Group 1 - The equity market experienced a volatile upward trend from January 19 to 23, 2026, with the Wande All A closing at 6893.11, up 1.81% from January 16, and the China Convertible Bond Index rising 2.92 during the same period [1][9] - The market has entered a narrow fluctuation range since January 13, with a net outflow of 265.9 billion yuan from stock ETFs from January 19 to 22, indicating a "slow bull" market sentiment [1][16] - The implied volatility has returned to a low level, suggesting a nurturing environment for a rebound, with the market attempting to break out of the fluctuation state [1][21] Group 2 - The strategy suggests maintaining a "slow bull" mindset, as the market attempts to break out of the narrow fluctuation range and return to an upward trend [2] - Historical analysis of 64 cases of upward breakouts from narrow fluctuation ranges since 2005 shows that such breakouts typically lead to a sustained upward trend [2][42] - The analysis of 48 instances of volume peaks since 2005 indicates that while upward trends continue after volume peaks, the pace of increase slows down, often leading to prolonged periods of fluctuation before resuming upward trends [2][45] Group 3 - In the convertible bond market, the valuation indicators are showing a decline in their timing significance, with the absolute price median and valuation center remaining at historically high levels [3][29] - The valuation center for convertible bonds at various price points remains high, with the 80 yuan parity corresponding to a valuation center of 54.44%, and the 100 yuan parity at 41.12% [3][29] - The market for convertible bonds is seeing renewed inflows, particularly in the context of strong underlying stocks, with a significant reduction in the number of convertible bonds priced below 130 yuan [3][61]

证券研究报告 短期交易地缘局势动荡，油价或震荡偏强 原油月报： 2026年1月报 证券分析师 陈潇榕 投资咨询资格编号：S1060523110001 马书蕾 投资咨询资格编号：S1060524070002 石油石化 强于大市（维持） 2026年1月25日 请务必阅读正文后免责条款 核心观点： 短期地缘风险升温，油价或呈震荡偏强走势 • 国际油价回顾：2026年1月油价呈现震荡偏强走势。2026年初以来，美委关系紧张，中东局势动荡，地缘风险升温再次成为支撑油价的主 因。重点影响事件：1）2026年1月3日,美国对委内瑞拉多处目标发动军事袭击，特朗普称抓获其总统马杜罗及其夫人并带离委内瑞拉， 并称美国将深度介入委内瑞拉石油产业。2）1月9日，特朗普与约20家石油企业高管会晤，要求他们向委内瑞拉投资1000亿美元以大幅增 产石油，但未获积极响应。3）1月12日，特朗普称对伊朗所有贸易伙伴征收25%关税。4）1月12日，维托尔集团和托克集团两家贸易巨头 已获美国政府初步批准销售委内瑞拉原油，近期就潜在的委内瑞拉原油销售事宜与中印大型炼油商展开磋商。5）1月15日消息，美国 "亚伯拉罕·林肯"号航空母舰协同多艘舰艇正驶 ...

- The report discusses the use of a fund position monitoring model to analyze public fund holdings. The model estimates that equity-biased hybrid products have reduced their holdings in the technology sector to 24.76%, while increasing their holdings in the cyclical sector to 21.33%, significantly exceeding the market benchmark weight of 18.41%[13] - The cyclical sector, particularly the chemical sector, has seen the most significant inflow of funds, with its holdings increasing from 4.6% on December 17 to 6.34% recently. Similarly, the non-ferrous metals sector maintains a high holding ratio of 8.59%, far exceeding the benchmark level[13] - The report highlights that the logic of public fund holdings has shifted from a technology AI narrative to a price increase narrative, as evidenced by the increased holdings in cyclical sectors such as chemicals, non-ferrous metals, and petrochemicals[13]

