证券研究报告 | 金融工程 gszqdatemark 2026 01 06 年 月 日 量化点评报告 一月配置建议：A 股具备相对优势 资产配置：综合赔率和胜率，A 股仍具备相对优势。 1）A 股：中等赔率-高胜率品种。基于 ERP 和 DRP 的标准化数值等权计 算 A 股赔率，截至 12 月底 A 股赔率下行至-0.2 倍标准差，回归中性水平， 近期中国主权 CDS 指标下行，使得 A 股胜率回升至 19%。 2）债券：中低赔率-中低胜率品种。近期债券赔率大幅回升，但仍处于-0.7 倍标准差的中低水平；债券胜率指标由于 CDS 下行回落至-6%，处于中低 胜率水平。 3）美股：极低赔率-高胜率品种。美股 AIAE 指标当前处于 55%的历史最 高点，位于 2.4 倍标准差水平，回撤风险仍然较高；近期净流动性指标持 续下行，公告意外信号维持中性，其余指标均为宽松信号，流动性指数回 升至 40%的较高水平。 风格配置：小盘和价值风格优势凸显。小盘风格配置价值提升：小盘风格 近期拥挤度大幅消化，呈现出"强趋势-低拥挤"的特征，配置价值有所修 复；价值低波微观打分较高：当前价值和低波风格三标尺综合排名均位居 前列 ...

离岸人民币兑美元升破6.97，最高升至6.9678，创2023年5月以来新高。 | VV | | 美元兑离岸人民币 | | | | --- | --- | --- | --- | --- | | | USDCNH.FX | | | | | 6.97381 | 前收 6.98000 开盘 | | | | | -0.00619 | 菜品 6.97392 -0.09% 买入 | | | | | 最高 | 6.98173 今年来 -0.02% 20 日 | | | | | 最低 | 6.96870 10 日 -0.85% 60 日 | | | | | से स्थि | 五日 日K 月K | | | 白菜 | | 叠加 6.99130 | | | 均价: -- | | | | | | | 20:19 6.9 | | 6.98000 | | | 0.00% | 20.10 40 | | | | | | 20:19 6.9 | | | | | | 20:19 6.9 | | | | | | 20:19 6.9 | | | | | | 20:19 6.9 | | 6.96870 | | | -0.16% 20:19 6.9 ...

来源：兴证策略张启尧团队 引言：近期人民币加速走强，新一轮人民币升值周期启动愈发成为市场共识。为何近期人民币升值加速？后续人民币升值还有哪些支撑，如何影响市场？ 历史上的人民币升值周期，A股与港股如何表现？如何把握新一轮人民币升值周期的配置机会？详见报告： 一、为何近期人民币升值加速？ 近期人民币加速升值引发市场关注。我们认为，背后主要反映的是美元再度走弱叠加年末"结汇潮"带动下，人民币相对其他非美货币的"补涨"，与市场对 人民币升值的一致预期形成共振。 首先，随着11月下旬市场美联储降息预期下修到位、未来宽松指引逐渐明朗，美元再度走弱带动人民币加速升值。10月美国政府停摆对于TGA账户的"抽 水"、叠加数据空窗期下美联储降息表态趋于谨慎，曾一度引发市场流动性预期收紧，进而带动美元指数持续走强，成为当时美元兑人民币汇率继续下行 的掣肘。而11月下旬以来，随着美国政府"开门"后流动性释放、12月美联储降息并开启技术性扩表，显著改善此前偏紧的流动性环境，叠加关键数据验证 以及特朗普对于"影子主席"的表态进一步为后续宽松打开想象空间，美元指数见顶回落、快速下行破98，是近期人民币加速升值的重要推动。 二、后续人民币 ...

