2026 年 01 月 04 日 经济指标出现转弱，PPI 关注度维持 最高 ——量化资产配置月报 202601 相关研究 - 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 请务必仔细阅读正文之后的各项信息披露与声明 量 化 策 略 证 券 研 究 报 告 ⚫ 提升红利、低波配置。按照定量指标的结果，目前经济出现转弱、流动性略偏松，信用指 标略好，微观映射中经济（盈利预期）继续为正但强度偏弱、未触发修正，流动性也未触 发修正，信用继续修正为偏弱，因此宏观各维度的方向继续为经济偏弱、流动性偏松和信 用收缩。本期我们继续主要按照对经济不敏感、对信用不敏感来选择得分前三的因子，宏 观部分选择以盈利、红利为主，红利得分明显提高；沪深 300 中盈利为共振因子，中证 500、中证 1000 中低波都成为共振因子。 ⚫ 大类资产配置观点：黄金配置比例维持高位。结合当前指标，修正后目前经济偏弱、流动 性 ...

2025 年 12 月 01 日 大股票池配置仍偏价值, PPI 关注 升至最高 -量化资产配置月报 202512 相关研究 证券分析师 沈思逸 A0230521070001 shensv@swsresearch.com 邓虎 A0230520070003 denqhu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 请务必仔细阅读正文之后的各项信息披露与声明 大股票池配置仍偏价值。按照定量指标的结果，目前经济出现回升、流动性略偏紧，信用 指标略好，微观映射中经济（盈利预期）继续向上但强度未触发修正，而流动性、信用都 修正为偏弱，因此宏观各维度的方向为经济好转、流动性偏弱和信用收缩。本期我们主要 按照对经济敏感、对信用不敏感来选择得分前三的因子，成长的宏观得分偏低，宏观部分 选择以价值、低波为主；沪深 300、中证 500 中价值都为共振因子，中证 500 中不再配 置成长，而中证 1000 成长得分仍靠前。 大类资产配置观点：黄金配置比例再次提高。目前经济上行、流动性偏紧、信用收缩，债 ● 券的观点偏弱，黄金由于动量回升重新配置到上 ...

2025 年 12 月 01 日 大股票池配置仍偏价值，PPI 关注度 升至最高 ——量化资产配置月报 202512 相关研究 - 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 权 益 量 化 研 究 证 券 研 究 报 告 请务必仔细阅读正文之后的各项信息披露与声明 本研究报告仅通过邮件提供给 中庚基金 使用。1 量 化 策 略 ⚫ 大股票池配置仍偏价值。按照定量指标的结果，目前经济出现回升、流动性略偏紧，信用 指标略好，微观映射中经济（盈利预期）继续向上但强度未触发修正，而流动性、信用都 修正为偏弱，因此宏观各维度的方向为经济好转、流动性偏弱和信用收缩。本期我们主要 按照对经济敏感、对信用不敏感来选择得分前三的因子，成长的宏观得分偏低，宏观部分 选择以价值、低波为主；沪深 300、中证 500 中价值都为共振因子，中证 500 中不再配 置成长，而中证 1000 成长得分仍靠前。 ⚫ ...

