Group 1: Economic Indicators - In January, the US manufacturing and services PMI exceeded expectations, indicating overall robust growth overseas [1] - In contrast, China's manufacturing PMI fell back into contraction territory, with both supply and demand components weakening compared to seasonal levels [1][10] - The price index has risen further, reflecting a rapid increase in upstream raw material prices, which is expected to suppress manufacturing supply and demand in the short term [1] Group 2: Market Sentiment and Strategies - Market risk appetite has declined, leading to weaker performance in A-shares and Hong Kong stocks, while bonds saw a slight increase [1][11] - The bond market experienced volatility, with the long end performing stronger due to a rebound logic and hedging demand, despite overall bond market gains falling short of expectations [1][10] - In the equity market, there is a potential for stabilization in risk appetite as volatility is digested, with a focus on high-yield assets and long-duration assets for value allocation [1][11] Group 3: Sector Analysis - The A-share market sentiment has weakened due to fluctuations in overseas markets, but there is potential for recovery in cyclical sectors and consumption as selling pressure from state-owned entities eases [11] - Small-cap and growth sectors may present good opportunities, with improved cost-effectiveness in growth stocks and a favorable calendar effect for small-cap stocks post-Spring Festival [11][12] - The Hong Kong market is currently in a phase of benefiting from liquidity, but its fundamentals remain weak, with the improvement of price levels by 2026 being crucial [12] Group 4: Commodity Insights - Recent geopolitical tensions have driven up gold prices due to increased safe-haven demand, although a subsequent drop occurred due to overheating in trading and expectations surrounding the Federal Reserve [3][12] - Oil prices have been influenced by threats against Iran and cold weather, but significant improvements in the oil supply-demand fundamentals are still under observation [12]

Quantitative Models and Construction Methods Model 1: Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Model Name**: Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Construction Idea**: The strategy aims to select funds with high stock price difference income, active trading motivation, and low possibility of performance dressing[2] - **Construction Process**: - The strategy combines the trading motivation factor and the stock price difference income factor - The trading motivation factor is constructed by classifying the trading motivations of funds[23] - The stock price difference income factor is derived from the stock price difference income in the fund's income statement[23] - The strategy adopts a semi-annual rebalancing approach, rebalancing at the end of March and August each year[23] - **Evaluation**: The strategy significantly outperformed the Wind Partial Equity Hybrid Fund Index in January 2026[2] Model 2: Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Model Name**: Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Construction Idea**: The strategy aims to capture the unique trading patterns of fund managers to generate excess returns[3] - **Construction Process**: - Construct a network based on the detailed holdings and transactions of fund managers[31] - Develop an indicator to measure the uniqueness of fund managers' trading[31] - The strategy adopts a semi-annual rebalancing approach, rebalancing at the beginning of April and September each year[31] - **Evaluation**: The strategy outperformed the Wind Partial Equity Hybrid Fund Index in January 2026[3] Model 3: Industry Theme ETF Rotation Strategy Based on Application Information - **Model Name**: Industry Theme ETF Rotation Strategy Based on Application Information - **Construction Idea**: The strategy aims to select industry theme ETFs similar to the applied ETFs to capture market investment hotspots[4] - **Construction Process**: - Conduct event-driven research on the entire issuance process of funds[36] - Construct the industry theme application similarity factor (T+1) based on the information disclosed during the application material public stage[36] - The strategy adopts a monthly rebalancing approach, with a transaction fee rate of 0.1% per side[36] - **Evaluation**: The strategy significantly outperformed the CSI 800 Index in January 2026[4] Model Backtesting Results Fund Selection Strategy Based on Trading Motivation Factor and Stock Price Difference Income Factor - **Monthly Return**: 10.96%[27] - **Annualized Return**: 11.56%[27] - **Annualized Volatility**: 21.60%[27] - **Sharpe Ratio**: 0.54[27] - **Maximum Drawdown**: 48.39%[27] - **Annualized Excess Return**: 3.87%[27] - **Excess Maximum Drawdown**: 19.22%[27] - **Information Ratio (IR)**: 0.64[27] - **Monthly Excess Return**: 3.60%[27] Fund Selection Strategy Based on Fund Manager's Trading Uniqueness - **Monthly Return**: 8.03%[35] - **Annualized Return**: 14.26%[35] - **Annualized Volatility**: 19.47%[35] - **Sharpe Ratio**: 0.73[35] - **Maximum Drawdown**: 37.26%[35] - **Annualized Excess Return**: 5.70%[35] - **Excess Maximum Drawdown**: 10.84%[35] - **Information Ratio (IR)**: 1.10[35] - **Monthly Excess Return**: 0.86%[35] Industry Theme ETF Rotation Strategy Based on Application Information - **Monthly Return**: 22.66%[40] - **Annualized Return**: 22.45%[40] - **Annualized Volatility**: 21.39%[40] - **Sharpe Ratio**: 1.05[40] - **Maximum Drawdown**: 34.89%[43] - **Annualized Excess Return**: 13.84%[43] - **Excess Maximum Drawdown**: 19.07%[43] - **Information Ratio (IR)**: 0.76[43] - **Monthly Excess Return**: 18.18%[43]