中信证券研报称，推动人民币升值的因素逐渐增多，市场关注度也开始升温，投资者要逐步适应在一个 人民币持续升值的环境下去做资产配置。从过去20年间7轮人民币升值周期来看，汇率并不是主导行业 配置的决定性因素。然而，部分行业在持续升值预期形成的初期确实会有更好表现，市场可能会复制这 样的肌肉记忆，同时从成本收入分析来看，约19%的行业会因为升值带来利润率提升，这些行业也会逐 步被投资者重视起来。此外，为抑制过快单边升值趋势而做出的政策应对，反而是影响行业配置的更重 要因素。行业配置上，在人民币持续升值的背景下，可以关注短期肌肉记忆驱动、利润率变化驱动以及 政策变化驱动三条线索，我们在本期聚焦详细梳理了潜在受益行业。 ...

中信证券研报称，推动人民币升值的因素逐渐增多，市场关注度也开始升温，投资者要逐步适应在一个 人民币持续升值的环境下去做资产配置。从过去20年间7轮人民币升值周期来看，汇率并不是主导行业 配置的决定性因素。然而，部分行业在持续升值预期形成的初期确实会有更好表现，市场可能会复制这 样的肌肉记忆，同时从成本收入分析来看，约19%的行业会因为升值带来利润率提升，这些行业也会逐 步被投资者重视起来。此外，为抑制过快单边升值趋势而做出的政策应对，反而是影响行业配置的更重 要因素。行业配置上，在人民币持续升值的背景下，可以关注短期肌肉记忆驱动、利润率变化驱动以及 政策变化驱动三条线索。 ...

人民财讯12月21日电，中信证券研报称，推动人民币升值的因素逐渐增多，市场关注度也开始升温，投 资者要逐步适应在一个人民币持续升值的环境下去做资产配置。从过去20年间7轮人民币升值周期来 看，汇率并不是主导行业配置的决定性因素。然而，部分行业在持续升值预期形成的初期确实会有更好 表现，市场可能会复制这样的肌肉记忆，同时从成本收入分析来看，约19%的行业会因为升值带来利润 率提升，这些行业也会逐步被投资者重视起来。此外，为抑制过快单边升值趋势而做出的政策应对，反 而是影响行业配置的更重要因素。行业配置上，在人民币持续升值的背景下，可以关注短期肌肉记忆驱 动、利润率变化驱动以及政策变化驱动三条线索。 转自：证券时报 ...

