- The report constructs a "total heat" indicator by aggregating the browsing, self-selection, and click counts of individual stocks, normalized as a percentage of the total market on the same day, and then multiplied by 10,000, with a value range of [0,10000][7] - The "total heat" indicator is used as a proxy for "sentiment heat" to track the attention levels of broad-based indices, industries, and concepts[7] - The broad-based indices are divided into groups: CSI 300, CSI 500, CSI 1000, CSI 2000, and "others," with the total heat indicators of the constituent stocks summed up to obtain the heat of these indices[8] - A simple rotation strategy is constructed based on the weekly heat change rate, buying the broad-based index with the highest heat change rate MA2 at the end of each week, and staying out of the market if the "others" group has the highest change rate[12] - The rotation strategy based on the broad-based heat change rate MA2 has an annualized return of 8.74% since 2017, with a maximum drawdown of 23.5%, and a return of 5.2% in 2026[15] - The weekly heat change rate MA2 for the main broad-based indices shows that the CSI 300 had the highest increase of 3.34% compared to the previous week, while the CSI 500 had the largest decrease of 5.98%[15] - The heat change rate MA2 for the Shenwan first-level industries shows that the oil and petrochemical industry had the highest increase of 58.0% compared to the previous week, while the electronics industry had the largest decrease of -14.1%[26] - The heat change rate MA2 for the Shenwan second-level industries shows that the top five industries with the highest positive change rates are jewelry, planting, liquor II, lighting equipment II, and oil service engineering[26] - The heat change rate for concepts shows that the top five concepts with the highest positive change rates are Huawei Digital Energy, horse racing concept, duty-free shops, Huawei Euler, and pumped storage[27] - Two simple portfolios are constructed: one selects the top 10 stocks with the highest total heat from the top five concepts with the highest heat change rates each week, and the other selects the bottom 10 stocks with the lowest total heat from the same concepts[30] - The historical performance of the portfolios shows that the bottom group can achieve an annualized return of 15.71% with a maximum drawdown of 28.89%[32]

- The report discusses multiple quantitative models for market timing, including short-term, medium-term, and long-term models. These models are constructed based on price-volume, acceleration and trend, momentum, and limit-up/down perspectives. The report emphasizes the importance of combining signals from different models and periods to achieve a balanced strategy[9][11][12] - The short-term models include the "Volume Model" (neutral), "Feature Institutional Model" (neutral), "Feature Volume Model" (bearish), "Smart Algorithm CSI 300 Model" (bullish), and "Smart Algorithm CSI 500 Model" (bearish)[11][70] - Medium-term models include the "Limit-Up/Down Model" (neutral), "Up-Down Return Difference Model" (bullish for some broad-based indices), and "Calendar Effect Model" (bullish)[12][71] - The long-term model is the "Long-Term Momentum Model," which is neutral[72] - Comprehensive models such as the "A-Share Comprehensive Weapon V3 Model" and "A-Share Comprehensive CSI 2000 Model" are neutral[73] - For Hong Kong stocks, the medium-term models include the "Turnover-to-Volatility Model" (bearish), "Hang Seng Index Up-Down Return Difference Model" (neutral), and "Up-Down Return Similarity Model" (bullish)[13][74] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly decline of -0.44%, outperforming the Shanghai Composite Index by 0.83%. Since December 31, 2020, the cumulative return of this pattern is 19.67%, exceeding the Shanghai Composite Index by 2.61%[43][44] - Backtesting results for the "Double-Bottom Pattern" show a weekly decline of -0.88%, outperforming the Shanghai Composite Index by 0.39%. Since December 31, 2020, the cumulative return of this pattern is 23.45%, exceeding the Shanghai Composite Index by 6.39%[43][50]

