Group 1 - A-shares opened higher with the Shanghai Composite Index up 0.21%, Shenzhen Component Index up 0.37%, and ChiNext Index up 0.48% on January 26, 2023, driven by sectors like non-ferrous metals and photovoltaic [1] - Recent market dynamics show a divergence in fund flows, with over 570 billion yuan exiting broad-based ETFs while approximately 110 billion yuan flowed into thematic industry ETFs, indicating a positive overall market sentiment [2] - The focus remains on "technology + resource products" as key investment themes, with sectors like AI semiconductors, new energy, and non-ferrous metals showing strong performance and growth potential [2] Group 2 - The spring market is expected to progress, with key themes including commercial aerospace and AI applications, alongside a focus on price increase chains driven by supply-demand mismatches [3] - The current price increase trend is supported by the expansion of AI hardware chains and upstream raw material chains benefiting from supply gaps, such as lithium carbonate and PTA [3] - The global shift in commodity prices, particularly in gold and silver, suggests a need to adjust pricing frameworks for scarce physical assets and core equity assets due to declining currency value [4]

Core Viewpoint - The A-share market experienced a divergence in capital sentiment last week, with small-cap stocks leading in gains, and industry rotation continuing. The focus is on the elasticity of capital and the direction of future rotations [1] Group 1: Market Dynamics - Since mid-January, the outflow of capital from broad-based ETFs has been relatively high, but there are still inflows from insurance funds and arbitrage demands from investors, indicating ongoing market momentum [1] - The rotation direction may gradually shift from thematic investments to sectors with performance validation, as historically, industries with sustained recovery capabilities during earnings forecast disclosures tend to yield excess returns [1] Group 2: Sector Focus - Current recovery signals are primarily concentrated in price increase chains, high-end manufacturing, and AI-related sectors. Specific attention is recommended for power equipment, basic chemicals, and semiconductor equipment, with a moderate increase in allocation towards cyclical dividends [1]

Group 1 - The macro environment shows easing external pressures from US-Europe relations, with a rebound in the Fed's interest rate cut trades and stable domestic macro data, alongside improvements in real estate high-frequency indicators [1] - Foreign and southbound capital continue to flow in, with public fund positions in Hong Kong stocks dropping to 23% in Q4, significantly reducing potential selling pressure [1] - The sentiment index has returned to a neutral range, with bullish expectations increasing, indicating a continued potential for a rebound in the first quarter [1] Group 2 - Focus on the AI chain (semiconductors, software) and innovative pharmaceuticals, while gradually accumulating quality consumer leaders and overweighting the upstream of the cyclical and power chains [1]

晨会纪要（R3） 晨会纪要 2026 年 01 月 26 日 | 市场数据 | | | | --- | --- | --- | | 指数名称 | 收盘 | 涨跌% | | 上证指数 | 4136.16 | 0.33 | | 深证成指 | 14439.66 | 0.79 | | 创业板指 | 3349.50 | 0.63 | | 科创 50 | 1553.71 | 0.78 | | 北证 50 | 1588.66 | 3.82 | | 沪深 300 | 4702.50 | -0.45 | 上证指数-沪深 300 走势图 -6% 4% 14% 24% 34% 2025-01 2025-04 2025-07 2025-10 上证指数 沪深300 | 何晨 | 分析师 | | --- | --- | | 执业证书编号:S0530513080001 | | | hechen@hnchasing.com | | | 胡跃才 | 分析师 | | 执业证书编号:S0530525070001 | | | huyuecai@hnchasing.com | | 晨会聚焦 一、财信研究观点 【债券研究】债券市场综述 二、重要财经资讯 ...

登录新浪财经APP 搜索【信披】查看更多考评等级 公告送出日期：2026年1月26日 1.公告基本信息 ■ 注：- 2.日常申购、赎回（转换、定期定额投资）业务的办理时间 英大上证科创板综合指数增强型发起式证券投资基金（以下简称"本基金"）自2026年1月30日起（含当 日）开放本基金基金份额的申购、赎回、转换及定期定额投资业务。 投资人在开放日办理基金份额的申购和赎回，具体办理时间为上海证券交易所、深圳证券交易所的正常 交易日的交易时间（若本基金参与港股通交易且该工作日为非港股通交易日，则基金管理人可根据实际 情况决定本基金是否开放基金份额申购与赎回等业务，具体以届时提前发布的公告为准），但基金管理 人根据法律法规、中国证监会的要求或《英大上证科创板综合指数增强型发起式证券投资基金基金合 同》（以下简称"《基金合同》"）的规定公告暂停申购、赎回时除外。若出现新的证券/期货交易市 场、证券/期货交易所交易时间变更、港股通交易规则变更或其他特殊情况，基金管理人将视情况对前 述开放日及开放时间进行相应的调整，但应在实施日前依照《信息披露办法》的有关规定在规定媒介上 公告。 基金管理人不得在基金合同约定之外的日期或 ...

