01 涨跌速览 本周 股市场整体承压运行，主要指数普遍收跌，结构性分化特征进一步加剧。 数据显示，全市场 41 个主要指数中仅有 2 个实现周度上涨，其余指数 不同程度下行。科创 50 、创业板指跌幅居前，大盘价值风格相对稳健，小微盘股跌势有所放缓，而中盘股成为本轮调整的重点区域。 从市场整体表现看，本周并未出现系统性恐慌性下跌，但风险偏好持续回落，节前资金谨慎特征显著。 行业层面，本周 A 股延续明显轮动特征。 日常消费、工业等防御性和早周期板块表现相对占优，成为资金阶段性避险的重要方向；与此同时，信息技 术、材料等前期涨幅较大的成长板块出现明显回调，科技与资源类板块调整压力集中释放。 个股层面，周涨幅居前的标的以超跌小盘股和部分消费防御类个股为主 ；而周跌幅榜中，贵金属、科技板块个股集中回调，此前高估值、高弹性方向的 去风险过程仍在延续。 户 外 缩量缓跌、情绪筑底, 节前市场进入防守与观望阶段 行业轮动:消费防御领涨, 成长板块深度回调体调整 本周A股行业轮动日常消费、工业等防御性与早周期板 块领涨,信息技术、材料等前期热门成长板块大幅回调。 行业周涨跌幅 (%) 2026.02.02 - 2026.0 ...

资产配置 | 动态跟踪 风险偏好主导资产表现，权益关注风格切 换 ——资产配置月报 202602 研究结论 风险提示 报告发布日期 2026 年 02 月 04 日 | 郑月灵 | 执业证书编号：S0860525120003 | | --- | --- | | | zhengyueling@orientsec.com.cn | | 021-63326320 | | | 周仕盈 | 执业证书编号：S0860125060012 | | | zhoushiying@orientsec.com.cn | | 021-63326320 | | | 提 名 沃 什 不 改 美 元 信 用 弱 化 格 局 ： | 2026-02-03 | | --- | --- | | 20260202 多资产配置周报 | | | 预期的变化利好中盘蓝筹：20260126A 股 | 2026-01-28 | | 风格及行业配置周报 | | | 以对冲配置思路应对美股/黄金"畏高" | 2026-01-19 | | 配置关注权益商品，行业聚焦中盘蓝筹： | 2026-01-04 | | ——资产配置月报 202601 | | 有关分析师的申 ...