Quantitative Models and Construction Methods 1. Model Name: ERP and DRP Standardized Equal-Weight Model for A-Share Odds - **Model Construction Idea**: The model calculates A-share odds using standardized values of ERP (Equity Risk Premium) and DRP (Default Risk Premium) with equal weighting[12] - **Model Construction Process**: - Standardized values of ERP and DRP are calculated - These values are equally weighted to derive the A-share odds - As of the end of November, the A-share odds declined to near the zero axis, indicating a neutral level[12] - **Model Evaluation**: The model reflects a neutral positioning for A-shares, with odds returning to a balanced state[12] 2. Model Name: Bond Odds Indicator - **Model Construction Idea**: The model uses the expected return difference between long-term and short-term bonds to construct a bond odds indicator[19] - **Model Construction Process**: - The expected return difference between long-term and short-term bonds is calculated - This difference is standardized to derive the bond odds indicator - Recently, the bond odds indicator has significantly rebounded but remains at a low level of -0.9 standard deviations[19] - **Model Evaluation**: The model effectively captures the rebound in bond odds, though it remains at a relatively low level[19] 3. Model Name: AIAE Indicator for US Stocks - **Model Construction Idea**: The AIAE (Asset Implied Allocation Efficiency) indicator measures the historical positioning of US stocks to assess risk and return[20] - **Model Construction Process**: - The AIAE indicator is calculated based on historical data - Currently, the AIAE indicator is at 55%, the highest point in its history, corresponding to 2.4 standard deviations above the mean - Historical analysis shows that when the AIAE indicator exceeded 50% in 2000 and 2022, the S&P 500 experienced significant corrections of 46% and 25%, respectively[20] - **Model Evaluation**: The model highlights elevated risks for US stocks, with the AIAE indicator at a historically high level[20] 4. Model Name: Federal Reserve Liquidity Index - **Model Construction Idea**: Combines quantity and price dimensions to construct a liquidity index for asset allocation[20] - **Model Construction Process**: - The index incorporates multiple factors, including net liquidity, Federal Reserve credit support, market expectations, and announcement surprises - After the October FOMC meeting, the announcement surprise signal turned negative, and net liquidity continued to decline - Other indicators showed easing signals, and the liquidity index returned to a moderately high level of 20%[20] - **Model Evaluation**: The model provides a comprehensive view of liquidity conditions, highlighting mixed signals in the current environment[20] --- Model Backtesting Results 1. ERP and DRP Standardized Equal-Weight Model for A-Share Odds - Odds: Neutral (near zero axis)[12] - Win Rate: 16%[12] 2. Bond Odds Indicator - Odds: -0.9 standard deviations (low level)[19] - Win Rate: -4% (medium level)[19] 3. AIAE Indicator for US Stocks - Odds: 2.4 standard deviations (historically high level)[20] - Win Rate: Not explicitly mentioned[20] 4. Federal Reserve Liquidity Index - Liquidity Index: 20% (moderately high level)[20] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Evaluates small-cap stocks based on odds, trends, and crowding levels[21] - **Factor Construction Process**: - Odds: 0.2 standard deviations (neutral level) - Trend: 1.2 standard deviations (high level) - Crowding: -1.4 standard deviations (low level) - Comprehensive score: 4[21] - **Factor Evaluation**: The factor shows strong trends and low crowding, making it attractive for allocation[21] 2. Factor Name: Value Factor - **Factor Construction Idea**: Assesses value stocks using odds, trends, and crowding levels[23] - **Factor Construction Process**: - Odds: 0.8 standard deviations (moderately high level) - Trend: 0.1 standard deviations (neutral level) - Crowding: -1.3 standard deviations (low level) - Comprehensive score: 3[23] - **Factor Evaluation**: The factor ranks high among others, suggesting it is worth focusing on[23] 3. Factor Name: Quality Factor - **Factor Construction Idea**: Evaluates quality stocks based on odds, trends, and crowding levels[26] - **Factor Construction Process**: - Odds: 1.2 standard deviations (high level) - Trend: -0.6 standard deviations (low level) - Crowding: 0.1 standard deviations (medium level) - Comprehensive score: 0[26] - **Factor Evaluation**: The factor's weak trend reduces its allocation value, requiring confirmation of a right-side trend[26] 4. Factor Name: Growth Factor - **Factor Construction Idea**: Analyzes growth stocks using odds, trends, and crowding levels[29] - **Factor Construction Process**: - Odds: 0.1 standard deviations (neutral level) - Trend: 0.5 standard deviations (moderately high level) - Crowding: 1.5 standard deviations (high level) - Comprehensive score: -1.2[29] - **Factor Evaluation**: The factor's high crowding level indicates elevated trading risks[29] --- Factor Backtesting Results 1. Small-Cap Factor - Odds: 0.2 standard deviations[21] - Trend: 1.2 standard deviations[21] - Crowding: -1.4 standard deviations[21] - Comprehensive Score: 4[21] 2. Value Factor - Odds: 0.8 standard deviations[23] - Trend: 0.1 standard deviations[23] - Crowding: -1.3 standard deviations[23] - Comprehensive Score: 3[23] 3. Quality Factor - Odds: 1.2 standard deviations[26] - Trend: -0.6 standard deviations[26] - Crowding: 0.1 standard deviations[26] - Comprehensive Score: 0[26] 4. Growth Factor - Odds: 0.1 standard deviations[29] - Trend: 0.5 standard deviations[29] - Crowding: 1.5 standard deviations[29] - Comprehensive Score: -1.2[29]