Market Overview - Global stock indices experienced more declines than gains this week, with Hong Kong, US, and European markets all showing downturns, while the A-share Shanghai Composite Index and Shenzhen Component Index rose slightly by 0.8% and 1.1% respectively[1] - In the A-share market, small-cap stocks outperformed large-cap stocks, with the Micro-cap Index, CSI 500, and CSI 2000 leading gains, while the SSE 50 and CSI 300 lagged behind[1] - In terms of sectors, cyclical and technology growth sectors performed well, with construction materials, oil and petrochemicals, steel, and chemicals leading the gains, while large financials, telecommunications, and food and beverage sectors faced declines[1] Market Outlook - The slow bull market trend is expected to continue, focusing on three main investment lines: technology sector expansion, price increase beneficiaries, and high-growth sectors in annual report forecasts[2] - The current period is marked by a high volume of annual report forecasts, with a pre-announcement success rate of 38% among over 900 listed companies, indicating strong interest in sectors like electronics, machinery, and pharmaceuticals[3] - The A-share market's trading volume remains robust, with a peak of 3.99 trillion yuan on January 14, and a turnover rate of 3.9%, suggesting potential for increased market volatility if the turnover rate continues to rise[3] Risk Factors - Key risks include unexpected global economic fluctuations, ineffective policy measures, overseas liquidity risks, and geopolitical tensions[2] - The current risk premium for the CSI 300 index stands at 5.27%, indicating that there is still ample room for growth compared to historical levels where risk premiums have dropped to around 2.5%[3]

Quantitative Models and Construction Methods Model: AI CSI 1000 Enhanced Portfolio - **Construction Idea**: The model is based on the full-spectrum fusion factor, which integrates both high-frequency and low-frequency price-volume data using deep learning and multi-task learning techniques[6][7] - **Construction Process**: 1. Train 27 high-frequency factors using a deep learning model to obtain high-frequency deep learning factors 2. Use multi-task learning to extract end-to-end features from low-frequency price-volume data, resulting in low-frequency multi-task factors 3. Combine the high-frequency and low-frequency factors to form the full-spectrum fusion factor[6] - **Evaluation**: The model shows significant excess returns and a high information ratio, indicating strong performance and effective risk management[1][7] - **Backtest Results**: - Annualized excess return: 21.60% - Annualized tracking error: 6.06% - Information ratio (IR): 3.57 - Maximum drawdown of excess return: 7.55% - Calmar ratio of excess return: 2.86[1][7] Model: LLM-FADT Text Stock Selection Strategy - **Construction Idea**: The model enhances the BERT-FADT strategy by incorporating additional interpretations from a large language model (LLM), including new title interpretations, market catalysts, implied meanings, potential risks, and return guidance[2][14][17] - **Construction Process**: 1. Input six types of text into a fine-tuned FinBERT model: original text, new title interpretations, market catalysts, implied meanings, potential risks, and return guidance 2. Convert these texts into text feature vectors 3. Train an XGBoost model using these enriched text features[17] - **Evaluation**: The LLM-FADT strategy is more stable and has smaller excess drawdowns compared to the BERT-FADT strategy, showing better performance in extreme market conditions[2][14][20] - **Backtest Results**: - Annualized return: 30.10% - Annualized excess return: 25.52% - Sharpe ratio: 1.18 - Information ratio (IR): 2.00[2][20][24] Model: AI Industry Rotation Model - **Construction Idea**: The model uses the full-spectrum fusion factor to score 32 primary industries and constructs a weekly rebalancing strategy by equally weighting the top 5 industries[3][38] - **Construction Process**: 1. Score each industry using the full-spectrum fusion factor based on the industry component stocks 2. Select the top 5 industries with the highest scores 3. Equally weight these industries and rebalance weekly[38][43] - **Evaluation**: The model effectively utilizes AI's feature extraction capabilities to capture patterns in multi-frequency price-volume data, complementing top-down strategies[3][38] - **Backtest Results**: - Annualized return: 26.87% - Annualized excess return: 19.02% - Maximum drawdown of excess return: 12.43% - Sharpe ratio of excess return: 1.85[3][41] Model: AI Thematic Index Rotation Model - **Construction Idea**: The model scores 133 thematic indices using the full-spectrum fusion factor and constructs a weekly rebalancing strategy by equally weighting the top 10 thematic indices[4][28] - **Construction Process**: 1. Score each thematic index using the full-spectrum fusion factor based on the index component stocks 2. Select the top 10 thematic indices with the highest scores 3. Equally weight these indices and rebalance weekly[28][31] - **Evaluation**: The model leverages AI to identify and capitalize on trends in thematic indices, providing a diversified and dynamic investment approach[4][28] - **Backtest Results**: - Annualized return: 16.92% - Annualized excess return: 9.37% - Maximum drawdown of excess return: 20.79% - Sharpe ratio of excess return: 0.73[4][30] Model Backtest Performance AI CSI 1000 Enhanced Portfolio - Annualized excess return: 21.60% - Annualized tracking error: 6.06% - Information ratio (IR): 3.57 - Maximum drawdown of excess return: 7.55% - Calmar ratio of excess return: 2.86[1][7] LLM-FADT Text Stock Selection Strategy - Annualized return: 30.10% - Annualized excess return: 25.52% - Sharpe ratio: 1.18 - Information ratio (IR): 2.00[2][20][24] AI Industry Rotation Model - Annualized return: 26.87% - Annualized excess return: 19.02% - Maximum drawdown of excess return: 12.43% - Sharpe ratio of excess return: 1.85[3][41] AI Thematic Index Rotation Model - Annualized return: 16.92% - Annualized excess return: 9.37% - Maximum drawdown of excess return: 20.79% - Sharpe ratio of excess return: 0.73[4][30]