- The report introduces a **style rotation model**, which includes the **value-growth rotation strategy** and the **large-small cap rotation strategy**. The construction idea is based on macroeconomic data, supplemented by market sentiment indicators to depict market risk preferences and crowding levels. The model uses a three-dimensional multi-factor scoring system to build a comprehensive style rotation scoring framework[6][9][14] - The **value-growth rotation strategy** scores higher for the value style, with a comprehensive score of 4 as of January 31, 2026. In January 2025, the strategy also scored 4, with the growth index yielding 5.65% and the value index yielding 2.38%[9][11] - The **large-small cap rotation strategy** scores higher for the small-cap style, with a comprehensive score of 2 as of January 31, 2026. In January 2025, the strategy scored 4, with the CSI 300 index yielding 1.65% and the CSI 1000 index yielding 8.68%[11][13] - The report also introduces an **industry rotation model**, constructed using four dimensions: macroeconomic indicators, mid-level fundamental indicators, micro-level technical indicators, and trading crowding indicators. A total of 10 indicators are combined into a scoring system to provide a comprehensive solution for industry index rotation[6][14][30] - The **macroeconomic indicators** divide industries into five sectors: upstream cyclical, midstream manufacturing, downstream consumption, TMT, and large finance. The scoring system is based on second-order differences in macroeconomic growth and liquidity. As of January 31, 2026, the macroeconomic growth dimension was in the "expansion strengthening/recession easing" stage, and the liquidity dimension was in the "easing intensification/tightening mitigation" stage. The recommendation is to allocate to large finance and midstream manufacturing sectors[18][20] - The **fundamental indicators** include historical prosperity, prosperity changes, and prosperity expectations. As of January 31, 2026, the top five industries ranked by fundamental factors are non-bank finance, non-ferrous metals, electronics, automobiles, and communication, while the bottom five are real estate, construction, coal, petroleum and petrochemicals, and agriculture, forestry, animal husbandry, and fishery[21][22] - The **technical indicators** include index momentum, leading stock momentum, and candlestick patterns. As of January 31, 2026, the top five industries ranked by technical factors are communication, media, basic chemicals, non-ferrous metals, and building materials, while the bottom five are construction, electricity and utilities, transportation, real estate, and home appliances[25][28] - The **crowding indicators** include financing inflows, turnover rate, and transaction proportion. As of January 31, 2026, the industries with the highest crowding levels are defense and military, petroleum and petrochemicals, non-ferrous metals, media, and basic chemicals, while the industries with the lowest crowding levels are textiles and apparel, automobiles, transportation, non-bank finance, and banking[26][29] - The **industry rotation comprehensive scoring system** combines positive scores from macroeconomic, fundamental, and technical dimensions while negatively configuring crowding factors. As of January 31, 2026, the top five industries ranked by comprehensive scores are non-bank finance, communication, non-ferrous metals, machinery, and electronics, while the bottom seven are construction, real estate, coal, home appliances, petroleum and petrochemicals, food and beverages, and electricity and utilities[30][32] - The **industry rotation strategy** has demonstrated stable excess returns historically. Since 2017, the strategy has achieved an annualized return of 18.4%, compared to a benchmark annualized return of 4.7%, resulting in an excess annualized return of 13.7%. The monthly IC average is 12.3%[15][16][17] - The **industry rotation strategy performance metrics** for individual years are as follows: - 2017: Excess return 25.6%, IC 28.0, IR 4.42 - 2018: Excess return 4.5%, IC 5.6, IR 0.69 - 2019: Excess return 13.2%, IC 19.6, IR 3.19 - 2020: Excess return 15.0%, IC 18.1, IR 1.85 - 2021: Excess return 25.7%, IC 10.7, IR 1.11 - 2022: Excess return 11.7%, IC 6.7, IR 0.61 - 2023: Excess return 8.9%, IC 10.1, IR 1.77 - 2024: Excess return 4.6%, IC 4.6, IR 0.40 - 2025: Excess return 15.6%, IC 5.7, IR 0.65 - Overall period: Excess return 13.7%, IC 12.3, IR 1.41[16][17]

- The report mentions a **cycle analysis model**, which is used to track market trends and identify potential stabilization signals. The model suggests that the market may have reached a small-scale stabilization point, indicating a shift into a short-term oscillation phase with a clearer oscillation range[2][7] - A **trend model** is also referenced, which remains in a bullish zone on a larger scale. This model supports the inference that the market's short-term movements are constrained within a two-way oscillation pattern[2][7] - The **industry rotation model** is highlighted, showing dispersed signals across various sectors. It identifies opportunities in low-valuation sectors like banking, adjusted sectors like media, and sectors that have been consolidating, such as communication and growth-oriented industries. This model suggests a balanced allocation strategy for the current market environment[7] - The **four-wheel drive industry model** is presented, which provides specific signals for sector opportunities. For example, it identifies potential opportunities in sectors like photovoltaic leaders, communication, and banking, as well as trading opportunities in growth-oriented sectors and media. The model uses signal types such as "potential opportunity" and "trading opportunity" to guide sector allocation[13]

