Investment Rating - The report maintains an "Outperform" rating for the internet industry [2][3][4] Core Insights - Major domestic companies are competing for AI Agent traffic entry points, with a focus on companies with the most certainty in computing power and large models, such as Alibaba, Baidu, and Tencent [2][4] - The AI sector is rapidly evolving, with significant investments in talent acquisition, computing infrastructure, and marketing by major players during the Spring Festival period [2] - The report highlights the importance of AI in transforming user interactions with software and the value creation potential of large models [2] Market Review - In January, the Hang Seng Technology Index rose by 3.7%, while the Nasdaq Internet Index fell by 4.7% [11] - The valuation of the Hang Seng Technology Index remained stable, with a PE-TTM of 23.84x, positioned at the 37.28 percentile since its inception [17] - The Nasdaq Index also maintained its valuation, with a PE-TTM of 41.39x, at the 66.79 percentile over the past decade [19] AI Developments - Google launched the open-source medical model MedGemma 1.5 and introduced personal intelligence features in Gemini [21][22] - OpenAI released ChatGPT Health and a user age prediction feature to enhance user safety [23] - Alibaba's Qwen3-Max-Thinking model was introduced, showcasing significant improvements in various dimensions [29] Industry Dynamics - The domestic gaming market is projected to grow by 8% in actual sales revenue by 2025, with a high volume of game approvals in January 2026 [39][40] - Financial technology sector shows a 5% year-on-year growth in payment institutions' reserve funds as of December 2025 [41][42] - The short video industry is experiencing regulatory upgrades and breakthroughs in overseas markets [44] - E-commerce platforms are optimizing store rating logic and reducing deposit requirements for various categories [47][50]

证券研究报告 | 2026年02月07日 宏观经济周报 开局遇冷，二月迎暖 2026 年作为"十五五"规划的开局之年，其首季经济表现备受瞩目。从 1 月 数据看，经济呈现显著的结构性分化特征。一方面，战略性新兴产业采购经 理指数（EPMI）环比升幅与 2024 年同期基本持平（考虑到春节时点相近）， 开局势头总体稳健，其中采购量、进口及就业等分项表现较好。另一方面， 制造业 PMI 环比回落，走势明显弱于 2024 年同期，尤其在生产、新订单等 关键需求指标上表现偏弱。这一"新兴产业强于整体制造业"的格局，延续 并强化了经济结构优化的长期趋势。1 月价格端也呈现积极信号，出厂价格 与原材料购进价格表现强劲，显示"反内卷"政策在促进工业品价格企稳回 升方面成效进一步显现。整体来看，1 月经济在结构上延续优化，但总量层 面未能延续去年 12 月的扩张势头，复苏进程出现阶段性波折。 积极信号在 2 月首周开始显现，各领域经济活动出现广泛改善，内需与外贸 短期动能均表现强劲。首先，生产端景气度明显回升，基建、机电及纺织等 领域生产活动普遍走强。其次，消费端受节前备货、出行增加拉动，市内客 流与物流投递量同比显著增长， ...

