**: This portfolio focuses on small-cap stocks with strong growth characteristics, aiming to capture the growth premium in the small-cap segment[37] **Model Construction Process**: Stocks are selected from the small-cap universe based on growth factors such as earnings growth and revenue growth. The portfolio is optimized to maximize growth exposure while managing risk[37] **Model Evaluation**: The portfolio delivered positive excess returns over the micro-cap index, indicating the effectiveness of the growth factor in the small-cap segment[37] Model Backtesting Results - **CSI 300 Enhanced Portfolio**: Weekly return 4.69%, excess return 1.90%, tracking error 4.71%, maximum drawdown 1.68%[9][15][22] - **CSI 500 Enhanced Portfolio**: Weekly return 6.34%, excess return -1.58%, tracking error 4.07%, maximum drawdown 3.11%[9][15][16] - **CSI 1000 Enhanced Portfolio**: Weekly return 6.17%, excess return -0.86%, tracking error 5.31%, maximum drawdown 4.45%[9][15][18] - **GARP Portfolio**: Weekly return 3.62%, excess return 0.84%, tracking error 13.93%, maximum drawdown 4.04%[32][33] - **Small-Cap Growth Portfolio**: Weekly return 4.95%, excess return 0.49%, tracking error 11.60%, maximum drawdown 9.76%[37][40] Quantitative Factors and Construction Methods - **Factor Name**: Market Capitalization (Size) Factor **Factor Construction Idea**: This factor captures the size effect, where smaller companies tend to outperform larger companies over time[42] **Factor Construction Process**: Stocks are ranked by their market capitalization, and the top 10% (large-cap) and bottom 10% (small-cap) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the size factor's performance[41][42] **Factor Evaluation**: The factor showed mixed performance, with large-cap stocks outperforming small-cap stocks in the observed period[42] - **Factor Name**: Price-to-Book Ratio (PB) Factor **Factor Construction Idea**: This factor identifies undervalued stocks based on their price-to-book ratios[42] **Factor Construction Process**: Stocks are ranked by their PB ratios, and the top 10% (high PB) and bottom 10% (low PB) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the PB factor's performance[41][42] **Factor Evaluation**: The factor underperformed during the observed period, with high PB stocks outperforming low PB stocks[42] - **Factor Name**: Expected Net Profit Adjustment Factor **Factor Construction Idea**: This factor captures the impact of expected net profit adjustments on stock performance[53] **Factor Construction Process**: Stocks are ranked by their expected net profit adjustments, and the top 10% (high adjustment) and bottom 10% (low adjustment) are selected to form long and short portfolios, respectively. The difference in returns between these portfolios represents the factor's performance[41][53] **Factor Evaluation**: The factor delivered positive returns, indicating its effectiveness in identifying stocks with favorable profit adjustments[53] Factor Backtesting Results - **Market Capitalization (Size) Factor**: Multi-market excess returns: -0.79% (All Market), 4.83% (CSI 300), -5.59% (CSI 500), -2.47% (CSI 1000)[42][43][48] - **Price-to-Book Ratio (PB) Factor**: Multi-market excess returns: -4.01% (All Market), -5.52% (CSI 300), -6.06% (CSI 500), -5.68% (CSI 1000)[42][43][48] - **Expected Net Profit Adjustment Factor**: Multi-market excess returns: 0.57% (All Market), 0.86% (CSI 300), 1.89% (CSI 500), -0.58% (CSI 1000)[53][54][55]

Investment Rating - The report maintains an "Overweight" rating for the real estate industry [2][5] Core Insights - Recent real estate transactions have declined due to external uncertainties, but future policies are expected to mitigate these negative impacts and stabilize the market [2][5] - The report highlights a significant drop in new home sales across major cities, with a 67.4% decrease week-on-week and a 46.3% decrease year-on-year for the 30 major cities [5][10] - The land transaction volume also showed a decline, with a total land supply area of 13.38 million square meters and a transaction area of 12.19 million square meters, reflecting a supply-to-sales ratio of 1.10 [4][10] Summary by Sections Transaction Data - In the second week of 2026, new home sales in 30 major cities amounted to 1.03 million square meters, down 67.4% from the previous week and down 46.3% year-on-year [5][10] - First-tier cities recorded a sales area of 320,000 square meters, a decrease of 54.9% week-on-week and 50% year-on-year [5][10] - Second-tier cities saw a sales area of 460,000 square meters, down 76.9% week-on-week and 41% year-on-year [5][10] - Third-tier cities had a sales area of 260,000 square meters, down 46.3% week-on-week and 50.6% year-on-year [5][10] Land Transaction Data - The total land supply area for the year to date in the top 100 cities is 2.37 million square meters, down 12% year-on-year, while the cumulative transaction area is 4.694 million square meters, down 23.2% year-on-year [4][10] - The total land transfer revenue reached 36.2 billion yuan, reflecting a year-on-year decrease of 31.9% [4][10] - The land premium rate was recorded at 0.5%, a decrease of 1.69 percentage points from the previous week [4][10] Inventory and Market Dynamics - The available housing inventory in 35 cities was 31.526 million square meters, down 1.07% month-on-month and down 2.03% year-on-year [10][12] - The inventory clearance cycle for these cities increased to 26.17 months, up 5.64% month-on-month and up 23.51% year-on-year [10][12]

Investment Rating - The report does not explicitly provide an investment rating for the AI industry Core Insights - NVIDIA has announced the expansion of its open-source model library, contributing the largest open-source dataset globally, which includes 100 trillion language training tokens and 100TB of vehicle sensor data, aimed at accelerating AI innovation across various sectors [3] - Marvell's acquisition of XConn is expected to enhance its capabilities in high-speed switching chip technology, allowing better market positioning in PCIe and CXL switching [4] - NAVER has built the largest AI computing cluster in South Korea, integrating 4000 NVIDIA B200 AI GPUs, significantly improving AI model training efficiency [5] - DeepMind and Boston Dynamics are collaborating to integrate the Gemini Robotics model into Atlas and Spot robots, focusing on real-world interaction and learning in dynamic environments [6] - OpenAI has launched the ChatGPT Health mode, designed to facilitate secure discussions about health issues while isolating sensitive information from general conversations [12] - NVIDIA has introduced the Rubin platform, featuring six new chips designed for advanced AI computing, aiming to lower costs and accelerate mainstream AI adoption [18] Summary by Sections AI Industry Dynamics - NVIDIA's contribution includes a vast open-source dataset across language, robotics, autonomous driving, and healthcare, marking a significant step in building an open ecosystem for AI [3] - Marvell's acquisition of XConn is set to enhance its technology in PCIe and CXL switching, with the deal expected to close in 2026 [4] - NAVER's new AI computing cluster can train a 72 billion parameter AI model in just 1.5 months, a significant improvement over the previous system [5] AI Application Insights - D.O.N is set to launch the world's first belt-type wearable device, VITAL BELT, which can monitor health metrics through clothing [7] - MSI's MEG X monitor integrates AI gaming features to enhance user experience, including AI-assisted gaming functionalities [9] - LG has showcased the CLOiD AI robot designed for home environments, capable of interacting with users and controlling smart home devices [10] - Amazon has launched a web-based version of its Alexa+ AI assistant, allowing seamless access across devices [11] AI Large Model Insights - OpenAI's ChatGPT Health mode aims to provide a secure platform for health discussions, isolating sensitive information from regular chat logs [12] - Universal Music is collaborating with NVIDIA to leverage AI in music discovery and creation, marking a significant industry partnership [13] - Google has integrated the Gemini AI solution into Gmail, enhancing email management for its 3 billion users [14] Technology Frontiers - NVIDIA's Rubin platform is designed to create advanced AI systems at lower costs, featuring a collaborative design of six new chips [18] - AMD's upcoming MI500 AI accelerator will utilize 2nm technology, promising higher performance compared to previous models [20] - ODINN has developed a compact AI supercomputer that offers high-level computing power without the need for extensive data center infrastructure [21]