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]

资产配置月报 202602 如何衡量黄金的交易拥挤度？ glmszqdatemark 2026 年 02 月 06 日 如何衡量黄金的交易拥挤度？ 大类资产量化观点 风格量化观点 行业配置量化观点 [Table_Author] | 分析师 | 叶尔乐 | | --- | --- | | 执业证书： S0590525110059 | | | 邮箱： | yeerle@glms.com.cn | | 分析师 | 祝子涵 | | 执业证书： S0590525110061 | | | 邮箱： | zhuzihan@glms.com.cn | 相关研究 本公司具备证券投资咨询业务资格，请务必阅读最后一页免责声明 证券研究报告 1 2026 年 1 月底黄金价格出现大幅下跌，或是短期利空事件触发与市场自身交易 结构脆弱性共振的结果。下跌的诱因或来自凯文沃什被提名美联储主席候选人， 但黄金长期上涨的根本逻辑未发生改变；前期黄金价格持续上涨，黄金交易拥挤 度处于高位，其脆弱的交易结构在利空消息的刺激下导致了这次大幅下跌。 对于黄金的交易拥挤度，我们可以从其价格乖离率和沪金主力平值 IV 来观察。在 黄金长期上涨逻辑不变的情况 ...

资产配置月报 202602 如何衡量黄金的交易拥挤度？ glmszqdatemark 2026 年 02 月 06 日 如何衡量黄金的交易拥挤度？ 大类资产量化观点 风格量化观点 行业配置量化观点 [Table_Author] | 分析师 | 叶尔乐 | | --- | --- | | 执业证书： S0590525110059 | | | 邮箱： | yeerle@glms.com.cn | | 分析师 | 祝子涵 | | 执业证书： S0590525110061 | | | 邮箱： | zhuzihan@glms.com.cn | 相关研究 本公司具备证券投资咨询业务资格，请务必阅读最后一页免责声明 证券研究报告 1 2026 年 1 月底黄金价格出现大幅下跌，或是短期利空事件触发与市场自身交易 结构脆弱性共振的结果。下跌的诱因或来自凯文沃什被提名美联储主席候选人， 但黄金长期上涨的根本逻辑未发生改变；前期黄金价格持续上涨，黄金交易拥挤 度处于高位，其脆弱的交易结构在利空消息的刺激下导致了这次大幅下跌。 对于黄金的交易拥挤度，我们可以从其价格乖离率和沪金主力平值 IV 来观察。在 黄金长期上涨逻辑不变的情况 ...

Core Viewpoint - The technology innovation sector is experiencing a strong rebound, driven by strategic emerging industries such as artificial intelligence, smart manufacturing, and semiconductors, which are propelling index growth [1] Group 1: Market Performance - The ChiNext 50 ETF (159375) rose over 0.2%, reflecting the ongoing strength of the technology innovation theme [1] - The ChiNext 50 Index (399673) tracks the performance of the top 50 securities in the ChiNext market based on average daily trading volume, focusing on well-known, large-cap, and liquid companies [1] Group 2: Industry Drivers - Strategic emerging industries, particularly artificial intelligence, smart manufacturing, and semiconductors, are identified as the core drivers of index performance [1] - The "Artificial Intelligence +" initiative has been included in the 14th Five-Year Plan, indicating accelerated technological breakthroughs and industrial applications [1] Group 3: Investment Implications - The ChiNext 50 Index, representing new economy sectors, is expected to benefit from multiple drivers including technological innovation, industrial upgrades, and regulatory advantages [1] - The constituent stocks of the ChiNext 50 Index are primarily concentrated in high-growth sectors such as power equipment and new energy, pharmaceuticals, and computers, showcasing significant growth potential and good liquidity [1]