金融工程定期 金融工程研究团队 魏建榕（首席分析师） 证书编号：S0790519120001 傅开波（分析师） 证书编号：S0790520090003 高 鹏（分析师） 证书编号：S0790520090002 苏俊豪（分析师） 证书编号：S0790522020001 胡亮勇（分析师） 证书编号：S0790522030001 王志豪（分析师） 证书编号：S0790522070003 股债配置观点：看多权益资产，最新权益仓位 25% 盛少成（分析师） 证书编号：S0790523060003 蒋 韬（分析师） 证书编号：S0790525070001 资产配置月报（2026 年 2 月） weijianrong@kysec.cn 证书编号：S0790519120001 魏建榕（分析师） 王志豪（分析师） 苏俊豪（分析师） wangzhihao@kysec.cn 证书编号：S0790522070003 sujunhao@kysec.cn 证书编号：S0790522020001 多资产配置观点：看多短债、平衡低估转债，谨慎看多黄金资产 债券久期择时：模型预测未来三个月水平因子上升，斜率因子陡峭化，曲率因 子增凸，推荐持有 ...

Core Conclusions - The A-share market typically exhibits balanced performance during spring rallies, with both growth and value sectors showing gains. In the latter half of bull markets, sector differentiation tends to converge, leading to a more uniform upward trend [1][2] - Recently, previously lagging sectors such as liquor and real estate have performed well, indicating a structural convergence in the market as it enters the latter half of the bull market and spring rally [1][3] - The equity market is expected to remain stable with potential for further upward movement. A balanced allocation strategy is recommended, with a focus on technology represented by AI applications, as well as traditional assets like liquor and real estate, and upstream cyclical sectors [1][3] Historical Context - Historically, during spring rallies since 2005, both growth and value styles have performed similarly, with average maximum gains of 24.0% for growth and 23.5% for value [2][14] - The current market is still within a bull market atmosphere that began in September 2024, with significant structural differentiation observed. The latter half of bull markets typically sees a more balanced performance across sectors [2][18] Market Dynamics - The recent A-share market has shown notable sector rotation, with the performance gap between styles narrowing. Since December 17, 2025, the spring rally has gradually unfolded, supported by broad-based ETFs, flexible foreign capital, and leveraged funds [1][11] - As of January 23, 2026, the industry rotation strength in the A-share market was at a historical low of 18% over the past five years, but there has been a recent uptick, suggesting that structural rotation may be beginning [12][14] Future Outlook - The current spring rally is expected to continue, with historical comparisons indicating a potential index increase of around 20%. The maximum increase of the Shanghai Composite Index since December 17, 2025, has only been 9.8%, indicating room for growth [3][30] - Continued macroeconomic policy support is anticipated to provide a fundamental basis for market growth, with a focus on stabilizing the real estate market as indicated by recent government statements [30][31] Sector Allocation - The technology sector, particularly driven by the AI wave, remains a key focus, with expectations for the rally to expand from hardware to application sectors. Recent developments in AI applications have been notable, suggesting a shift towards practical implementations [31][32] - In addition to technology, traditional value sectors such as undervalued liquor and real estate assets are also recommended for consideration in the current market environment [31][32]

