● 本报记者 张韵 中证稀有金属主题指数选取不超过50家业务涉及稀有金属采矿、冶炼和加工的上市公司证券作为指数样 本，共包括50只成分股。截至收盘，50只成分股全部上涨，包括东方钽业、云南锗业、安宁股份在内的 8只个股涨停。 2月25日，A股市场主要指数集体收红，创业板指单日涨幅超1.4%，全市场ETF涨多跌少。Wind数据显 示，全市场1400余只ETF中，超1000只实现上涨。稀土和稀有金属主题ETF表现尤为突出，多只相关产 品跻身单日涨幅榜前列，稀土主题ETF盘中交易价格更是创下上市以来新高。 业内人士认为，稀土板块的亮眼表现或离不开稀土行业供需关系的改善。供给方面，供给增长滞后于需 求扩张；需求方面，机器人、低空经济等产业快速发展拉动需求释放。海外冶炼成本高企等因素也对价 格形成支撑，稀土战略价值有望迎来重估。 资金流向方面，前一交易日（2月24日），ETF全市场单日获资金净流入超过110亿元。这也是近6个交 易日以来，单日净流入规模最高的一天。主投港股市场的ETF吸金势头较强，多只产品单日资金净流入 额均超10亿元。 与稀土和稀有金属板块ETF的强势行情形成对照的是，能源类ETF出现一定回调。2只标 ...

Market Performance Overview - In the week from February 2 to February 6, 2025, gold prices increased while domestic equity market indices experienced fluctuations downward [4] - The food and beverage, beauty care, and power equipment sectors showed the highest gains, while non-ferrous metals, communication, and electronics sectors faced the largest declines [4] Fund Product Issuance - A total of 40 new funds were established in the domestic market this week, with a combined issuance of 30.859 billion units [5] - The breakdown of new funds includes 9 FOF funds, 16 equity funds, 7 bond funds, and 8 mixed funds [5] - Across the entire market, 33 new funds were issued, comprising 14 equity funds, 7 mixed funds, 6 FOF funds, and 6 bond funds [5] Fund Product Performance Tracking - Long-term thematic fund indices showed that consumer and new energy thematic funds increased in net value, while other thematic funds performed poorly, with TMT thematic funds experiencing significant declines [6] - As of February 6, 2026, the net value changes for various thematic funds were as follows: consumer (+0.94%), new energy (+0.38%), financial real estate (-0.03%), pharmaceuticals (-0.61%), national defense and military (-1.37%), industry rotation (-2.23%), industry balance (-2.56%), cyclical (-4.60%), and TMT (-5.74%) [6] ETF Market Tracking - This week, the pace of profit-taking in equity ETFs slowed, with a total outflow of 24.3 billion yuan from small and large-cap thematic ETFs, while Hong Kong stock ETFs saw a net inflow exceeding 10 billion yuan [7] - The median return for equity ETFs was -1.75%, with a net outflow of 7.801 billion yuan [7] - Hong Kong stock ETFs had a median return of -2.12% and a net inflow of 18.493 billion yuan, while cross-border ETFs had a median return of -2.51% with a net inflow of 3.210 billion yuan [7] - Commodity ETFs recorded a median return of -6.07% and a net outflow of 2.887 billion yuan [7] Broad-based ETF Insights - The week saw significant net inflows into the Sci-Tech Innovation Board thematic ETFs, totaling 5.507 billion yuan [8] - TMT thematic ETFs also experienced notable net inflows, amounting to 9.964 billion yuan [8] ESG Financial Product Tracking - This week, 21 new green bonds were issued, with a total issuance scale of 20.191 billion yuan [9] - The domestic green bond market has steadily developed, with a cumulative issuance scale of 5.26 trillion yuan and a total of 4,548 bonds issued as of February 6, 2026 [9] - The existing ESG funds in the domestic market total 211, with a combined scale of 156.021 billion yuan [9] - In terms of fund performance, the median net value changes for active equity, passive stock index, and bond ESG funds were -1.15%, -0.84%, and +0.05%, respectively, with low-carbon economy, clean energy, and carbon neutrality thematic funds performing well [9]