Investment Rating - The report does not explicitly state an investment rating for the industry [1] Core Insights - The report highlights that the deep learning factor tracking has shown a cumulative absolute return of 74.91% since its inception, with a relative excess return of 38.96% compared to the benchmark [10] - The sentiment factor tracking indicates that the top five industries with positive sentiment changes are petroleum and petrochemicals, non-ferrous metals, food and beverage, coal, and textiles and apparel [34] - The market valuation tracking shows that the rolling 5-year historical percentiles for major indices are at 91% for CSI 300, 100% for CSI 500, and 100% for CSI 1000, indicating high valuation levels [44] Summary by Sections Deep Learning Factor Tracking - A long-only portfolio was constructed based on the DecompGRU model, with a cumulative absolute return of 74.91% and a maximum drawdown of 10.08% since March 31, 2025 [10] - An ETF rotation portfolio was also created, achieving a cumulative absolute return of 40.08% since March 18, 2025, with a maximum drawdown of 7.82% [13] Sentiment Factor Tracking - The report tracks sentiment across broad indices, with the CSI 300 showing the highest increase in sentiment by 11.05% compared to the previous week [3] - The top five industries with positive sentiment changes include petroleum and petrochemicals, non-ferrous metals, food and beverage, coal, and textiles and apparel [34] Market Valuation Monitoring - The report indicates that several primary industries are currently above the 80% historical percentile for valuations, including electronics, power equipment, light industry manufacturing, and construction materials [46] - Conversely, industries like food and beverage and non-bank financials are below the 20% historical percentile, suggesting potential undervaluation [46] Event Tracking - The report details various corporate events, including stock incentive plans, significant shareholder buybacks, and analyst coverage updates, which may influence market sentiment and stock performance [48][56][57]

Group 1 - The core view of the report emphasizes the importance of "rebalancing" in the investment strategy for 2026, highlighting the dual focus on AI technology, overseas equipment, and global pricing resources as the main consensus among institutional investors [1][2] - The report indicates that the share of technology and overseas sectors in A-share profits (excluding finance) is approaching 40% by Q4 2025, suggesting a significant shift in the profit structure towards high-end technology and manufacturing, which is expected to reshape the A-share profit landscape and drive a new upward cycle in 2026-2027 [1][2] - The report outlines a transition from "new triumphing over old" in 2025 to "new and old dancing together" in 2026, where "new" refers to AI technology moving downstream and "old" refers to traditional industries stabilizing and growing through overseas business [2][3] Group 2 - The report highlights that global pricing resources, particularly gold, are experiencing a shift in asset allocation due to narratives of de-globalization and financialization, with a notable increase in trading sentiment driven by interest rate cuts and a weak dollar [2][3] - It is noted that the pricing of resource commodities is becoming increasingly differentiated, with financial attributes of resource pricing outperforming those based on commodity attributes [2][3] - The report stresses the need to be cautious of the assumption that the dollar will remain weak throughout 2026, as there may be a return to commodity attributes and a decline in financial attributes, making supply-demand fundamentals more critical for resource price increases [3] Group 3 - Observations from Q4 2025 indicate a significant increase in institutional holdings in sectors such as non-ferrous metals, communications, basic chemicals, non-bank financials, and machinery, while reductions were noted in pharmaceuticals, computing, electronics, media, and power equipment [9][10] - The report identifies a divergence in institutional investment in the AI industry chain, with a decrease in holdings in sectors with weaker earnings visibility, while sectors with strong earnings visibility, such as optical modules, saw increases [10][11] - The report also notes that institutional investors are increasingly favoring resource commodities that benefit from price increases, particularly in the non-ferrous and chemical sectors, indicating a strategic shift towards these areas [10][11]

Group 1: A-Share Market Analysis - The A-share market showed a volatile trend this week, with an average daily trading volume of 3.06 trillion, a week-on-week increase of 9.44%[6] - Major indices like the Shanghai 50 and CSI A100 recorded positive returns, while the Shanghai Composite and Shenzhen Component indices experienced declines[6] - Value and large-cap growth sectors performed relatively well, whereas small-cap indices such as CSI 1000 and CSI 2000 saw significant drops[6] - Cyclical sectors like oil, telecommunications, coal, and non-ferrous metals outperformed, while growth sectors including computers and new energy faced larger declines[6] Group 2: Market Insights and Expectations - The A-share market displayed structural characteristics and volatility, with gold stocks experiencing a collective pullback due to fluctuations in international gold prices[6] - AI and technology growth stocks continued to attract capital, as evidenced by the strong performance of the Sci-Tech 50 index, indicating a favorable investment logic in growth sectors[6] - Short-term market conditions are expected to remain structurally volatile, with potential pullback pressures on previously strong cyclical sectors lacking sustained catalysts[7] - The upcoming period post-Spring Festival until the Two Sessions is anticipated to be a more certain upward phase for the market, suggesting strategic positioning opportunities[7] Group 3: Hong Kong Market Analysis - The Hong Kong market performed strongly this week, with the Hang Seng Index rising by 2.38% and the Hang Seng China Enterprises Index increasing by 1.71%[8] - The Hang Seng Technology Index saw a slight decline of 1.38%, indicating volatility within the tech sector[8] - Leading sectors included energy (7.44%), real estate and construction (5.71%), and finance (5.3%), while information technology and healthcare experienced minor declines[8] Group 4: Future Outlook and Risks - The Hong Kong market is expected to continue its structural upward trend, supported by Fed rate cut expectations and improving sentiment in the A-share market[9] - Sustained demand for AI is likely to benefit the tech sector in Hong Kong, although investors should remain cautious of external policy uncertainties[9] - A prudent asset allocation strategy is recommended, focusing on high-dividend assets and sectors with both profitability improvement and growth potential[9] - Risks include unexpected tightening of global liquidity and complexities in market dynamics and policy changes[10]