- The short-term trading volume model is neutral[2][11] - The characteristic institutional model based on the Dragon and Tiger list is neutral[2][11] - The characteristic trading volume model is bearish[2][11] - The intelligent algorithm model for the CSI 300 is bullish[2][11] - The intelligent algorithm model for the CSI 500 is bearish[2][11] - The mid-term limit-up and limit-down model is neutral[2][12] - The mid-term up-down return difference model is bullish for some broad-based indices[2][12] - The mid-term calendar effect model is bullish[2][12] - The long-term momentum model is neutral[2][12] - The comprehensive A-share V3 model is neutral[2][13] - The comprehensive A-share Guozheng 2000 model is neutral[2][13] - The mid-term trading volume to volatility model for Hong Kong stocks is bearish[2][13] - The Hang Seng Index up-down return difference model is neutral[2][13] - The Hang Seng Index up-down return similarity model is bullish[2][13]

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 来源：华金证券 炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 来源：华金证券 春节前A 股短期走势对节后行情可能有一定影响。2010 年以来的16 年中，有9次春节前5 个交易日内上 证综指走强（弱）而节后1 个交易日上证综指下跌（上涨）；有12 次节前5 个交易日内上证综指涨跌与 节后5 个交易日内涨跌同向。 春季行情未完，春节期间风险可能有限，可持股过节。（1）今年春节期间经济和盈利预期可能改善。 一是春节出行、消费数据可能偏好。二是今年春节地产销售可能有所回暖：首先，低基数效应下今年春 节期间地产销售同比增速可能有所回升；其次，各地刺激地产销售的政策预期较强，春节假期期间地产 销售可能延续回暖趋势。（2）春节期间流动性可能维持宽松。一是春节期间宏观流动性可能维持宽 松：首先，海外方面，2 月11 日美国1 月CPI 数据将公布，2 月17 日美国零售数据将披露，预计美元指 数可能继续维持低位震荡，海外对国内流动性宽松的掣肘有限；其次，国内方面，春节前流动性季节性 紧张下央行可能加大净投放力度。二是春节前 ...