Core Viewpoint - Xinhongye (301310.SZ) has signed a partnership agreement to establish an investment fund focused on superconducting materials, with a total investment scale of 21.46 million RMB [1] Group 1: Investment Details - The investment fund, named Qingdao Jingde Venture Capital Partnership (Limited Partnership), will have a total scale of 21.46 million RMB [1] - Xinhongye will contribute 15 million RMB, representing a 69.8975% stake in the partnership [1] - The fund will focus on research, production, and related sectors of high-quality targets in the superconducting materials field [1]

在多平台下架"基金实时估值"相关功能后，不少博主在小红书等社交平台分享可进行实时估值的小程序或是软件。 此前报道：突发！基金实时估值再被叫停 应监管要求，多平台于月初下架"基金实时估值"相关功能，随后小红书平台多个笔记显示，大量账号分享了自制的实时估值小程序或软件。不过最近几日 小红书禁言了多个分享基金实时估值工具的博主。记者注意到，上周发布的关于基金实时估值小程序或软件的笔记，有不少已被删除，部分分享链接已不 可用。有博主开小号称大号被禁言一个月，并在笔记评论区"指路"同名公众号。 不过，最近小红书已关注到这一现象，部分账号因此被禁言、相关笔记被删除。记者注意到，有被禁言账号转战微信公众号，也有博主转向小红书群聊分 享。不过已有博主称被举报后禁止使用群聊部分功能。 不久前监管机构发布的2026年第1期机构监管情况通报提到，日常监管关注到部分基金销售机构和非持牌的第三方平台重新上架"基金实时估值"功能，直 接提供或通过"净值参考""板块涨跌"等形式变相提供"基金实时估值"功能，同时还衍生出按平台当日申购金额排序的"加仓榜"、展示用户持仓收益和明细 的"实盘榜"等。上述功能可能误导投资者，摊薄基金产品收益，引发 ...

Core Insights - The Shenzhen Stock Exchange has released the "ETF Market Development White Paper (2025)", outlining the innovative trajectory of new ETF products driven by policy guidance and market demand [1] - By the end of 2025, the total scale of domestic ETFs is expected to exceed 6 trillion yuan, with an annual growth rate of 62% [2] Group 1: ETF Product Development - The white paper highlights a clear strategy for the development of ETFs in Shenzhen, focusing on the economic transformation and reform of the ChiNext market, with the introduction of multiple ChiNext 50 and ChiNext Composite Index ETFs [1] - The launch of thematic ETFs in artificial intelligence and robotics allows investors to easily share in the benefits of industrial transformation [1] - The bond ETF sector has seen significant innovation, with the introduction of 4 benchmark credit bond ETFs and 10 sci-tech bond ETFs, totaling a scale of 203.1 billion yuan, which is a 4.35 times increase compared to before listing [1] Group 2: Market Growth and Structure - The growth of the ETF market is primarily driven by the increase in existing product scales and the issuance of high-quality new products, contributing nearly 70% to the market increment [2] - The structure of new product issuance shows a solid multi-dimensional development pattern, with broad-based ETFs expanding steadily, while industry-themed ETFs have experienced explosive growth, particularly in the technology sector [4] - Strategy ETFs, such as those focused on dividends and free cash flow, have seen a nearly doubling in number, while bond ETFs contributed 489 billion yuan in new issuance [4] Group 3: Institutional Participation - The year 2025 is marked as a significant year for the issuance of new ETF products, with E Fund leading the industry by launching 32 new ETFs [4] - Other major institutions like China Universal Asset Management and Penghua Fund have also contributed significantly, with over 20 new ETFs each [4] - A total of 16 fund companies launched more than 10 new ETFs in the year, indicating a competitive landscape that injects continuous innovation into the ETF market [4]