Quantitative Models and Construction - **Model Name**: FIFAA (Flexible Indeterminate Factor Asset Allocation) **Model Construction Idea**: Combines quantitative academic rigor with subjective forward-looking flexibility, using historical data (ex-post) and subjective views (ex-ante) to derive asset-factor exposure and optimize portfolio allocation[2][15][74] **Model Construction Process**: 1. **Factor Selection**: Select tradable, low-correlation macroeconomic factors with clear economic logic. Factors include global equities (economic growth), U.S. Treasuries (interest rate/defensive), credit, inflation protection, and currency protection[15][16][20] 2. **Asset-Factor Mapping**: Use LASSO regression to calculate historical beta exposure, then adjust using subjective views derived from professional investor interviews. Subjective single-factor beta is converted into multi-factor beta using matrix transformations[16][35][39] - Formula for historical beta regression: $$y\,=\,X W\,=\,w_{1}x_{1}+\cdots+w_{n}x_{n}$$[32] Loss function for Ridge regression: $$L(w)\,=\,\sum_{i=1}^{n}(y_{i}-\sum w_{j}x_{i j})+\lambda\sum w_{j}^{2}$$[33] Subjective beta transformation: $$\beta_{f}^{*}\,=\,(1\quad F_{f})\,{\binom{\beta_{f}}{\beta_{!f}}}$$[35] $$\beta=F^{-1}\beta^{*}$$[39] 3. **Factor Exposure Optimization**: Optimize factor exposure based on subjective risk/reward judgment or quantitative methods[17] 4. **Portfolio Optimization**: Maximize expected returns while minimizing factor exposure differences. Constraints include absolute exposure differences ≤ 10% of the larger exposure value[44] - Optimization formula: $$m a x(w^{T}r)$$ $$s.\,t.\,w^{T}I\;=\;1$$ $$a b s(w^{T}\beta_{i}-w^{T}\beta_{j})<0.1*m a x(a b s(w^{T}\beta_{i}),a b s(w^{T}\beta_{j}))$$[44] 5. **Rebalancing**: Allow slight deviations in factor exposure to reduce transaction costs and frequency[18] **Model Evaluation**: Provides higher returns and risk-adjusted performance compared to equal-weighted portfolios. Simplified implementation demonstrates practical feasibility[2][74] Model Backtesting Results - **Default Parameters**: - Historical beta optimization: Annualized return 13.63%, annualized volatility 11.47%, max drawdown -18.97%[49][50] - Adjusted beta optimization: Annualized return 15.43%, annualized volatility 16.46%, max drawdown -33.86%[49][50] - Equal-weight portfolio: Annualized return 10.32%, annualized volatility 11.91%, max drawdown -25.27%[49][50] - **Different Adjustment Coefficients**: - Coefficient range (0.1-0.5): Annualized return varies between 15.16%-15.43%, annualized volatility between 15.73%-16.46%, max drawdown between -30.51% to -37.50%[57][59] - **Different Expected Returns**: - Neutral expected return scenarios (5%, 10%, 15%): Annualized return ranges from 13.63%-15.90%, annualized volatility from 11.47%-16.45%, max drawdown from -18.97% to -36.67%[69][70][71][72] Quantitative Factors and Construction - **Factor Name**: Macroeconomic Factors (Economic Growth, Interest Rate, Inflation) **Factor Construction Idea**: Represent macroeconomic trends using tradable indices to ensure simplicity and reduce calculation errors[15][20][30] **Factor Construction Process**: - Economic growth: Represented by stock indices (e.g., Wind All A Index, S&P 500)[30] - Interest rate: Represented by bond indices (e.g., China Bond Treasury Wealth Index)[30] - Inflation: Composite of commodity indices (e.g., Nanhua Industrial, Agricultural, Energy, and Black Metal indices)[20][30] **Factor Evaluation**: Tradable and low-correlation factors ensure practical applicability and reduce subjective judgment uncertainty[15][16][20] Factor Backtesting Results - **Macroeconomic Factor Correlation Matrix**: - Wind All A vs. S&P 500: 0.15 - Wind All A vs. China Bond Treasury: -0.12 - Wind All A vs. Commodity Composite: 0.30[28][30] - **Factor Performance**: - Economic growth factor (Wind All A): Annualized return 13.63%-15.43% depending on optimization method[49][50][69] - Inflation factor (Commodity Composite): Adjusted beta optimization shows higher returns during inflationary periods[49][50][69]

Core Insights - The report predicts changes in the yield curve using the Nelson-Siegel model, which describes the curve's dynamics through three factors: level, slope, and curvature [3][29]. - An improvement in the model for predicting the level factor has been made by incorporating policy rates, market benchmark rates, slope, and curvature factors, which enhances the predictive accuracy [4][56]. - The duration rotation strategy based on yield curve predictions shows robust performance, consistently outperforming benchmarks and achieving significant excess returns [5][91]. Duration Rotation Strategy - The latest signal from the duration rotation strategy, as of October 31, 2025, indicates a strong preference for long-duration interest rate bonds, with a signal value of 10 [6][96]. - The strategy is designed to capitalize on the natural "risk-return-liquidity" trade-offs present in different maturity bonds, where short-term bonds offer lower duration and volatility but higher reinvestment risk, while long-term bonds provide higher coupon protection but are more exposed to interest rate risk [10][14]. Yield Curve Construction - The report establishes the yield curve using historical spot rate data from 2006 to 2025, showing that the average yield curve is monotonically upward over the entire period [21][22]. - Principal component analysis of historical spot rates reveals three main components that represent the level, slope, and curvature of the yield curve, providing insights into its dynamics [26][41]. Statistical Characteristics of Spot Rates - The statistical characteristics of spot rates indicate that as the maturity increases, the mean yield rises while volatility decreases, with the average yield curve showing a consistent upward trend [21][22]. - The report provides detailed statistics on various maturities, including total returns, annualized returns, annualized volatility, Sharpe ratios, and maximum drawdowns, highlighting the performance of different maturity segments [12][95]. Model Improvements - The report discusses enhancements to the predictive model for the level factor by integrating external variables such as policy rates and market rates, which have shown to improve the direction prediction accuracy [56][62]. - The introduction of additional factors, including slope and curvature, aims to refine predictions during periods of yield curve inversion, thereby increasing the model's robustness [70][75]. Backtesting Results - Backtesting results demonstrate that the improved duration rotation strategy yields a total return of 110.37% over the evaluation period, significantly outperforming various maturity indices and equal-weighted indices [91][95]. - The strategy's maximum drawdown is reported at 5.36%, which is lower than the maximum drawdown of 7.23% for the 7-10 year index, indicating a more stable performance [95].