1. Report Industry Investment Rating No relevant content provided. 2. Core Views of the Report - Overseas asset fluctuations have been repaired. Although the fourth - quarter reports of tech giants show that cloud - computing revenue and 2026 capex expenditure guidance exceed expectations, market divergence is rising, and the previous structured market is undergoing "destructuring". At least in the first half of 2026, tech growth will maintain its momentum due to factors such as the decrease in the expectation of the Fed's marginal monetary policy easing and the potential IPO of OpenAI in the third or fourth quarter of 2026 [1][37]. - In the domestic equity market, defensive sectors like food and beverage performed well last week, followed by pro - cyclical sectors, while tech growth sectors generally showed high volatility. For convertible bonds, due to the priority of winning rate over odds, high - volatility targets with tech themes and mostly being new - issue targets make it difficult to control drawdowns and increase the difficulty of speculation. Before the holiday, it is recommended to balance risks. High - position funds should actively switch from high - to low - risk assets, and low - position funds should seize the opportunity to invest in targets with clear performance inflection points and high visibility of upward trends in 2026 [1][37][39]. 3. Summary by Relevant Catalogs 3.1. Weekly Market Review 3.1.1. Overall Decline in the Equity Market - From February 2nd to February 6th, the equity market declined overall. The Shanghai Composite Index fell 1.27% to 4065.58 points, the Shenzhen Component Index dropped 2.11% to 13906.73 points, the ChiNext Index decreased 3.28% to 3236.46 points, and the CSI 300 fell 1.33% to 4643.60 points. The average daily trading volume of the two markets decreased by 21.36% week - on - week to 23879.96 billion yuan [6][10]. - Among the 31 Shenwan primary industries, 16 industries closed up, with 3 industries rising more than 2%. Food and beverage, beauty care, power equipment, transportation, and banking led the gains, rising 4.31%, 3.69%, 2.20%, 1.90%, and 1.70% respectively. Non - ferrous metals, communication, electronics, steel, and computer led the losses, with declines of - 8.51%, - 6.95%, - 5.23%, - 3.35%, and - 3.27% respectively [16]. 3.1.2. Overall Rise in the Convertible Bond Market - From February 2nd to February 6th, the CSI Convertible Bond Index rose 0.05% to 520.79 points. Among the 29 Shenwan primary industries, 22 industries closed up, with 2 industries rising more than 2%. Social services, power equipment, transportation, national defense and military industry, and petroleum and petrochemicals led the gains, rising 4.95%, 2.95%, 1.85%, 1.76%, and 1.42% respectively. Computer, electronics, communication, non - bank finance, and non - ferrous metals led the losses, falling 4.85%, 3.06%, 2.22%, 2.13%, and 1.94% respectively [19]. - The average daily trading volume of the convertible bond market was 902.09 billion yuan, a significant decrease of 30.87 billion yuan, with a month - on - month change of - 3.31%. The top ten convertible bonds in terms of trading volume were Shangtai Convertible Bond, Naipu Convertible Bond 02, Dongshi Convertible Bond, Yanpai Convertible Bond, Shuangliang Convertible Bond, Jize Convertible Bond, Yongji Convertible Bond, Jiemei Convertible Bond, Tairui Convertible Bond, and Jialian Convertible Bond. The average trading volume of the top ten convertible bonds reached 116.84 billion yuan, and the trading volume of the top - ranked bond was 335.59 billion yuan [19]. - Approximately 54.71% of individual convertible bonds rose, about 21.73% of them had a gain in the range of 0 - 1%, and 17.54% of them had a gain of more than 2% [19]. - The overall market conversion premium rate increased, with the average daily conversion premium rate this week being 44.31%, a 1.56 - percentage - point increase from last week. By price range, except for the convertible bonds in the price range below 90 yuan, the average daily conversion premium rate quantiles of convertible bonds in other price ranges narrowed. The narrowing amplitude was the largest in the 110 - 120 yuan price range, reaching 30.31 percentage points. By parity range, except for the convertible bonds in the parity range below 90 yuan, the average daily conversion premium rates of convertible bonds in other parity ranges narrowed, with the largest narrowing amplitude of 15.41 percentage points in the 110 - 120 yuan parity range [24]. - In terms of the premium rate changes of each industry, the conversion premium rates of 12 industries widened, with 3 industries having a widening amplitude of more than 2 percentage points. Social services, household appliances, food and beverage, media, and textile and apparel led the widening, with amplitudes of 9.03, 3.54, 2.90, 1.59, and 1.27 percentage points respectively. Building materials, communication, agriculture, forestry, animal husbandry and fishery, non - bank finance, and electronics led the narrowing, with amplitudes of - 14.89, - 14.64, - 5.78, - 4.62, and - 3.81 percentage points respectively [28]. - In terms of conversion parity, the parity of 4 industries increased, with 1 industry having a widening amplitude of more than 2%. Communication, transportation, banking, and social services led the widening, with amplitudes of 16.51%, 1.19%, 0.61%, and 0.13% respectively. Non - bank finance, non - ferrous metals, building materials, automobiles, and electronics led the narrowing, with amplitudes of - 29.31%, - 15.94%, - 13.22%, - 11.74%, and - 10.64% respectively [30]. 3.1.3. Comparison of Stock and Bond Market Sentiments - From February 2nd to February 6th, the weekly weighted average change of the convertible bond market was negative, and the median was positive. The weekly weighted average change of the underlying stock market was positive, and the median was negative. In terms of trading volume, the trading volume of the convertible bond market decreased by 4.05% month - on - month and was at the 82.40% quantile level since 2022. The trading volume of the underlying stock market decreased by 22.67% month - on - month and was at the 88.20% quantile level since 2022. Both the underlying stocks and convertible bonds had a significant reduction in trading volume, and the underlying stock trading volume was at a higher quantile level. In terms of the proportion of rising and falling stocks and bonds, about 60.00% of convertible bonds closed up, and about 43.85% of underlying stocks closed up. About 64.62% of convertible bonds had a larger change than the underlying stocks. In general, the trading sentiment of the convertible bond market was better this week [34]. 3.2. Outlook and Investment Strategy - Overseas asset fluctuations have been repaired. Although the fourth - quarter reports of tech giants show that cloud - computing revenue and 2026 capex expenditure guidance exceed expectations, market divergence is rising, and the previous structured market is undergoing "destructuring". At least in the first half of 2026, tech growth will maintain its momentum due to factors such as the decrease in the expectation of the Fed's marginal monetary policy easing and the potential IPO of OpenAI in the third or fourth quarter of 2026 [1][37]. - In the domestic equity market, defensive sectors like food and beverage performed well last week, followed by pro - cyclical sectors, while tech growth sectors generally showed high volatility. For convertible bonds, due to the priority of winning rate over odds, high - volatility targets with tech themes and mostly being new - issue targets make it difficult to control drawdowns and increase the difficulty of speculation. Before the holiday, it is recommended to balance risks. High - position funds should actively switch from high - to low - risk assets, and low - position funds should seize the opportunity to invest in targets with clear performance inflection points and high visibility of upward trends in 2026 [1][37][39]. - Specific targets recommended for attention include Bo 25, Baolong, Saite, Huitian, Suli, Jianlong, Tairui, Yongjin, Zhongte, Yongxi, Dinglong, Li'ang, Shenglan Convertible Bond 02, Chaosheng, Lihe, Huachen, Tiannai Convertible Bond, etc. [1][39]. - The top ten high - rated, medium - low - priced convertible bonds with the greatest potential for parity premium rate repair next week are Liqun Convertible Bond, Bengang Convertible Bond, Lutai Convertible Bond, Lianchuang Convertible Bond, Xingye Convertible Bond, Yingfeng Convertible Bond, Guotou Convertible Bond, Nenghua Convertible Bond, Qingnong Convertible Bond, and Ziyin Convertible Bond [1][39].