Group 1 - Since September 2024, the proportion of southbound capital in transaction volume has rapidly increased to 20%-30%, nearly doubling compared to before 2024, indicating a significant shift in market dynamics [3][8] - Historical reviews of two rounds of competition for pricing power in the Hong Kong stock market occurred in 2016-2017 and 2020-2021, typically initiated by policy optimizations and inflows of incremental capital [15][28] - The current southbound capital inflow is characterized by a higher proportion of medium to long-term funds, with insurance capital making 41 stakes in 2025, 35 of which were in H-shares, marking a record high in the past decade [3][31] Group 2 - The industries where southbound capital and Chinese capital have pricing power include semiconductors and dividend-paying sectors, while industries lacking pricing power include internet, hardware, software services, home appliances, and media [3][36] - The top five industries by southbound capital holdings include coal (41.8%), semiconductors (32.7%), environmental protection (24.5%), oil and petrochemicals (24.1%), and pharmaceutical biology (20.5%) [37] - The active management public funds have a low preference for Hong Kong stocks, with significant holdings concentrated in AI-related CSP giants, electronics, and innovative pharmaceuticals [46] Group 3 - The current sentiment in the Hong Kong stock market has fully reflected negative factors such as US-China trade friction and the high unlock peak at the end of last year, suggesting potential upward investment opportunities if liquidity pressure eases [53][54] - The spring rally in the Hong Kong stock market has a high probability of success, with southbound capital and foreign capital expected to net inflow at the beginning of the year, driven by the demand for core Chinese assets [53][54] - The pricing power of southbound capital is rapidly increasing, with expectations of a potential upward beta in the Hong Kong stock market at the beginning of the year [3][53]

Market Review - Global stock indices experienced more declines than gains this week, with Hong Kong, US, and European markets all showing downturns. In contrast, the A-share market saw slight increases, with the Shanghai Composite Index and Shenzhen Component Index rising by 0.8% and 1.1% respectively. Small-cap stocks outperformed large-cap stocks, with indices such as the Micro-cap Index, CSI 500, and CSI 2000 leading gains, while the SSE 50 and CSI 300 lagged behind. In terms of sectors, cyclical and technology growth sectors performed well, with construction materials, oil and petrochemicals, steel, and chemicals leading the gains, while large financials, telecommunications, and food and beverage sectors faced declines. In the commodities market, precious metals continued to strengthen, with COMEX silver and gold prices reaching new historical highs, while domestic black commodities remained weak. The US dollar index fell below 98, and the RMB appreciated against the US dollar [1][2][3]. Market Outlook - The report maintains a "slow bull" market trend and focuses on three main investment lines. In the past two weeks, under "counter-cyclical adjustment" measures, net outflows from major A-share ETFs and a slight decline in financing balances have effectively controlled trading momentum. Market turnover remains relatively high, with strong support for small-cap growth stocks, indicating a shift into a phase of accelerated sector rotation. Looking ahead, the current period coincides with a dense disclosure of annual report forecasts, with high-growth sectors becoming the focal point of market attention. The report suggests focusing on the expansion of technology trends, price increase themes, and sectors with high growth in annual report forecasts [2][3]. Sector Allocation - The report recommends focusing on the following sectors: 1) Technology industry expansion, including AI computing, AI applications, robotics, space photovoltaics, storage, and Hong Kong internet sectors 2) Sectors benefiting from "anti-involution" and price increases, such as chemicals and non-ferrous metals 3) Industries with high growth in annual report forecasts, including electronics, machinery, and pharmaceuticals [2][3]. Structural Analysis - Currently, the market is in a window of dense annual report forecast disclosures, with high growth or improving sectors becoming the focus. As of January 24, over 900 listed companies have disclosed their 2025 performance forecasts, with an overall positive forecast rate of 38%. In specific sectors, those with high growth in annual reports (with a median year-on-year growth rate of over 100% in net profit after deducting non-recurring gains) include PCB, storage, optical modules, lithium batteries, non-ferrous metals, and pharmaceuticals. Since the beginning of the year, the Wind pre-increase index has risen by 18%, indicating that outstanding performance sectors have become one of the market's focal points [3][4]. Long-term Perspective - From a medium to long-term perspective, comparing the current A-share market to previous bull markets, this round of market activity is still in the middle stage, with a "slow bull" trend expected to continue. Compared to the peaks of the bull markets in 2007, 2015, and 2021, the CSI 300 index has only reached the mid-stage, with current index levels significantly lower than previous highs. The current risk premium of the CSI 300 is 5.27%, which is higher than the 2.5% level seen in previous bull markets. Additionally, the ratios of total A-share market capitalization to M2 and free float market capitalization to household deposits are both near historical averages, indicating that there is still ample space and opportunity for the market [3][4].