A-Share Market - The A-share market exhibited a volatile trend this week, with an average daily trading volume of 3.06 trillion, reflecting a week-on-week increase of 9.44% [6] - Major indices showed mixed performance, with large-cap indices like the Shanghai 50 and CSI A100 recording positive returns, while the Shanghai Composite and Shenzhen Component indices experienced declines [6] - Sector performance was diverse, with cyclical and value sectors such as oil, petrochemicals, telecommunications, coal, and non-ferrous metals performing well, while growth sectors like computers, power equipment, new energy, and automobiles faced significant declines [6] - The market displayed structural characteristics and volatility, with precious metals and resource cyclical sectors initially strong but later retreating due to fluctuations in international gold prices, indicating rapid shifts in market sentiment and short-term speculative influences [6][7] - AI and technology growth stocks continued to attract capital, as evidenced by the strong performance of the Sci-Tech 50 index, supporting the investment logic in growth directions [6] Outlook for A-Share Market - The short-term outlook suggests a continuation of structural trends, but increased volatility is anticipated. Cyclical sectors that were previously strong may face correction pressures if lacking sustained catalysts [7] - With the Spring Festival approaching, the period after the festival until the Two Sessions may present a more certain upward trend, suggesting opportunities for strategic positioning post-festival [7] Hong Kong Market - The Hong Kong market showed strong overall performance this week, with major indices rising, including a 2.38% increase in the Hang Seng Index and a 1.71% rise in the Hang Seng China Enterprises Index [8] - The Hang Seng Technology Index experienced a slight decline of 1.38%, indicating volatility within the technology sector [8] - Leading sectors included energy (7.44%), real estate and construction (5.71%), and finance (5.3%), while information technology and healthcare sectors saw slight declines [8] - The market exhibited complex and differentiated characteristics, with a rebound in property stocks due to rising policy expectations, while gold and non-ferrous metal stocks experienced significant fluctuations influenced by international gold price volatility [8] - Despite a slight decline in the Hang Seng Technology Index, certain AI concept stocks like Baidu and Alibaba remained active due to advancements in AI chips, highlighting the sustained appeal of AI as a long-term driver [8] Outlook for Hong Kong Market - The outlook for the Hong Kong market suggests a potential continuation of structural upward trends, supported by expectations of interest rate cuts from the Federal Reserve and a recovery in A-share sentiment [9] - Continued improvement in AI demand is expected to benefit the technology sector in Hong Kong, although investors should remain cautious of external policy uncertainties and consider a prudent allocation strategy, focusing on high-dividend assets and sectors with both profitability improvement and growth potential [9]

Group 1 - The core economic characteristic throughout the year is "production stronger than demand, external demand better than internal demand" with a loose monetary policy maintaining a low interbank interest rate, the lowest since 2020 [1] - Under the macroeconomic weakness and loose liquidity environment, investment in prosperity is favored, suggesting a focus on "technology + resource products" as dual main lines [1] Group 2 - The Guotai ETF (159375) tracks the ChiNext 50 Index (399673), which has a daily price fluctuation limit of 20%, selecting 50 securities with high average daily trading volume from the ChiNext market to reflect the overall performance of well-known, large-cap, and liquid enterprises [1] - The constituent stocks are primarily concentrated in emerging industries such as power equipment and new energy, pharmaceuticals, and computers, exhibiting characteristics of high growth and high liquidity [1]

- The report does not contain any specific quantitative models or factors for analysis[1][2][3]