Group 1 - Nanjing has established a market-oriented mother fund with a total scale of 100 billion yuan, featuring a 20-year duration, which is rare among government-led innovation funds in China [1] - The mother fund aims to attract quality fund management institutions and social capital to support the high-quality development of the real economy, focusing on cultivating enterprises with long-term core competitiveness [1] - The total scale of funds launched and signed at the 2026 Zijin Mountain Venture Capital Conference exceeds 700 billion yuan, including various provincial and central enterprise funds [2][3] Group 2 - In addition to the mother fund, two other funds, each with a scale of 100 billion yuan, were unveiled, focusing on modern service industry innovation and the full lifecycle cultivation of sci-tech enterprises [2] - A total of 15 artificial intelligence industry chain funds were established to meet the equity financing needs of AI enterprises, while 9 angel investment funds with a total scale of 47 billion yuan were also signed to support early-stage tech startups [3] - The Nanjing municipal government has introduced policies to accelerate the cultivation of new productive forces, aiming to create an industrial fund cluster exceeding 2000 billion yuan [3][4]

Rock carved with “ETF” on a stormy shoreline, waves crashing nearby and a lighthouse in the distance. Key Points Following a reversal in the precious metals rally, investors looking for defensive plays might consider ETFs that employ strategies to protect against downside risk. SPLV and SWAN both provide targeted exposure to the S&P 500 (or portions thereof) while attempting to manage risk using volatility metrics and Treasurys, respectively. TLT aims for long-dated Treasurys, attempting to balance a p ...

Group 1 - The core viewpoint is that while there are concerns about the absolute growth of AI companies, their stock price increases are supported by EPS growth, making valuation increases reasonable [1][2] - AI companies are expected to have stable profit models and broad market space, which may allow them to enjoy higher valuations [1] - The focus of investment may shift from a few standout companies to a more diversified opportunity landscape, with a balanced approach to AI stocks and other sectors [1][2] Group 2 - The penetration of AI in businesses is expected to be gradual, requiring time for companies to integrate AI into their operations [2] - If AI fails to translate into commercial value, it could negatively impact the market, leading to a cautious optimism regarding AI's long-term trend [2] - The growth style is expected to remain strong in 2025, but excess returns have started to narrow compared to other styles, indicating a potential shift towards a more balanced market style in 2026 [2][3] Group 3 - Global economic growth is anticipated to continue in 2026, supported by AI-related investments, a loose monetary policy environment, and reduced tariff uncertainties [3][4] - Emerging markets are expected to outperform developed markets, driven by capital market reforms and the concentration of AI industry chains [5][6] - The trend of diversifying asset allocation away from a heavy reliance on US assets towards a more varied approach is expected to strengthen over the next three to five years [6] Group 4 - In the Chinese market, the focus is on the transformation of the economy, with rising consumption contributing to GDP and creating investment opportunities in new consumption sectors [6][7] - The real estate sector is no longer the preferred investment choice, with high-value exports expected to drive corporate profitability [7] - The role of private enterprises in China's economy is anticipated to grow, supported by government policies aimed at enhancing their vitality [7][8] Group 5 - A balanced investment strategy is recommended to reduce portfolio volatility, including long-term assets and healthy revenue-generating companies, particularly in AI, innovative pharmaceuticals, and stable consumer sectors [8] - Improvements in corporate governance and increased returns to investors are expected to attract foreign capital into the market [8] - The trend of increasing dividend payout ratios in A-shares is seen as a positive factor for attracting foreign investment [8]

Group 1 - The estimated profit for today is +70,000 (assets of 5.5 million), with a total profit of +119,000 across two accounts, resulting in a profit-loss ratio of +1.49% [1] - The Hang Seng Technology index has rebounded after a 20% correction from its peak, indicating a potential upward trend [1] - The recent surge in AI applications has led to significant gains, with the Media ETF rising over 4% and the Software ETF increasing over 3% [5] Group 2 - In January, there were 546,300 new fund accounts opened, representing a month-on-month increase of 123.8% and a year-on-year increase of 168.72% [6][9] - The increase in new fund accounts suggests a significant influx of retail investor capital into public funds [6] - The actual increase in fund shares will be assessed when the fund association releases data at the end of February [7][9] Group 3 - The surge in new fund accounts may be influenced by short-term arbitrage opportunities, particularly related to silver LOF [8] - If new account openings continue to rise in February, it would indicate a shift of household savings into financial products [9] - Conversely, a decline in new account openings would suggest that many new accounts were opened for short-term trading rather than long-term investment [9] Group 4 - The A-share market temperature is at 71.36, up 1.6 degrees from the previous trading day, while the Hong Kong market temperature is at 54.41, up 1.9 degrees [10][11] - The MACD golden cross signal has formed, indicating a positive trend for certain stocks [11]