Core Insights - The cross-border ETF market has shown significant growth, with total assets nearing 10 trillion yuan, reflecting a compound annual growth rate of over 70% compared to three years ago [2][3][6] - Hong Kong ETFs dominate the cross-border ETF landscape, accounting for over 70% of the total market size, which has increased more than fivefold in three years [4][5][6] Market Growth - As of February 6, the total size of cross-border ETFs reached 983.8 billion yuan, with 212 products available, compared to 203.2 billion yuan and 106 products three years ago [2][3] - The market experienced fluctuations, initially surpassing 1 trillion yuan in January 2026 before declining due to market volatility [2] Product Performance - There has been a notable increase in large cross-border ETFs, with 25 products exceeding 10 billion yuan in size, up from 6 three years ago [3] - The top-performing ETFs include the Fortune Fund's Hong Kong Internet ETF, which saw a return of 19.87% over the past year, and the Huaxia Fund's Hang Seng Technology Index ETF, with a return of 10.5% [3][4] Market Composition - Hong Kong ETFs are the most significant segment, with over 7 trillion yuan in assets, while U.S. ETFs linked to indices like the Nasdaq 100 and S&P 500 also show substantial growth [4][5] - Emerging markets are represented as well, with ETFs tracking indices in Saudi Arabia and Brazil gaining traction [5] Institutional Outlook - Analysts are optimistic about the long-term potential of cross-border ETFs, driven by factors such as U.S. interest rate cuts and the recovery of the Chinese economy [6][7] - The cross-border ETF market is seen as a bridge for domestic investors to access global markets, allowing for diversified investment opportunities [6][7]

Quantitative Models and Factors Summary Quantitative Models and Construction Methods Model Name: Volume Timing Model - **Model Construction Idea**: The model uses volume signals to determine market timing[12] - **Model Construction Process**: - The model evaluates the volume timing signals for major indices as of February 6, 2026, and maintains a cautious view[24] - **Model Evaluation**: The model is currently signaling a cautious outlook for all major indices[24] Model Name: Momentum Sentiment Indicator - **Model Construction Idea**: The model uses the number of stocks with positive returns within an index to gauge market sentiment[24] - **Model Construction Process**: - Calculate the proportion of stocks in the CSI 300 index with positive returns over the past N days - The formula is: $ \text{CSI 300 Index N-day Upward Stock Proportion} = \frac{\text{Number of stocks with positive returns in the past N days}}{\text{Total number of stocks in the index}} $[24] - **Model Evaluation**: The indicator can quickly capture upward opportunities but may miss out on gains during sustained market exuberance and has limitations in predicting downturns[25] Model Name: Moving Average Sentiment Indicator - **Model Construction Idea**: The model uses the eight moving average system to determine the trend state of the CSI 300 index[32] - **Model Construction Process**: - Calculate the eight moving average values for the CSI 300 index closing prices with parameters 8, 13, 21, 34, 55, 89, 144, 233 - Assign values to the moving average indicator based on the moving average interval values - The formula is: $ \text{Indicator Value} = \begin{cases} -1 & \text{if interval value is 1/2/3} \\ 0 & \text{if interval value is 4/5/6} \\ 1 & \text{if interval value is 7/8/9} \end{cases} $[32] - **Model Evaluation**: The recent CSI 300 index is in a non-prosperous sentiment interval[32] Model Backtesting Results Volume Timing Model - **Signal**: Cautious for all major indices[24] Momentum Sentiment Indicator - **Current Value**: The indicator is above 60%, indicating high market sentiment[25] Moving Average Sentiment Indicator - **Current Value**: The CSI 300 index is in a non-prosperous sentiment interval[32] Quantitative Factors and Construction Methods Factor Name: Cross-sectional Volatility - **Factor