Market Trends - The probability of A-shares rising before and after the Spring Festival exceeds 70%[1] - The average trading volume decreases by 4.0% in the week before the festival, while it increases by 22.3% in the week after[11] - The Shanghai Composite Index has an 81% probability of rising in the week before the festival, with an average increase of 1.8%[15] Fund Flows - Leverage funds show a significant outflow before the festival, with an average net buy of -66.9 billion CNY, and a shift to inflow after the festival with a net buy of 14.2 billion CNY[18] - ETF funds experience net inflows of 214.3 billion CNY before the festival, but the inflow slows down after, averaging 171.9 billion CNY[18] - Foreign capital inflow remains stable, with a net inflow probability of 60-70% during the festival period[16] Investment Strategy - Holding stocks during the festival is recommended due to a favorable market outlook and historical data supporting this strategy[19] - The current macroeconomic policy remains positive, with expectations of a return to liquidity easing in the U.S. and a bullish sentiment in the domestic market[19] - A balanced allocation strategy is advised, focusing on technology, AI applications, and traditional value sectors like real estate and liquor[20]

Core Conclusions - The probability of A-shares rising before and after the Spring Festival exceeds 70%, with a trend of reduced trading volume before the holiday and increased volume afterward [1][11] - Historical data shows that leveraged funds typically flow out before the holiday and flow back in afterward, while ETF funds show a pattern of inflow before and a slowdown in inflow after, contributing to the observed changes in trading volume and style differentiation in A-shares [2][16] - Current macroeconomic policies are positive, and overseas risks are manageable, suggesting that holding stocks during the holiday may be a better strategy, with a balanced allocation focusing on technology, particularly AI applications, and attention to cyclical sectors and real estate [2][19] Trading Volume and Style Differentiation - A-shares typically experience reduced trading volume before the Spring Festival, averaging a 4.0% decrease, while trading volume increases by an average of 22.3% after the holiday [11][13] - The probability of the Shanghai Composite Index rising in the week before the Spring Festival is 81%, with an average increase of 1.8%, while the probability for the week after is 76%, with an average increase of 1.3% [15] - Growth and value styles perform similarly before and after the holiday, but large-cap stocks outperform small-cap stocks before the holiday, while small-cap stocks significantly outperform large-cap stocks afterward, with a 87.5% probability of small-cap stocks rising in the week after [12][15] Fund Flow Analysis - Leveraged funds show a significant outflow before the Spring Festival, with an average net buy of -66.9 billion yuan, while they exhibit a net inflow of 14.2 billion yuan after the holiday [16][18] - ETF funds experience net inflows averaging 214.3 billion yuan before the holiday, but the net inflow decreases to 171.9 billion yuan after, with a lower probability of net inflows [16][18] - Foreign capital flows show little change before and after the holiday, maintaining a net inflow probability of 60-70% [16][17] Sector Allocation Strategy - The technology sector remains a key focus, driven by the AI wave, with expectations for the market to shift from hardware to application development [20] - There are opportunities in traditional value sectors, such as undervalued real estate and liquor stocks, as the spring market rally progresses [20][21] - The cyclical sector is also worth monitoring due to emerging demand from AI and new energy industries, alongside improving supply-demand dynamics in resource sectors [20]

Investment Rating - The investment rating for the industry is "Outperform the Market" (maintained) [1] Core Insights - The recent regulatory framework established by the China Securities Regulatory Commission (CSRC) marks the end of the "regulatory vacuum" in the realm of domestic assets issued overseas as asset-backed securities tokens (RWA). This framework is centered around a "filing system" and aims to provide a clear path for compliant institutions to participate in global RWA innovation, benefiting the industry's healthy development in the long term [2][3] - The new guidelines will transition RWA business from "wild growth" to "compliant competition," promoting the exit of non-compliant projects in the short term while laying a sustainable development foundation for the industry in the long term [3][5] - The guidelines emphasize the importance of asset quality and compliance