- **Domestic Asset BL Model** - **Model Name**: Black-Litterman (BL) Model - **Construction Idea**: The BL model integrates subjective views with quantitative asset allocation using Bayesian theory, optimizing asset weights based on market analysis and expected returns. It addresses the sensitivity of mean-variance models to expected returns and provides higher fault tolerance compared to purely subjective investments [26][27][33] - **Construction Process**: 1. Use historical returns of assets over the past five years to estimate market equilibrium returns (Π) 2. Specify a risk aversion coefficient (e.g., λ = 10), which corresponds to a target volatility 3. Alternatively, assign fixed weights (e.g., stock:bond:convertible bond:commodity:gold = 10:80:5:2.5:2.5) and reverse calculate the risk aversion coefficient dynamically for each period [33] - **Evaluation**: The BL model effectively combines subjective views with quantitative methods, providing robust asset allocation solutions [26][27] - **Domestic Asset Risk Parity Model** - **Model Name**: Risk Parity Model - **Construction Idea**: The model aims to equalize the risk contribution of each asset to the overall portfolio, optimizing asset weights based on expected volatility and correlation [32][35] - **Construction Process**: 1. Select appropriate underlying assets 2. Calculate each asset's risk contribution to the portfolio 3. Solve optimization problems to determine final asset weights 4. Use daily returns over the past five years to estimate the covariance matrix for stability [35] - **Evaluation**: The model provides stable returns across economic cycles and is well-suited for domestic investors [32][35] - **Macro Factor-Based Asset Allocation Strategy** - **Model Name**: Macro Factor-Based Strategy - **Construction Idea**: The strategy bridges macroeconomic research with asset allocation by constructing high-frequency macro factors (e.g., growth, inflation, interest rates, credit, exchange rates, liquidity) and aligning asset weights with subjective macroeconomic views [41][46] - **Construction Process**: 1. Calculate factor exposure levels for assets monthly 2. Use risk parity portfolios as benchmarks to compute baseline factor exposures 3. Adjust factor exposure targets based on subjective macroeconomic views (e.g., inflation up = positive deviation) 4. Solve for asset weights using the model [41][46] - **Evaluation**: The strategy effectively incorporates macroeconomic insights into asset allocation, enhancing adaptability to changing economic conditions [41][46] - **Backtest Results for Models** - **Domestic Asset BL Model 1**: - Annualized return: 3.58% - Max drawdown: 1.31% - Annualized volatility: 2.19% [31][33] - **Domestic Asset BL Model 2**: - Annualized return: 3.18% - Max drawdown: 1.06% - Annualized volatility: 1.99% [31][33] - **Domestic Asset Risk Parity Model**: - Annualized return: 3.12% - Max drawdown: 0.76% - Annualized volatility: 1.34% [39][40] - **Macro Factor-Based Strategy**: - Annualized return: 3.42% - Max drawdown: 0.65% - Annualized volatility: 1.32% [46][47]