Core Viewpoint - The article discusses the current state of the Chinese stock market, highlighting a decrease in trading activity and concentration, while noting an increase in foreign capital inflow into A-shares and Hong Kong stocks [3][4]. Market Pricing Status - Market sentiment has declined, with average daily trading volume dropping to 2 trillion yuan and the average number of daily limit-up stocks decreasing to 68.4 [3] - The proportion of stocks that increased in value has risen to 54.77%, with the median weekly return for all A-shares increasing to 0.6% [3] - Industry trading concentration has decreased, with only one industry (electric power equipment and new energy) having a turnover rate above 95% [3] A-Share Fund Flow - The issuance of new equity funds has decreased to 21.84 billion yuan, with overall stock positions slightly reduced [4] - The private equity confidence index has slightly declined, but positions are nearing the highest levels of the year [4] - Foreign capital inflow reached 800 million USD, with northbound trading accounting for 27.4% of total trading volume [4] - The IPO fundraising for the period was 3.59 billion yuan, with a future lock-up release scale of 24.73 billion yuan [4] - Net buying in margin trading has decreased to 11.63 billion yuan, accounting for 10.8% of total trading volume [4] A-Share Industry Allocation - Foreign capital primarily flowed into the electronics sector, with a net inflow of 6.32 million USD, while the power equipment sector saw a net inflow of 6.83 billion yuan [5] - The non-bank financial sector and pharmaceutical sector saw significant net inflows in ETFs, while the electronics and power equipment sectors experienced net outflows [5] Hong Kong and Global Fund Flow - Southbound capital inflow increased to 38.68 billion yuan, reaching the 89th percentile since 2022 [6] - Global capital flows showed a net outflow from developed markets and a net inflow into emerging markets, with significant inflows into Asian stock markets, particularly in Japan and China [6]

1. Report Industry Investment Rating - Not provided in the content 2. Core View of the Report - In Q3 2025, funds reduced their positions in the Main Board and increased their positions in the Science and Technology Innovation Board and the ChiNext Board. The overall stock market value ratio of four types of active equity funds slightly increased, and the concentration of fund holdings rose. The allocation of leading companies showed differentiation, with a continuous decrease in the allocation of first - tier leaders and a recovery in the allocation of second - and third - tier leaders. Funds significantly increased their allocation to communication and information technology and reduced their allocation to optional consumption, necessary consumption, and finance. Each scale of funds shifted from consumption, financial real estate to TMT [11][14][19][29]. 3. Summary According to the Table of Contents 3.1 Position Slightly Increased, Concentration Declined Again - **Sector Allocation**: In Q3 2025, funds reduced their positions in the Main Board by 5.13 percentage points to 67.39% and increased their positions in the ChiNext Board by 3.84 percentage points to 19.04% compared with Q2 2025. The proportion of Hong Kong stock holdings continued to increase [11]. - **Stock Market Value Ratio**: The overall stock market value ratio of four types of active equity funds slightly increased. The proportion of stocks in the total fund assets increased to 85.62% quarter - on - quarter, while the proportion of bonds decreased to 3.95% quarter - on - quarter, and the cash ratio decreased [14]. - **Concentration of Fund Holdings**: In Q3 2025, the concentration of the top 50 fund holdings reached 44.5%. The profitability of fund heavy - holding stocks was acceptable, with the top 10 stocks significantly outperforming the common equity fund index. The average return of the top 10 heavy - holding stocks in Q3 2025 reached 65.8%, significantly outperforming the 26.4% of the common equity fund index [16][17]. - **Allocation of Leading Companies**: In Q3 2025, the proportion of fund holdings in first - tier/second - and third - tier leading companies decreased by 1.23 and increased by 2.07 percentage points quarter - on - quarter to 25.83% and 15.31% respectively. Funds mainly increased their allocation to leading companies in communication, electric power and new energy, and non - ferrous metals industries, and mainly reduced their allocation to leading companies in household appliances, banking, and food and beverage industries [19]. 3.2 Expansion of Public Fund Scale, Contraction of Share - **Overall Scale and Share**: The overall management scale of public funds expanded rapidly, but the share contracted. The scale of each size of funds increased quarter - on - quarter, but the share growth rate showed differentiation. The position adjustment directions of large and small public funds were relatively consistent, and each scale of funds shifted from consumption, financial real estate to TMT [56][60][70]. 3.3 Reduction in Manufacturing, Consumption, and Cyclical Sectors, Increase in TMT - **Industry Allocation Changes**: In Q3, funds significantly increased their allocation to communication and information technology, with an increase of 5.9pct in the information technology sector and 4.6pct in the communication business sector. They reduced their allocation to optional consumption and necessary consumption sectors by 3.2pct and 2.4pct respectively. In terms of heavy - holding allocation ratio changes, the heavy - holding allocation ratios of electronics, communication, computer, and electric power and new energy increased the most, while the ratios of banking, food and beverage, household appliances, and national defense and military industry decreased the most. In terms of over - allocation ratio levels, electronics, communication, electric power and new energy, and medicine had the highest over - allocation ratios, while banking, non - banking, public utilities, and petroleum and petrochemical were still significantly under - allocated [29][31]. - **Sub - industry Allocation**: At the secondary industry level, the heavy - holding allocation ratios of communication equipment, computer equipment, semiconductors, and components increased significantly in Q3, while those of white goods, regional banks, and liquor decreased significantly [46][49].