capabilities, shifting market competition from mere concept speculation to a focus on asset selection, structural design, and compliance operations [5][6] Summary by Sections Regulatory Framework - The guidelines clearly define the concept of "domestic assets issued overseas as asset-backed securities tokens," establishing the regulatory scope and including RWA tokenization under securities regulation [4] - A "filing system" is the core mechanism, requiring domestic entities to file with the CSRC before conducting business, ensuring compliance and transparency [4] - A negative list is established, detailing six scenarios under which related businesses cannot operate, thus providing clear boundaries for market participants [4] Impact on RWA Business - The guidelines are seen as a milestone for the nascent RWA business in China, with short-term market clearing and uncertainty reduction expected [5] - Long-term, successful filings will enhance investor confidence and attract traditional financial institutions and asset management companies, leading to increased capital inflow [5] - The core competitiveness of businesses will shift towards asset quality and compliance capabilities, with institutions that can identify and integrate quality underlying assets standing out [5] Implications for Financial Institutions - Investment banks and securitization service providers will benefit from new core business lines, particularly those with cross-border securitization experience [6] - Financial technology and compliance technology companies will find new market opportunities, especially in providing solutions for information disclosure and compliance monitoring [6] - The main challenges include rising compliance costs and the scarcity of professionals who understand both traditional securitization and blockchain technology [6] Investment Recommendations - The implementation of RWA regulatory rules is expected to catalyze investment opportunities in the non-bank financial sector. The report highlights the increasing value of non-bank financial institutions, particularly recommending Citic Securities and Guotai Junan as beneficiaries of the improved market environment and policy implementation [7]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices, aiming to consistently outperform their respective benchmarks [11][12][14] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization [12] - The report monitors the performance of common stock selection factors across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices, by constructing single-factor Maximized Factor Exposure (MFE) portfolios and tracking their relative excess returns [11][15][42] - The MFE portfolio construction process involves optimizing the portfolio to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, turnover rate, and component stock weight proportion [42][43][44] - The optimization model for MFE portfolios is expressed as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style factor deviation, industry deviation, stock weight deviation, component stock weight proportion, and stock weight limits [42][43] - The report provides detailed performance tracking of single-factor MFE portfolios across different stock selection spaces, highlighting factors such as SP, SPTTM, EP, and others that performed well in specific indices like CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices [15][18][20][22][24][26] - The report also tracks the excess returns of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500, with detailed statistics on maximum, minimum, and median excess returns over different time periods [28][32][35][38][41]

Investment Rating - The investment rating for Alphabet Inc. (GOOGL.O) is "Outperform" [7] Core Insights - The company reported strong revenue growth in Q4 2025, with total revenue reaching $113.8 billion, a year-over-year increase of 18% [2] - Google Cloud revenue grew significantly, reaching $17.7 billion, up 48% year-over-year, with a backlog of $240 billion, reflecting a 158% increase [3][11] - The capital expenditure (CAPEX) for Q4 2025 was $27.9 billion, a 113% increase year-over-year, with expectations for 2026 CAPEX to reach $175-185 billion, representing a 91%-102% increase [4][12] Summary by Sections Financial Overview - In Q4 2025, the company achieved a GAAP gross margin of 60%, with operating profit of $31.3 billion, a 16% increase year-over-year, and a net profit of $34.5 billion, up 30% [2][13] - Service revenue was $95.9 billion, with search ad revenue at $63.1 billion, a 17% increase year-over-year, and YouTube ad revenue at $11.4 billion, up 9% [10][14] Cloud Performance - Google Cloud's operating profit margin (OPM) improved to 30.1%, with nearly 75% of customers utilizing vertically optimized AI