Construction Idea**: The factor measures the cross-sectional volatility of index constituent stocks to assess the Alpha environment[36] - **Factor Construction Process**: - Calculate the cross-sectional volatility for the CSI 300, CSI 500, and CSI 1000 index constituent stocks - The formula is: $ \text{Cross-sectional Volatility} = \sqrt{\frac{1}{N-1} \sum_{i=1}^{N} (R_i - \bar{R})^2} $ where $ R_i $ is the return of stock i, and $ \bar{R} $ is the average return[37] - **Factor Evaluation**: The short-term Alpha environment has deteriorated, but the quarterly view shows a good Alpha environment for the CSI 300 and CSI 1000 indices[36] Factor Name: Time-series Volatility - **Factor Construction Idea**: The factor measures the time-series volatility of index constituent stocks to assess the Alpha environment[37] - **Factor Construction Process**: - Calculate the time-series volatility for the CSI 300, CSI 500, and CSI 1000 index constituent stocks - The formula is: $ \text{Time-series Volatility} = \sqrt{\frac{1}{T-1} \sum_{t=1}^{T} (R_t - \bar{R})^2} $ where $ R_t $ is the return at time t, and $ \bar{R} $ is the average return[40] - **Factor Evaluation**: The recent week shows an improvement in the Alpha environment for all indices[37] Factor Backtesting Results Cross-sectional Volatility - **CSI 300**: - Last quarter average: 2.17% - Last quarter percentile (2 years): 70.99% - Last quarter percentile (1 year): 74.07% - Last quarter percentile (6 months): 65.64%[37] - **CSI 500**: - Last quarter average: 2.48% - Last quarter percentile (2 years): 48.41% - Last quarter percentile (1 year): 53.97% - Last quarter percentile (6 months): 56.35%[37] - **CSI 1000**: - Last quarter average: 2.63% - Last quarter percentile (2 years): 66.53% - Last quarter percentile (1 year): 68.92% - Last quarter percentile (6 months): 66.14%[37] Time-series Volatility - **CSI 300**: - Last quarter average: 0.96% - Last quarter percentile (2 years): 58.02% - Last quarter percentile (1 year): 60.91% - Last quarter percentile (6 months): 47.94%[40] - **CSI 500**: - Last quarter average: 1.27% - Last quarter percentile (2 years): 50.00% - Last quarter percentile (1 year): 57.94% - Last quarter percentile (6 months): 60.32%[40] - **CSI 1000**: - Last quarter average: 1.22% - Last quarter percentile (2 years): 63.35% - Last quarter percentile (1 year): 71.31% - Last quarter percentile (6 months): 66.93%[40]

Group 1 - The stock ETF market experienced a net outflow of 790 billion yuan in January, with broad-based ETFs being the main contributors to the outflow [1] - In February, the trend of capital outflow continued, with a single-day net outflow of 13.771 billion yuan on the first trading day, influenced by significant declines in the three major stock indices [1] - Broad-based ETFs and the metals sector were the largest "blood loss" categories, while sector-specific ETFs like semiconductors and pharmaceuticals attracted significant inflows [1][2] Group 2 - As of February 2, the total scale of 1,321 stock ETFs (including cross-border ETFs) was 4.09 trillion yuan, showing a notable decrease due to market declines [2] - Sector-specific ETFs and Hong Kong stock ETFs saw the largest inflows, with 3.715 billion yuan and 3.346 billion yuan respectively on February 2 [2] - The semiconductor sector had a remarkable net inflow of 2.61 billion yuan on February 2, with the Guolian An CSI All-Share Semiconductor ETF leading with a net inflow of 903 million yuan [2] Group 3 - The broad-based ETF sector saw a significant net outflow of 23.778 billion yuan on the previous day, with a total scale decrease of 68.672 billion yuan [5] - The CSI 500 ETF had the largest single-day net outflow of 13.02 billion yuan, followed by