Group 1 - The article emphasizes a balanced approach to factor selection, integrating macroeconomic quantitative insights with factor momentum to identify resonant factors while adjusting for discrepancies between macro and micro indicators [1] - Current macroeconomic indicators show signs of economic recovery, slightly loose liquidity, and improved credit metrics, leading to a revised outlook of economic improvement, weak liquidity, and loose credit [1] - The article presents a table summarizing the performance of various factors across different indices, indicating that growth factors remain strong in the CSI 300, while the CSI 500 shows a more balanced factor selection [2][3] Group 2 - Economic leading indicators are beginning to rise, with the PMI and new orders showing increases, suggesting a slight upward trend in economic activity expected to continue into early 2026 [5][9] - The liquidity environment is assessed as slightly loose despite rising interest rates, with a comprehensive liquidity signal indicating a mixed outlook [11][15] - Credit indicators are showing weakness, with a slight positive shift in overall credit metrics, indicating a complex credit environment [15][16] Group 3 - The article suggests a preference for asset allocation towards gold due to strong momentum, while equity allocations are slightly reduced, reflecting a cautious stance on A-shares [16] - The focus of market attention is shifting from liquidity to economic indicators, with recent trends indicating a growing concern for economic performance over liquidity conditions [17] - Industry selection is advised to favor sectors sensitive to economic changes but less affected by liquidity, with public utilities and coal being highlighted as top sectors based on their sensitivity scores [19]

Group 1 - The report indicates that the economic leading indicators are showing signs of a slight recovery, with liquidity remaining slightly loose and credit indicators improving [3][12][19] - The economic forecast model suggests that October 2025 is at a turning point, with expectations for a slight upward trend over the next three months before entering a plateau [12][13] - The report highlights that the focus of the market is shifting towards economic indicators, surpassing liquidity concerns, with increased attention on economic and PPI-related factors [26][27] Group 2 - The liquidity environment is characterized by rising interest rates, with long-term rates exceeding the average, while overall liquidity remains slightly loose due to positive monetary supply signals [19][22] - Credit indicators have shown a slight positive trend, although the overall credit volume and structure remain low, indicating a mixed outlook for credit conditions [23][24] - The asset allocation perspective suggests a high allocation to gold due to strong momentum, while equity allocations have been slightly reduced [24][25] Group 3 - The industry selection is leaning towards sectors that are sensitive to economic conditions but less sensitive to liquidity, with a notable increase in defensive and consumer attributes [28][29] - The report identifies specific industries with the highest sensitivity to economic changes, including utilities and coal, while also highlighting sectors like media and consumer electronics for credit sensitivity [28][29] - The overall balance in industry selection reflects a decline in growth attributes, emphasizing a more defensive investment strategy [29]

Group 1 - The report indicates a slight recovery in economic indicators, with liquidity remaining slightly loose and credit indicators showing improvement. The macroeconomic dimensions suggest an overall direction of economic improvement, weak liquidity, and loose credit [3][6][8] - The economic leading indicators are expected to show a slight upward trend over the next three months, indicating a bottoming out in October 2025, with a prolonged period of slight recovery compared to last month [12][13] - The liquidity environment is characterized by rising interest rates, with long-term rates exceeding the moving average, while overall liquidity remains slightly loose due to positive monetary supply signals [19][22] Group 2 - The report emphasizes a high allocation to gold, with a weakening view on bonds and a slight reduction in A-share allocation. The current economic upturn, tight liquidity, and favorable credit conditions support this allocation strategy [24][26] - Market focus has shifted towards economic indicators, surpassing liquidity concerns, with a notable increase in attention to economic and PPI-related factors since September [26][28] - The industry selection is inclined towards sectors sensitive to economic changes, less sensitive to liquidity, and sensitive to credit conditions. The report highlights a decrease in growth attributes and an increase in defensive and consumer attributes, indicating a balanced approach [28][30][29]

Group 1 - The report highlights that domestic equity assets performed well in July 2025, with the risk parity strategy achieving a year-to-date return of 2.65% [2][5][20] - The report provides a summary of various asset allocation strategies, indicating that the domestic asset BL strategy 1 and 2 yielded returns of 2.40% and 2.34% respectively, while the risk parity strategy and macro factor-based strategy returned 2.65% and 2.59% respectively [21][41][42] - The report notes that the domestic equity market saw significant gains, with the CSI 1000 index rising by 4.8% and the Hang Seng Index increasing by 2.78% in July [8][9][10] Group 2 - The report discusses the correlation between different asset classes, indicating that the correlation between the CSI 300 and the total wealth index of government bonds was -38.08%, suggesting a potential for diversification [15][16] - The report outlines the performance of various asset allocation models, with the domestic risk parity strategy showing a maximum drawdown of 0.76% and an annualized volatility of 1.46% [41][42] - The macroeconomic outlook suggests downward risks for growth factors, while inflation expectations may stabilize due to recent policy measures [45][47]