solutions [3][11] - The number of new customers at year-end doubled compared to the first quarter, and the number of transactions exceeding $1 billion in 2025 surpassed the total of the previous three years [3][11] Capital Expenditure - The company plans to significantly increase its capital expenditures, with a forecast of $175-185 billion for 2026, nearly doubling from the previous year [4][12] - Approximately 60% of the Q4 2025 CAPEX was allocated to servers, while 40% was directed towards data centers and networking equipment [4][12] Revenue and Profit Forecast - Revenue forecasts for 2026 and 2027 have been raised to $471.8 billion and $543.3 billion, respectively, with net profit estimates adjusted to $145.4 billion and $165.4 billion [4][6]

Investment Rating - The investment rating for Alphabet Inc. (GOOGL.O) is "Outperform" [7] Core Insights - The company reported a strong revenue growth of 18% year-over-year, reaching $113.8 billion in Q4 2025, with a net profit of $34.5 billion, reflecting a 30% increase [2][13] - Google Cloud revenue surged by 48% year-over-year to $17.7 billion, with a backlog of $240 billion, indicating robust demand and growth potential [3][11] - Capital expenditures (CAPEX) for Q4 2025 were $27.9 billion, up 113% year-over-year, with expectations for 2026 CAPEX to reach $175-185 billion, nearly doubling from the previous year [4][12] Revenue Overview - Total revenue for Q4 2025 was $113.8 billion, with a GAAP gross margin of 60% and an operating profit margin of 31.6% [2][13] - Service revenue was $95.9 billion, with search advertising revenue at $63.1 billion, up 17% year-over-year, and YouTube ad revenue at $11.4 billion, up 9% [10][14] Cloud Performance - Google Cloud's operating profit margin improved to 30.1%, with significant growth in new customer acquisition and existing customer spending exceeding initial commitments by over 30% [3][11] - Nearly 75% of Google Cloud customers are utilizing vertically optimized AI solutions [3][11] Capital Expenditure Plans - The company plans to significantly increase its capital expenditures, with a forecast of $175-185 billion for 2026, representing a year-over-year increase of 91%-102% [4][12] Financial Projections - Revenue projections for 2026 and 2027 have been raised to $471.8 billion and $543.3 billion, respectively, with net profit estimates adjusted to $145.4 billion and $165.4 billion [4][6]

- The "Hong Kong Stock Selection Portfolio" model is constructed based on a dual-layer selection process that integrates fundamental and technical analysis. The stock pool is initially built using analyst recommendation events, such as upward earnings revisions, first-time coverage, and unexpected positive research report titles. Stocks with both fundamental support and technical resonance are selected to form the portfolio. The backtesting period spans from January 1, 2010, to December 31, 2025, with an annualized return of 19.08% and an excess return of 18.06% relative to the Hang Seng Index after considering transaction costs in a fully invested state[14][15][19] - The "Stable New High Stock Screening" factor is based on the concept of momentum and trend-following strategies, which are particularly effective in the Hong Kong stock market. The factor emphasizes stocks that have recently reached a 250-day high, with a specific calculation formula: $ 250\text{-day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $ Here, $\text{Close}_{t}$ represents the latest closing price, and $\text{ts\_max(Close, 250)}$ is the maximum closing price over the past 250 trading days. A value of 0 indicates a new high, while positive values indicate the degree of pullback from the high. Stocks are further filtered based on analyst attention, relative strength, price stability, and trend continuation metrics[20][22][23] - The backtesting results for the "Hong Kong Stock Selection Portfolio" model show an annualized return of 19.08%, an excess return of 18.06%, and an information ratio (IR) of 1.19 over the entire sample period. The maximum drawdown was 23.73%, with a tracking error of 14.60% and a return-to-drawdown ratio of 0.76[19] - The "Stable New High Stock Screening" factor identified stocks such as Nine Dragons Paper, with the highest number of stocks coming from the cyclical sector (14 stocks), followed by consumer, manufacturing, financial, technology, and pharmaceutical sectors. Specific stocks include Nine Dragons Paper, China Eastern Airlines, and Pacific Basin Shipping, among others[22][23][28]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model Name: Guosen JinGong Index Enhanced Portfolio - **Model Construction Idea**: The model aims to outperform its respective benchmarks by constructing enhanced portfolios based on multiple factors[11][12]. - **Model Construction Process**: 1. **Return Prediction**: Predicting the returns of stocks within the benchmark index. 