Group 1 - The core viewpoint of the articles highlights the increasing internationalization of Chinese securities firms, with firms like Huatai Securities and GF Securities actively investing in their international subsidiaries to enhance competitiveness in the global market [1][2]. - Huatai Securities plans to increase its investment in its wholly-owned subsidiary, Huatai International Financial Holdings, by up to 9 billion HKD to support overseas business development [1]. - Huatai International has become a key platform for Huatai Securities' international operations, with significant revenue contributions, reporting 3.762 billion HKD in revenue and 1.145 billion HKD in net profit for the first half of 2025 [1]. Group 2 - The internationalization strategy is seen as a crucial approach for Chinese securities firms to expand their profit margins, with several firms, including GF Securities, also announcing substantial capital increases for their overseas subsidiaries [2]. - Analysts suggest that increasing capital for international subsidiaries will enhance the capital strength of Chinese securities firms, improving their competitiveness and market share in the international arena [2]. - The focus of international business development is shifting from mere scale expansion to value cultivation, with firms aiming to provide comprehensive financial services and support for domestic enterprises going global [3].

Core Viewpoint - The ETF-FOF market is experiencing a surge in new products, with expectations for significant growth as major banks increase their involvement and the focus shifts from alpha to beta returns [1][2]. Group 1: Market Trends - As of early 2026, six ETF-FOF products have been reported, with three more entering the sales phase, indicating a growing interest in this investment vehicle [1]. - The domestic ETF market is projected to exceed 6 trillion yuan by 2025, with over 1,400 ETFs available, creating favorable conditions for the development of ETF-FOF products [2]. Group 2: Investment Strategy - ETF-FOF represents a new paradigm in asset allocation, allowing for a focus on the underlying assets rather than solely on fund managers, which aligns with future trends in the FOF industry [2]. - The shift towards beta management is seen as crucial for achieving stable absolute returns, while alpha management is viewed as a supplementary strategy [3]. Group 3: Institutional Participation - Major banks are increasingly entering the FOF market, with products like the "Long Win Plan" from China Construction Bank and others achieving significant fundraising success, indicating strong demand for these investment products [4]. - An estimated 50 trillion yuan in long-term deposits will mature in 2026, suggesting a substantial influx of capital into financial investments, with stable products like FOF likely to attract a significant portion of this capital [4]. Group 4: Future Outlook - The FOF market in China is entering a golden development period, with the potential to evolve from a niche tool to a mainstream investment choice as investor awareness and product optimization improve [5].

[Table_Title2] 保险Ⅱ 行业评级: 推荐 证券研究报告|行业点评报告 [Table_Date] 2026 年 01 月 25 日 [Table_Title] 2025Q4 非银板块公募持仓点评：非银重仓环 比提升，保险获显著增配 ► 保险板块环比显著增配 在主动基金重仓持股口径下，保险板块持仓比例由三季度末的 0.61%显著提升至四季度末的 1.25%，我们预 计主要系 2026 年开门红和资负匹配均预期向好。个股上，四季度末保险板块持仓市值前五大股票分别为：中国 平安 A 持股数量 3.15 亿股（环比+97.6%）、持仓市值 215.44 亿元（环比+145.2%）；中国太保 A 持股数量 1.78 亿股（环比+66.3%）、持仓市值 74.68 亿元（环比+98.4%）；中国人寿 H 持股 1.48 亿股（环比 +44.2%）、持仓市值 36.61 亿元（环比+76.7%）；中国平安 H 持股数量 0.59 亿股（环比+107%）、持仓市值 34.79 亿元（环比+151.5%）；新华保险 A 持股数量 0.40 亿股（环比+33.5%）、持仓市值 28.05 亿元（环比 +52.2%）。 ...

Investment Rating - The industry investment rating is "Overweight" (maintained) [1] Core Insights - The report indicates a significant improvement in market trading volume and new fund issuance at the beginning of 2026, which is favorable for the fundamentals of financial IT and brokerage sectors. Brokerage firms are expected to continue rapid growth in their brokerage business, while investment banking, asset management, and overseas expansion are likely to enhance the return on equity (ROE) of leading brokerage firms. The insurance sector has also seen a strong start in both individual and bank-insurance channels, with a continued trend of deposit migration, suggesting a positive outlook for the insurance sector in the spring market [4][6]. Summary by Sections Brokerage Sector - Daily average trading volume for stock funds reached 3.44 trillion, down 16% week-on-week; however, the average trading volume since the beginning of 2026 is 3.64 trillion, a 105% increase compared to Q1 2025 [4] - New stock and mixed fund issuance in January 2026 totaled 44.3 billion, a 56% year-on-year increase [4] - The "Public Fund Performance Benchmark Guidelines" was officially released on January 23, 2026, establishing stricter standards for benchmark selection and changes, enhancing performance evaluation and compensation management systems [4] Insurance Sector - The fourth quarter of 2025 saw a stable research value for ordinary life insurance products at 1.89%, slightly down from 1.90% in the previous quarter, indicating a trend towards stability [6] - The individual insurance channel is under pressure due to various factors, but the strong start in 2026 is expected to improve new policy growth, aided by favorable market conditions [6] - The stabilization of long-term interest rates and a favorable equity market are expected to enhance net assets and profitability for insurance companies, with a potential valuation recovery towards 1x PEV for leading firms [6] Recommended Stocks - Recommended stocks include Guangfa Securities, Guotai Junan, Huatai Securities, and China International Capital Corporation H, as well as China Life, China Pacific Insurance, and Ping An Insurance [7]

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]