the CSI 300 ETF with 7.2 billion yuan [5] - The metals sector also experienced a notable net outflow of 4.39 billion yuan, influenced by market sentiment and short-term profit-taking [6] Group 4 - On February 2, the top inflow ETFs included the Fortune CSI 300 ETF with a net inflow of 903 million yuan and the Guolian An CSI All-Share Semiconductor ETF with 744 million yuan [3][7] - The Huatai-PineBridge CSI Dividend ETF also saw a significant inflow of 741 million yuan, indicating strong investor interest in dividend-related investments [3] - The top inflow for the Hong Kong technology sector ETFs included the Huatai-PineBridge Hang Seng Technology ETF with a net inflow of 715 million yuan [4]

Core Viewpoint - The report indicates a significant improvement in investor sentiment, with nearly 80% of investors optimistic about the 2026 market, driven by a strong performance in 2025 and expectations for continued growth in technology sectors, particularly AI and chips [1][7][22]. Group 1: Market Performance and Investor Sentiment - In 2025, major stock indices in A-shares showed strong performance, with the Shanghai Composite Index rising nearly 20%, marking its best annual performance in six years [7][30]. - Approximately 57% of surveyed investors reported profits in 2025, a notable increase from previous years, with a 15 percentage point rise from 2024 [8][32]. - The average asset allocation in securities accounts increased to 41.68% by the end of 2025, reflecting a shift of funds from savings to equity investments [10][34]. Group 2: Sector Performance - The technology sector, particularly AI and chips, was identified as the primary source of investment returns, with 26% of investors citing it as their top-performing sector [3][42]. - Other notable sectors included the new energy industry at 24% and cyclical stocks at 18%, while traditional consumer sectors lagged behind [3][42]. Group 3: Future Expectations - Looking ahead to 2026, 78% of investors expect the market to rise, with 47% anticipating gains of over 5% [24][49]. - Investors are particularly optimistic about the technology sector, with 39% believing that tech stocks will continue to outperform [24][49]. - A significant 81% of investors are optimistic about the spring market in 2026, with a focus on technology growth [51]. Group 4: Investment Strategies - The report highlights a trend of increasing allocations to equity assets, with 42% of investors planning to increase their investments in stocks [48][35]. - There is a notable interest in gold investments, with 71% of investors having allocated funds to gold, reflecting its status as a safe haven amid market volatility [37][13]. - The preference for indirect investment methods, such as ETFs, is growing, particularly in the Hong Kong stock market, where 58% of investors chose this route [40][39]. Group 5: Economic Factors - The report notes that the decline in risk-free interest rates has prompted a shift in investment strategies, with more investors considering equities over traditional savings [11][36]. - Expectations for liquidity in the market remain high, with over 60% of investors anticipating a continued influx of capital into equities [48][22].

| 4). 4300点附近 | 16% | | --- | --- | | 5). 4400点附近 | 22% | | 6). 4500点及以上 | 10% | （感谢申万宏源证券、东北证券相关营业部对本调查的支持。上图为部分调查结果） □ 伴随着A股主要指数在2025年全线收红，近六成受访投资者实现盈利。其中，以人工智能为代表的核 心热点板块在2025年持续上涨，成为贡献投资收益的主要来源 □ 在无风险利率持续下行的背景下，随着股市赚钱效应不断增强，居民存款向权益资产"搬家"的现象在 2025年初现端倪 □ 近八成投资者看涨2026年股市，并且对春季行情充满期待。值得一提的是，投资者对今年上证综指波 动范围的预期"乐观但不激进"，倾向于在指数稳健运行的背景下，把握结构性机会而非博弈指数大幅突 破 ◎记者 汪友若 投资收益连续两年上升 纵观2025年全年，主要宽基股指均在当年4月初触底后一路高歌猛进。上证综指从年内低位的3040.69点 起步，一度冲破4000点大关，全年涨幅接近20%，创下近六年来最佳年度表现；科技含量更高的创业板 指和科创综指全年涨幅更是接近50%。 市场行情的向好直接惠及广大投资者，近六 ...