Report Overview - The report is a weekly update on public - offering funds from February 2, 2026, to February 6, 2026, analyzing the performance of the equity and fixed - income markets and various fund indices [1][3]. 1. Investment Rating - The report does not mention the industry investment rating. 2. Core Views - In the equity market, last week (2026.02.02 - 2026.02.06), major indices like the Shanghai Composite Index, CSI 300, and ChiNext Index declined. The A - share market's downward volatility increased due to global resource futures fluctuations and US tech giants' earnings announcements. The tech sector may face more pressure after the Two Sessions, while mid - stream industries with profitability repair and ROE improvement are worth attention [3][12]. - In the fixed - income market, the bond yield curve flattened last week. Short - term negative factors have been eliminated, so the bond market may not experience significant fluctuations. However, after the Spring Festival, the bond market's volatility may increase due to the active stock market and policy expectations [4][14]. 3. Summary by Directory 3.1 Weekly Market Observation 3.1.1 Equity Market - Last week, the Shanghai Composite Index fell 1.27%, the CSI 300 dropped 1.33%, and the ChiNext Index declined 3.28%. The A - share market's volatility increased, and the overall market sentiment cooled. The daily average trading volume of the whole A - shares was 24032 billion, a decrease from the previous week. Industry rotation was rapid, with resource - cycle sectors and power equipment being active. The tech - growth sector faced adjustment pressure [12]. - The value style strengthened due to low - level price rebounds and high - dividend defensive needs. The market is trading "event - driven prosperity improvement" and "assets with clear price signals." The tech sector may face more pressure after the Two Sessions, and mid - stream industries with profitability repair and ROE improvement should be focused on [13]. 3.1.2 Pan - Fixed - Income Market - Last week, the bond yield curve flattened. The 1 - year Treasury yield rose 1.80BP to 1.32%, the 10 - year yield fell 0.1BP to 1.81%, and the 30 - year yield dropped 3.8BP to 2.25%. The long - end yield continued to decline, and the term spread narrowed. The bond market was oscillating strongly. Some risk - averse funds may have flowed into the bond market due to stock market fluctuations, and the central bank actively provided liquidity. The local bond issuance was well - received, eliminating previous market concerns. However, after the Spring Festival, the bond market's volatility may increase [4][14]. - US Treasury yields declined last week. The 1 - year yield dropped 3BP to 3.45%, the 2 - year yield fell 2BP to 3.50%, and the 10 - year yield decreased 4BP to 4.22%. The employment market data showed a cooling trend, which led to the decline of the US Treasury yield curve. The CSI REITs Total Return Index fell 0.91% last week, and most sectors declined. Four new public - offering REITs made progress in the primary market [15]. 3.2 Fund Index Performance Tracking 3.2.1 Equity Fund Index Performance | Index Classification | Last Week | Last Month | Since the Beginning of this Year | Since Strategy Implementation | | --- | --- | --- | --- | --- | | Active Stock Fund Selection | - 2.20% | 3.82% | 7.86% | 55.05% | | Value Stock Fund Selection | - 0.24% | 3.64% | 6.91% | 29.34% | | Balanced Stock Fund Selection | - 1.81% | 2.25% | 6.04% | 37.83% | | Growth Stock Fund Selection | - 2.65% | 3.40% | 8.12% | 56.09% | | Pharmaceutical Stock Fund Selection | - 0.85% | - 5.45% | 3.27% | 15.22% | | Consumption Stock Fund Selection | - 0.57% | 1.17% | 4.13% | 9.11% | | Technology Stock Fund Selection | - 1.98% | 1.83% | 6.27% | 62.66% | | High - end Manufacturing Stock Fund Selection | - 3.30% | 1.58% | 7.39% | 42.12% | | Cyclical Stock Fund Selection | - 3.76% | 4.70% | 9.95% | 43.97% | [17] 3.2.2 Pan - Fixed - Income Fund Index Performance | Index Classification | Last Week | Last Month | Since the Beginning of this Year | Since Strategy Implementation | | --- | --- | --- | --- | --- | | Money - enhancing Index | 0.03% | 0.12% | 0.14% | 4.63% | | Short - term Bond Fund Selection | 0.04% | 0.15% | 0.17% | 4.79% | | Medium - and Long - term Bond Fund Selection | 0.09% | 0.45% | 0.42% | 7.25% | | Low - volatility Fixed - income + Fund Selection | - 0.04% | 0.51% | 0.95% | 5.56% | | Medium - volatility Fixed - income + Fund Selection | - 0.63% | 0.42% | 1.15% | 7.54% | | High - volatility Fixed - income + Fund Selection | - 0.23% | 0.69% | 1.65% | 9.98% | | Convertible Bond Fund Selection | - 0.20% | 3.63% | 6.68% | 32.56% | | QDII Bond Fund Selection | - 0.26% | - 0.13% | - 0.18% | 9.82% | | REITs Fund Selection | - 1.86% | 2.19% | 3.77% | 35.61% | [18] 3.3 Index Positioning and Benchmarks 3.3.1 Equity Strategy Theme Index - Active Stock Fund Selection Index: Selects 15 funds each period, with equal - weight allocation. The core position selects active equity funds based on performance competitiveness and style stability, and the style distribution is balanced according to the CSI Equity - oriented Fund Index. The benchmark is the CSI Equity - oriented Fund Index [19]. 