Report Industry Investment Rating - Not provided in the report Core Viewpoints - On January 29, 2026, most industries in the convertible bond market corrected, and the valuation increased month - on - month. The convertible bond market's trading sentiment weakened, and the market style favored large - cap value stocks [2]. Summary by Directory I. Market Main Index Performance - The CSI Convertible Bond Index decreased by 0.70% month - on - month, the Shanghai Composite Index increased by 0.16%, the Shenzhen Component Index decreased by 0.30%, the ChiNext Index decreased by 0.57%, the SSE 50 Index increased by 1.65%, and the CSI 1000 Index decreased by 0.80% [2]. - In terms of market style, large - cap value stocks were relatively dominant. Large - cap growth stocks increased by 0.76%, large - cap value stocks increased by 2.21%, mid - cap growth stocks decreased by 1.02%, mid - cap value stocks increased by 0.50%, small - cap growth stocks decreased by 1.43%, and small - cap value stocks remained unchanged [2]. II. Market Fund Performance - The trading volume of the convertible bond market was 81.418 billion yuan, a month - on - month decrease of 0.06%. The total trading volume of the Wind All - A Index was 325.9418 billion yuan, a month - on - month increase of 8.93%. The net outflow of main funds from the Shanghai and Shenzhen stock markets was 6.0222 billion yuan, and the yield of the 10 - year treasury bond increased by 0.10bp to 1.82% [2]. - The share of Boshi Convertible Bond ETF was 4.311 billion shares, with a net increase of 62.2 million shares; the share of Haifutong Convertible Bond ETF was 891 million shares, with a net decrease of 34.5 million shares [37][40]. III. Convertible Bond Price and Valuation - The weighted average closing price of convertible bonds decreased to 142.82 yuan, a month - on - month decrease of 0.72%. Among them, the closing price of equity - biased convertible bonds was 208.09 yuan, a month - on - month decrease of 1.59%; the closing price of bond - biased convertible bonds was 123.66 yuan, a month - on - month increase of 0.34%; the closing price of balanced convertible bonds was 134.77 yuan, a month - on - month decrease of 0.23% [3]. - The proportion of high - price bonds above 130 yuan decreased by 0.59 pct to 77.87%. The proportion of the 120 - 130 yuan (inclusive) range increased by 0.58 pct to 16.80%. There were no bonds with a closing price below 100 yuan. The median price was 140.54 yuan, a month - on - month decrease of 0.33% [3]. - The conversion premium rate of the 100 - yuan par - value fitting increased to 38.99%, a month - on - month increase of 0.85 pct. The overall weighted par value decreased to 105.83 yuan, a month - on - month decrease of 0.26% [3]. - The premium rate of equity - biased convertible bonds decreased by 0.95 pct to 19.09%; the premium rate of bond - biased convertible bonds increased by 1.19 pct to 91.95%; the premium rate of balanced convertible bonds decreased by 0.77 pct to 29.94% [3]. IV. Industry Performance - In the A - share market, the top three rising industries were food and beverage (+6.57%), media (+3.53%), and real estate (+2.65%); the top three falling industries were electronics (-3.56%), national defense and military industry (-1.79%), and power equipment (-1.78%) [4]. - In the convertible bond market, 21 industries fell. The top three falling industries were steel (-3.94%), electronics (-2.15%), and machinery and equipment (-2.05%); the top three rising industries were non - ferrous metals (+2.06%), communication (+1.27%), and media (+1.15%) [4]. - In terms of closing price, large - cycle industries decreased by 0.66%, manufacturing industries decreased by 1.38%, technology industries decreased by 0.23%, large - consumption industries decreased by 0.17%, and large - finance industries increased by 0.25% [4]. - In terms of conversion premium rate, large - cycle industries decreased by 0.7 pct, manufacturing industries increased by 0.71 pct, technology industries increased by 0.25 pct, large - consumption industries increased by 0.79 pct, and large - finance industries increased by 0.16 pct [4]. - In terms of conversion value, large - cycle industries increased by 0.05%, manufacturing industries decreased by 2.02%, technology industries decreased by 0.53%, large - consumption industries increased by 0.06%, and large - finance industries increased by 1.35% [4]. - In terms of pure bond premium rate, large - cycle industries decreased by 1.0 pct, manufacturing industries decreased by 2.3 pct, technology industries decreased by 0.47 pct, large - consumption industries decreased by 0.23 pct, and large - finance industries increased by 0.28 pct [5]. V. Industry Rotation - Food and beverage, media, and real estate led the rise. The daily increase rates of food and beverage, media, and real estate were 6.57%, 3.53%, and 2.65% respectively in the A - share market [55]. - The report also provided the weekly, monthly, and year - to - date increase rates of various industries, as well as their valuation quantiles such as PE (TTM), 3 - year and 10 - year quantiles of PE and PB (LF) [55].