- The "Odds + Win Rate Strategy" was constructed by combining the risk budgets of the odds strategy and the win rate strategy, resulting in a comprehensive score. The strategy has achieved an annualized return of 6.8% since 2011, with a maximum drawdown of 2.9%. Since 2014, the annualized return was 7.4%, with a maximum drawdown of 2.3%. From 2019 onwards, the annualized return was 6.5%, with a maximum drawdown of 2.3%[3][46][48] - The "Small Cap Factor" is characterized by medium odds (0.1 standard deviation), strong trend (1.3 standard deviation), and low crowding (-1.1 standard deviation). Its comprehensive score has risen significantly to 3.2, indicating improved allocation value[19][20][21] - The "Value Factor" exhibits high odds (1.0 standard deviation), moderate trend (-0.2 standard deviation), and low crowding (-1.4 standard deviation). Its comprehensive score is 3, suggesting it is relatively favorable compared to other factors[21][23][34] - The "Quality Factor" currently shows high odds (1.2 standard deviation), moderate crowding (-0.2 standard deviation), but weak trend (-1.0 standard deviation). Its comprehensive score is -0.6, indicating lower allocation value[24][25][26] - The "Growth Factor" is in a high crowding state, with odds at 0.5 standard deviation, trend at 0.3 standard deviation, and crowding at 1.3 standard deviation. Its comprehensive score has dropped to -0.8, highlighting higher trading risks[27][28][29] - The "Odds-Enhanced Strategy" focuses on overweighting high-odds assets and underweighting low-odds assets under a target volatility constraint. Since 2011, it has achieved an annualized return of 6.7% with a maximum drawdown of 3.1%. From 2014, the annualized return was 7.5%, with a maximum drawdown of 2.8%. Since 2019, the annualized return was 7.0%, with a maximum drawdown of 2.8%[40][41][42] - The "Win Rate-Enhanced Strategy" derives macro win rate scores from five factors: currency, credit, growth, inflation, and overseas. Since 2011, it has achieved an annualized return of 7.2% with a maximum drawdown of 3.4%. From 2014, the annualized return was 8.1%, with a maximum drawdown of 2.2%. Since 2019, the annualized return was 7.0%, with a maximum drawdown of 1.5%[43][44][45]