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Model - **Model Construction Idea**: The model uses volume-based timing signals to assess market sentiment and provide trading signals[23] - **Model Construction Process**: - The model evaluates the volume timing signals of major broad-based indices - Signals are categorized as "cautious" or "optimistic" based on volume trends - As of January 30, 2026, all major indices (e.g., SSE Composite Index, CSI 300, etc.) showed "cautious" volume timing signals[24] - **Model Evaluation**: The model provides a straightforward approach to gauge market sentiment but may lack granularity in capturing nuanced market dynamics[23][24] 2. Model Name: Momentum Sentiment Indicator - **Model Construction Idea**: This model identifies market sentiment by analyzing the proportion of stocks with positive returns in the CSI 300 Index over a specific period[24] - **Model Construction Process**: - The indicator is calculated as: $ \text{CSI 300 N-day Upward Stock Proportion} = \frac{\text{Number of CSI 300 stocks with positive returns over N days}}{\text{Total number of CSI 300 stocks}} $ - The indicator is smoothed using two moving averages with different window periods (N1 = 50, N2 = 35) to create a "fast line" and a "slow line" - A buy signal is generated when the fast line exceeds the slow line, and a neutral signal is generated when the fast line falls below the slow line[26][28] - **Model Evaluation**: The indicator is effective in capturing upward market opportunities but may fail to predict downturns accurately. It also tends to miss gains during prolonged market exuberance[25] 3. Model Name: Moving Average Sentiment Indicator - **Model Construction Idea**: This model uses an eight-moving-average system to assess the trend state of the CSI 300 Index and generate trading signals[32] - **Model Construction Process**: - Calculate the eight moving averages of the CSI 300 Index closing price with parameters: 8, 13, 21, 34, 55, 89, 144, and 233 - Assign values to the indicator based on the number of moving averages the current price exceeds: - If the price exceeds more than five moving averages, the sentiment is bullish - Generate a buy signal when the current price exceeds five moving averages[36] - **Model Evaluation**: The model provides a clear framework for trend analysis but may oversimplify complex market dynamics[36] --- Model Backtesting Results 1. Volume Timing Model - All major indices (e.g., SSE Composite Index, CSI 300, CSI 500, etc.) showed "cautious" volume timing signals as of January 30, 2026[24] 2. Momentum Sentiment Indicator - The CSI 300 N-day upward stock proportion indicator was above 60% as of January 30, 2026, indicating high market sentiment[25] - The fast line was above the slow line, suggesting a bullish outlook for the CSI 300 Index[26] 3. Moving Average Sentiment Indicator - The CSI 300 Index was in a "sentiment prosperity zone" as of January 30, 2026, indicating a bullish sentiment[36] --- Quantitative Factors and Construction Methods 1. Factor Name: Cross-sectional Volatility - **Factor Construction Idea**: Measures the dispersion of returns among index constituents to assess the Alpha environment[37] - **Factor Construction Process**: - Calculate the cross-sectional volatility of index constituents (e.g., CSI 300, CSI 500, CSI 1000) - Compare the recent quarter's average volatility to historical percentiles to evaluate the Alpha environment[38] - **Factor Evaluation**: The factor effectively captures short-term Alpha opportunities but may not fully reflect long-term trends[37] 2. Factor Name: Time-series Volatility - **Factor Construction Idea**: Measures the volatility of index constituents