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]

证券研究报告 | 策略专题研究 | 中国策略 策略专题研究 报告日期：2026 年 02 月 05 日 涨价品种出现分化，券商景气度高增 ——行业景气度跟踪报告（2026 年 2 月） 核心观点 上游周期品中，前期涨价品种出现一定程度的分化。从周环比数据看，有色金属中仅 黄金价格出现上涨，白银和其他工业金属出现不同程度的下行，石油石化、基础化工 等细分品种价格亦出现一定回落。双焦价格回暖。TMT 中，半导体销售周期上行行业 景气度不减。下游消费品中，飞天茅台当年散装价格环比上行，支撑白酒走强。金融 地产方面，两市成交额放大，两融余额处于高位，彰显券商景气高增。 ❑ 上游周期 1）有色金属：价格出现分化，comex 黄金价格周环比上行；2）煤炭：煤炭开采 和洗选 PPI 同比增速修复，双焦价格回暖；3）石油石化：石油和天然气开采业 PPI 下行，原油价格承压 ❑ 中游周期 1）钢铁：铁矿石和螺纹钢价格周环比上行；2）基础化工：主要品种价格下行； 3）建筑材料：行业景气度仍处于相对低位；4）交通运输：海运业务走低，快递 业务增速放缓。 ❑ 中游制造 1）轻工制造：建材家居景气度下行，白卡纸价格处于低位。2）汽车： ...

从时间节点看，2月往往处于春节前后，是全年中市场情绪相对活跃的阶段之一。历史经验显示，在政 策预期、资金回流以及风险偏好回升等多重因素作用下，春季行情往往具备一定延续性。众赢财富通研 究发现，当前市场在经历前期震荡整理后，整体估值压力有所缓解，而成交活跃度与主题投资热度正在 回升，这为结构性机会的展开提供了基础。 在行业分布方面，电子板块依旧是券商配置的"压舱石"。随着算力需求持续扩张、国产替代进程加快以 及产业链景气度改善，相关细分领域的中长期逻辑并未发生变化。海光信息等公司因其在核心技术和产 业链地位上的优势，获得多家券商同时推荐，反映出机构对科技自主可控与高端制造方向的持续看好。 众赢财富通观察发现，电子板块内部的分化正在加剧，资金更倾向于流向业绩确定性相对较高、具备产 业趋势支撑的细分龙头。 机械设备板块在2月金股中同样占据重要位置。该板块一方面受益于制造业升级与设备更新需求，另一 方面也与算力基础设施、能源开发等投资方向密切相关。部分券商指出，机械设备企业订单可见度较 高、盈利修复节奏相对明确，在市场风格切换阶段具备较好的配置价值。众赢财富通认为，在当前宏观 环境下，兼具成长属性与一定周期弹性的装备 ...