3.3.2 Investment Style Index - Value Stock Fund Selection Index: Selects 10 funds with deep - value, quality - value, and balanced - value styles. The benchmark is the CSI 800 Value Index [19]. - Balanced Stock Fund Selection Index: Selects 10 funds with relatively balanced and value - growth styles. The benchmark is the CSI 800 [21]. - Growth Stock Fund Selection Index: Selects 10 funds with active - growth, quality - growth, and balanced - growth styles. The benchmark is the 800 Growth Index [25]. 3.3.3 Industry Theme Index - Pharmaceutical Stock Fund Selection Index: Selects 15 funds based on the intersection market value ratio of fund equity holdings and the representative index (CITIC Pharmaceutical). The benchmark is the pharmaceutical theme fund index [27]. - Consumption Stock Fund Selection Index: Selects 10 funds based on the intersection market value ratio of fund equity holdings and representative indices (CITIC Automobile, Home Appliances, etc.). The benchmark is the consumption theme fund index [31]. - Technology Stock Fund Selection Index: Selects 10 funds based on the intersection market value ratio of fund equity holdings and representative indices (CITIC Electronics, etc.). The benchmark is the technology theme fund index [34]. - High - end Manufacturing Stock Fund Selection Index: Selects 10 funds based on the intersection market value ratio of fund equity holdings and representative indices (CITIC Construction, etc.). The benchmark is the high - end manufacturing theme fund index [35]. - Cyclical Stock Fund Selection Index: Selects 5 funds based on the intersection market value ratio of fund equity holdings and representative indices (CITIC Petrochemical, etc.). The benchmark is the cyclical theme fund index [39]. 3.3.4 Money - enhancing Index - Money - enhancing Strategy Index: Aims for liquidity management, pursues a curve that exceeds money - market funds. It mainly allocates money - market funds and inter - bank certificate of deposit index funds. The benchmark is the CSI Money - market Fund Index [42]. 3.3.5 Pure - Bond Index - Short - term Bond Fund Selection Index: Aims for liquidity management, ensures drawdown control, and pursues a smooth - upward curve. It selects 5 funds with stable long - term returns, strict drawdown control, and significant absolute - return ability. The benchmark is 50% * Short - term Pure - Bond Fund Index+50% * Ordinary Money - market Fund Index [44]. - Medium - and Long - term Bond Fund Selection Index: Invests in medium - and long - term pure - bond funds, pursues stable returns while controlling drawdowns. It selects 5 funds each period, balancing coupon strategies and band - trading operations, and adjusts the ratio of credit - bond funds and interest - rate - bond funds according to market conditions [47]. 3.3.6 Fixed - income + Index - Low - volatility Fixed - income + Fund Selection Index: The equity central position is 10%, selects 10 funds with an equity central position within 15% in the past three years and recently. The benchmark is 10% CSI 800 Index+90% ChinaBond New Composite Full - price Index [51]. - Medium - volatility Fixed - income + Fund Selection Index: The equity central position is 20%, selects 5 funds with an equity central position between 15% - 25% in the past three years and recently. The benchmark is 20% CSI 800 Index+80% ChinaBond New Composite Full - price Index [51]. - High - volatility Fixed - income + Fund Selection Index: The equity central position is 30%, selects 5 funds with an equity central position between 25% - 35% in the past three years and recently. The benchmark is 30% CSI 800 Index+70% ChinaBond New Composite Full - price Index [55]. 3.3.7 Other Pan - Fixed - Income Index - Convertible Bond Fund Selection Index: Selects 5 funds from a sample space of bond - type funds with a convertible - bond investment ratio of at least 60% in the latest period and at least 80% in the past four quarters, based on an evaluation system [58]. - QDII Bond Fund Selection Index: Selects 6 funds with stable returns and good risk control based on credit and duration conditions [60]. - REITs Fund Selection Index: Selects 10 funds with stable operation, reasonable valuation, and certain elasticity based on the underlying asset type [61].