个股方面，本周中国平安、美的集团、海光信息、寒武纪-U、赣锋锂业获主力净流入居前，均超10亿 元；净流出方面，三花智控、中际旭创、香农芯创遭主力净流出居前，分别为61.4亿元、49.88亿元、 41.69亿元。 按申万一级行业来看，本周银行、非银金融、有色金属、煤炭、石油石化五大行业获得主力资金青睐， 其中，银行业获主力净流入47.52亿元；在净流出方面，电子、通信、计算机、电力设备、机械设备行 业均遭抛售超百亿元。 ...

Core Insights - The CCG50 Forum released the 2025 annual rankings of Chinese listed companies' governance and ESG performance, evaluating 5,292 companies based on various governance indices and ESG criteria [1][3][13]. Governance Rankings - The governance rankings include 11 core lists, with the top 100 companies ranked based on a comprehensive index. Notable companies include: - Health元, 唐山港, and 西部证券 leading the overall governance list [2][8]. - 中煤能源 topped the small investor protection list, evaluated on 36 indicators across four dimensions [2][15]. - 唐山港 ranked first in the board governance category, assessed on 38 indicators [2][21]. - 埃斯顿 led the financial governance list, evaluated on 31 indicators [2][39]. ESG Rankings - The ESG rankings are divided into non-financial and financial sectors: - In the non-financial sector, 中国石油, 中国石化, and 中国中铁 ranked highest, evaluated on 132 indicators with weights of 55% for governance, 35% for social responsibility, and 10% for environmental protection [3]. - The financial sector's top ten ESG companies include 工商银行 and 农业银行, evaluated based on industry-specific criteria [3]. Risk Awareness - The forum also published a list of 50 companies with governance risks, including *ST 广道 and ST 中迪, providing a reference for investors [3][19]. Methodology - The rankings were developed using scientific modeling and quantitative calculations rather than traditional voting methods, referencing international standards to showcase the differences in governance levels and ESG performance among Chinese listed companies [3][13][19].

- The report introduces a quantitative model based on Convolutional Neural Networks (CNNs) to analyze price-volume data and predict future prices. The model standardizes price-volume data into graphical representations and maps learned features to industry theme indices, such as the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[78][80][81] - The construction process of the CNN model involves transforming individual stock price-volume data within a specific window into standardized graphical charts. These charts are then input into the CNN for feature extraction and prediction modeling. The learned features are subsequently applied to identify and allocate industry themes[78][80] - The evaluation of the CNN model highlights its ability to capture complex patterns in price-volume data and effectively map these patterns to industry themes. This approach provides a novel perspective for quantitative investment strategies[78][81] - Backtesting results indicate that the CNN model's latest configuration suggests a focus on themes such as petrochemicals, chemicals, machine tools, semiconductors, and nonferrous metals. Specific indices include the CSI Petrochemical Industry Index, CSI Subdivision Chemical Industry Theme Index, CSI Machine Tool Index, CSI Semiconductor Material Equipment Theme Index, and CSI Nonferrous Metals Index[80][81]