Fund Size Statistics - The top three equity ETF types by fund size change are: Scale Index ETF (¥15.406 billion), Cross-border Industry Index ETF (¥6.624 billion), and Strategy Index ETF (¥5.384 billion) [9] - The bottom three equity ETF types by fund size change are: Theme Index ETF (-¥26.004 billion), Cross-border Scale Index ETF (-¥1.807 billion), and Cross-border Theme Index ETF (¥0.203 billion) [9] - The top three equity ETF products by fund size change are: CSI 500 ETF (¥2.832 billion), Chemical ETF (¥2.386 billion), and HuShen 300 ETF (¥2.229 billion) [9] - The bottom three equity ETF products by fund size change are: Communication ETF (-¥30.885 billion), Non-ferrous Metals ETF (-¥3.932 billion), and Gold Stock ETF (-¥2.963 billion) [13] Market Outlook - The macro timing model for February 2026 has a score of 0, indicating a historical 78.57% probability of the full A index rising in the following month, with an average increase of 3.37% [23] - A-shares are expected to experience a short-term volatile market, influenced by liquidity from overseas markets and the recent AI bubble discussions affecting tech growth stocks [23] - The recommendation is to adopt a balanced ETF allocation strategy due to the anticipated short-term fluctuations in the market [60]

Core Insights - Guangdong's goods trade import and export reached 9.49 trillion yuan in 2025, marking a 4.4% year-on-year increase and setting a historical record, accounting for 20.9% of the national foreign trade total, contributing 24.1% to national foreign trade growth [2] Group 1: Export Structure and Growth - The export structure in Guangdong is shifting towards new and superior products, with new productive forces accelerating growth. In 2025, Guangdong exported 4.15 trillion yuan in electromechanical products, a 7.3% increase, making up 68.7% of total exports, up 3.1 percentage points year-on-year [3] - Exports of electronic components, electrical equipment, and computers grew by 20%, 16.8%, and 9.9% respectively, while green and intelligent products like drones, 3D printers, and industrial robots saw export growth rates of 40.9%, 37.1%, and 33.9% [3] Group 2: Company Performance and Market Expansion - Dongguan's Songling Technology Co., Ltd. reported a 52% increase in orders in 2025 compared to 2024, with an expected stable growth rate of 30% to 40% for the current year. The company exported industrial robots worth 460 million yuan, a year-on-year increase of over 30% [4] - The Guangdong Customs has been actively guiding enterprises in technical trade responses and intellectual property protection, facilitating the expansion of Dongguan's smart manufacturing into global markets [4] Group 3: New Trade Dynamics and Support Measures - The Guangdong Customs has focused on cultivating new trade dynamics and creating a "new engine" for foreign trade, with electric vehicle exports increasing 31 times during the 14th Five-Year Plan period, reaching 153 countries and regions [5] - The number of enterprises with import and export performance in Guangdong reached 172,000 in 2025, a 17.6% increase, with private enterprises accounting for 148,000, up 20% [6] Group 4: International Cooperation and Market Diversification - Guangdong's foreign trade market is becoming more diversified, with imports and exports to ASEAN, Hong Kong, and the EU each surpassing 1 trillion yuan in 2025, growing by 5.8%, 12.5%, and 8.4% respectively [8] - The province's exports to emerging markets in Central Asia, Africa, and the Middle East grew by 23.6%, 10.7%, and 8.5%, all exceeding the overall growth rate of Guangdong [8] Group 5: Infrastructure and Regulatory Improvements - Guangdong has implemented 166 measures to promote cross-border trade facilitation, achieving 24/7 appointment customs clearance at key ports and expanding shipping routes to 73 [6] - The provincial government has introduced 108 measures to optimize the business environment, with general trade growing at an average of 9.9% annually, increasing its share from 51.2% to 58.2% [7]