over time to assess the Alpha environment[38] - **Factor Construction Process**: - Calculate the time-series volatility of index constituents (e.g., CSI 300, CSI 500, CSI 1000) - Compare the recent quarter's average volatility to historical percentiles to evaluate the Alpha environment[41] - **Factor Evaluation**: The factor provides insights into market stability but may be less effective in highly volatile markets[38] --- Factor Backtesting Results 1. Cross-sectional Volatility - CSI 300: Recent quarter average volatility at 2.14%, in the 69.55th percentile of the past two years[38] - CSI 500: Recent quarter average volatility at 2.45%, in the 50.79th percentile of the past two years[38] - CSI 1000: Recent quarter average volatility at 2.61%, in the 66.93rd percentile of the past two years[38] 2. Time-series Volatility - CSI 300: Recent quarter average volatility at 0.96%, in the 57.20th percentile of the past two years[41] - CSI 500: Recent quarter average volatility at 1.22%, in the 50.79th percentile of the past two years[41] - CSI 1000: Recent quarter average volatility at 1.17%, in the 64.94th percentile of the past two years[41]

2026年开年，资金借道跨境ETF持续向港股科技板块聚集。 Wind数据显示，2026年1月以来，超160只港股ETF（仅统计跨境ETF，下同）合计获得283.89亿元净流入。其中，约九成资金流入了港股通互 联网ETF、恒生科技ETF等科技主题产品。 同时，净申购增量及基金净值增长共同推动港股ETF的规模扩容提速：截至2026年1月29日，其总规模逼近8000亿元，较2025年底增加了近790 亿元。 从产品端看，近期，港股科技主题基金产品有望加速"上新"。 据21世纪经济报道记者粗略统计，自2026年开年以来，公募基金管理人至少已上报了28只港股主题基金（尚待批复），投资方向覆盖了港股科 技、医药、红利、消费等多个赛道。而科技主题基金依旧占据C位。 科技ETF持续"吸金" 经历一段时间的疲软之后，港股各行业板块在2026年初有所反弹。资金流入港股ETF的趋势也仍在延续。 据Wind统计，截至2026年1月29日，开年以来，160多只港股ETF共计获得283.89亿元净流入。 其中，共有10只产品的"吸金"规模达到10亿元以上，分别是：广发港股通非银ETF、富国港股通互联网ETF、恒生科技ETF天弘、华泰柏瑞 ...

Core Viewpoint - The Hong Kong stock market is experiencing significant inflows into technology-themed ETFs, with over 160 ETFs attracting a net inflow of 28.389 billion yuan since the beginning of 2026, primarily driven by interest in internet and technology sectors [1][3][4]. Fund Inflows - As of January 29, 2026, more than 160 Hong Kong ETFs have collectively received a net inflow of 28.389 billion yuan, with 10 products attracting over 1 billion yuan each [3][4]. - The top 10 ETFs by net inflow include several technology-focused funds, indicating a strong preference for this sector among investors [3][4]. ETF Performance - Technology-themed ETFs have been the main contributors to inflows, with net inflows of 16.052 billion yuan for technology ETFs and 9.916 billion yuan for internet-themed ETFs, accounting for approximately 90% of total net inflows [4]. - Most Hong Kong ETFs have recorded positive returns since the beginning of 2026, with several funds achieving returns exceeding 10% [7]. Fund Size Growth - The total size of Hong Kong ETFs reached approximately 779.031 billion yuan as of January 29, 2026, marking an increase of nearly 79 billion yuan, or about 11%, since the end of 2025 [8]. New Fund Launches - Public fund managers have reported at least 28 new Hong Kong-themed funds since the beginning of January 2026, focusing primarily on technology and healthcare sectors [9][10][12]. - The new funds include a variety of types such as ETFs, index funds, and mixed funds, with a significant emphasis on technology and healthcare themes [11][12]. Investment Trends - Factors driving investment into the Hong Kong market include corporate profit recovery, lower dynamic P/E ratios in the technology sector compared to the U.S. Nasdaq, and favorable trends in artificial intelligence and innovative pharmaceuticals [6]. - Long-term investment opportunities are seen in technology, upstream resources, and companies with global competitiveness, particularly in the context of AI advancements and industrial upgrades [14][15].