Investment Rating - The report indicates a favorable outlook for mid-cap blue-chip stocks, particularly in cyclical sectors such as non-ferrous metals and chemicals, as well as manufacturing sectors like engineering machinery [6][16]. Core Insights - The anticipated changes are beneficial for mid-cap blue chips, with recent events catalyzing interest in cyclical industries and manufacturing, aligning with the market's shift towards a more balanced risk appetite [6][16]. - Key sectors to watch include non-ferrous metals, defense and military industry, and machinery equipment, with a noted strengthening trend in these areas [20][27]. Summary by Sections 1. Expected Changes Favoring Mid-Cap Blue Chips - Liquidity expectations have improved, leading to intensified trading activity, driven by rising optimism regarding interest rate cuts following the potential election of BlackRock's Riedel as Fed Chair, with a probability of 54% [9][11]. - Significant price increases in typical petrochemical products such as butadiene rubber, PX, PTA, and ethylene glycol have enhanced profitability expectations for refining companies [11][12]. - China's engineering machinery exports surged to USD 6.417 billion in December 2025, a 27.2% year-on-year increase, supported by more active participation in Belt and Road Initiative projects [12][15]. - The real estate market shows signs of recovery, particularly in major cities like Shanghai and Shenzhen, although the sustainability of this trend remains uncertain [15][27]. 2. Trading Dynamics - The report notes that while the overall market sentiment for large-cap stocks has declined, mid-cap and small-cap stocks exhibit stable short-term sentiment with decreasing medium-term uncertainty, suggesting potential for small-cap stocks to catch up [17][27]. - The report emphasizes the importance of monitoring trading behavior through asset volatility, indicating that changes in volatility reflect shifts in trading sentiment [17][20]. 3. Sector Rotation - The report highlights a strengthening trend in cyclical sectors related to mid-cap blue chips, particularly non-ferrous metals and basic chemicals, while the real estate sector shows weakening reversal signals [20][21][27]. - Short-term sentiment and medium-term uncertainty for non-ferrous metals, defense, and petrochemical sectors are both on the rise, indicating potential investment opportunities [23][26].

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]

Core Viewpoint - The global financial market is at a crossroads of geopolitical turmoil and macroeconomic transformation, with geopolitical risks and economic concerns pushing sovereign bond yields to risky highs, highlighting the role of gold as a core safe-haven asset [1][3] Group 1: Bond Market Dynamics - GTC ZEHUI Capital indicates that the deeper momentum in the global bond market stems from structural changes in the Japanese fixed income market, with rising Japanese bond yields significantly impacting global liquidity [4] - Recent data shows that the U.S. 10-year Treasury yield has fluctuated around 4.269% after reaching a high of 4.3%, which has suppressed investor risk appetite [4] - The high interest rate environment has notably pressured the stock market, causing major indices like the S&P 500 to perform weakly, while gold has strengthened, reaching approximately $4847.58 per ounce and briefly hitting a historical peak of $4888.54 [4] Group 2: Sector Rotation and Market Trends - The market's main theme for 2026 is undergoing a qualitative change, with the previous "Mag 7" era dominated by tech giants giving way to more resilient cyclical sectors such as energy, materials, small-cap stocks, and housing [2][4] - This shift is primarily driven by investors re-evaluating inflation expectations and the recovery of the real economy [2] Group 3: Policy and Economic Outlook - As 2026 is an election year, policy uncertainty and populist tendencies may amplify market volatility, with strong governmental pressure to maintain low interest rates to support livelihoods and housing affordability [5] - GTC ZEHUI Capital suggests that the government may intervene in excessively high market rates through influencing central bank decisions or utilizing government-sponsored entities to purchase bonds [5] - Despite short-term sell-offs driven by yield pressures, these often present long-term investment opportunities, and investors are advised to remain patient and observe policy implementation rhythms [5] Group 4: Future Market Volatility - GTC ZEHUI Capital forecasts that global market volatility will remain high, posing challenges to traditional asset pricing models [3][5] - Investors should focus on structural opportunities in the energy and materials sectors and use gold as a tool to hedge against policy risks, emphasizing the importance of calm observation and prudent positioning in a complex environment [5]