2. **Risk Control**: Implementing risk control measures to manage the portfolio's risk exposure. 3. **Portfolio Optimization**: Optimizing the portfolio to maximize returns while adhering to the risk constraints[12]. - **Model Evaluation**: The model is designed to consistently outperform its benchmarks by leveraging multiple factors[11]. Model Backtesting Results - **Guosen JinGong Index Enhanced Portfolio**: - **CSI 300 Index Enhanced Portfolio**: Weekly excess return 0.24%, annual excess return 3.21%[5][14]. - **CSI 500 Index Enhanced Portfolio**: Weekly excess return 0.53%, annual excess return -0.27%[5][14]. - **CSI 1000 Index Enhanced Portfolio**: Weekly excess return 1.63%, annual excess return 3.92%[5][14]. - **CSI A500 Index Enhanced Portfolio**: Weekly excess return 0.40%, annual excess return 3.28%[5][14]. Quantitative Factors and Construction Methods Factor Name: Single-Season SP - **Factor Construction Idea**: This factor measures the ratio of single-quarter operating revenue to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{Single-Season SP} = \frac{\text{Single-Quarter Operating Revenue}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and public fund heavy index selection spaces[1][26]. Factor Name: Single-Season EP - **Factor Construction Idea**: This factor measures the ratio of single-quarter net profit attributable to the parent company to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{Single-Season EP} = \frac{\text{Single-Quarter Net Profit Attributable to Parent}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and CSI A500 index selection spaces[1][24]. Factor Name: SPTTM - **Factor Construction Idea**: This factor measures the ratio of trailing twelve months (TTM) operating revenue to total market value[17]. - **Factor Construction Process**: - Formula: $ \text{SPTTM} = \frac{\text{TTM Operating Revenue}}{\text{Total Market Value}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 300 and public fund heavy index selection spaces[1][26]. Factor Name: One-Month Reversal - **Factor Construction Idea**: This factor measures the price change over the past 20 trading days[17]. - **Factor Construction Process**: - Formula: $ \text{One-Month Reversal} = \text{Price Change over Past 20 Trading Days} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 500 and CSI 1000 index selection spaces[1][20][22]. Factor Name: Three-Month Reversal - **Factor Construction Idea**: This factor measures the price change over the past 60 trading days[17]. - **Factor Construction Process**: - Formula: $ \text{Three-Month Reversal} = \text{Price Change over Past 60 Trading Days} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 500 and CSI 1000 index selection spaces[1][20][22]. Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: This factor measures the average absolute value of daily price changes over the past 20 trading days divided by the average trading volume[17]. - **Factor Construction Process**: - Formula: $ \text{Non-Liquidity Shock} = \frac{\text{Average Absolute Daily Price Change}}{\text{Average Trading Volume}} $[17]. - **Factor Evaluation**: This factor performed well in the CSI 1000 index selection space[1][22]. Factor Backtesting Results - **Single-Season SP**: - **CSI 300**: Weekly excess return 1.33%, monthly excess return 0.89%, annualized historical return 2.80%[19]. - **Public Fund Heavy Index**: Weekly excess return 1.45%, monthly excess return 1.56%, annualized historical return 1.98%[26]. - **Single-Season EP**: - **CSI 300**: Weekly excess return 0.99%, monthly excess return 1.48%, annualized historical return 5.37%[19]. - **CSI A500**: Weekly excess return 1.29%, monthly excess return 1.59%, annualized historical return 5.16%[24]. - **SPTTM**: - **CSI 300**: Weekly excess return 1.11%, monthly excess return 0.81%, annualized historical return 2.03%[19]. - **Public Fund Heavy Index**: Weekly excess return 1.44%, monthly excess return 1.09%, annualized historical return 0.76%[26]. - **One-Month Reversal**: - **CSI 500**: Weekly excess return 1.19%, monthly excess return -0.32%, annualized historical return -1.60%[20]. - **CSI 1000**: Weekly excess return 1.77%, monthly excess return -0.37%, annualized historical return -4.29%[22]. - **Three-Month Reversal**: - **CSI 500**: Weekly excess return 1.42%, monthly excess return -3.14%, annualized historical return -2.38%[20]. - **CSI 1000**: Weekly excess return 1.56%, monthly excess return 0.76%, annualized historical return -1.95%[22]. - **Non-Liquidity Shock**: - **CSI 1000**: Weekly excess return 1.52%, monthly excess return 2.73%, annualized historical return 2.48%[22].