● 市场关注点： PPI关注度维持最高。根据Factor Mimicking模型，2024年通胀（通缩）的关注度较高，而近2024年9月后流动性持续为最受关注的变量； 2025年10月末PPI关注度小幅超越经济，目前已成为最受关注的变量，提示市场对后续需求回升的关注度突出，而流动性的关注度1月提升后重新回落。 ● 宏观视角下的行业选择： 结合各宏观维度指标观点，本期行业选择与上期保持一致。 ● 风险提示：模型根据历史数据构建，历史表现不代表未来，市场环境发生重大变化时可能失效。 01 成长得分降低、整体风格偏均衡 在《求同存异：宏观量化与因子动量的左右侧配合》中，我们将宏观量化给出的因子选择观点与因子动量的观点进行结合，选择产生共振的因子，而对其他因子，除了成长因 子任意方法选择即配置，其他因子若为市值、基本面因子则参考宏观结果，若为价量、分析师预期因子则参考因子动量结果。 ● 大类资产配置观点： 黄金仓位略有下降。结合当前指标，修正后目前经济偏弱、流动性偏松、信用收缩，债券的观点好转，黄金动量回落、仓位有所 下降，美股仓位对应上升。 ● 经济前瞻指标： 进入下行末期。根据本月更新的经济前瞻指标模型提示，20 ...

2026 年 03 月 01 日 成长得分降低、整体风格偏均衡 ——量化资产配置月报 202603 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 益 量 化 研 究 | 1. 成长得分降低、整体风格偏均衡 4 | | --- | | 2. 各宏观指标方向与资产配置观点 6 | | 2.1 经济前瞻指标：进入下行末期 6 | | 2.2 流动性：维持略偏松 9 | | 2.3 综合信用指标 10 | | 2.4 大类配置观点：黄金仓位略有下降 10 | | 3. 市场关注点：PPI 关注度维持最高 10 | | 4. 宏观视角下的行业选择 11 | | 5. 风险提示 12 | 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 权 量 化 策 略 - ⚫ 成长得分降低、整体风格偏均衡。按照定量指标的结果，目前经济出现 ...

Group 1 - The core viewpoint of the article indicates a return of low volatility factors, forming a resonance in the current economic environment, which is characterized by weakening economic indicators, slightly loose liquidity, and a contraction in credit [1][5][6] - The macroeconomic dimensions suggest a consistent direction of weak economy, loose liquidity, and credit contraction, aligning with previous assessments [5][6] - The article emphasizes the selection of factors that are insensitive to economic changes but sensitive to liquidity and credit, with a notable absence of clear preferences for growth or value factors [6][9] Group 2 - The asset allocation perspective suggests a slight allocation to US stocks, with a positive outlook on bonds despite low overall positions influenced by other assets [21][22] - The economic leading indicators maintain a downward judgment, with predictions indicating a continued decline into early 2026, supported by recent PMI data showing a decrease [9][12] - The liquidity environment is assessed as slightly loose, with short-term interest rates declining and monetary supply showing a neutral signal, while excess reserves continue to decrease [16][19] Group 3 - The article highlights that the market's focus remains on PPI, which has gained prominence over economic indicators, indicating heightened attention to future demand recovery [22][24] - Industry selection continues to favor sectors that are less sensitive to economic fluctuations, particularly TMT (Technology, Media, and Telecommunications) and consumer sectors [24][25] - The analysis of macroeconomic indicators suggests that industries such as electronics, retail, and computing are currently positioned favorably based on their sensitivity to liquidity and credit [25]

Group 1 - The report indicates a return of low volatility factors, forming a resonance in the current market environment, with economic indicators showing a weakening trend, liquidity slightly easing, and credit indicators remaining weak [2][8][11] - The report emphasizes the selection of factors that are insensitive to economic changes but sensitive to liquidity and credit, highlighting the low volatility factor in the CSI 300 as a key resonant factor [5][9][11] - The macroeconomic outlook suggests a continued downtrend in economic indicators, with the economic forecast model indicating that February 2026 is at the beginning of a decline that started in December 2025 [11][12][13] Group 2 - The liquidity environment is assessed as slightly easing, with short-term interest rates declining and monetary supply showing a neutral signal, while excess reserves are decreasing [18][21][23] - Credit indicators are showing a weakening trend, with credit spreads widening and overall credit metrics declining, indicating a contraction in credit availability [24][25] - The asset allocation strategy suggests a slight allocation to US stocks, with a neutral stance on A-shares and a positive outlook on gold based on momentum [26][28] Group 3 - The report identifies PPI as the most closely monitored variable, with inflation concerns rising and liquidity becoming a significant focus for the market [28] - The industry selection is biased towards TMT (Technology, Media, and Telecommunications) and consumer sectors, based on macroeconomic indicators and their sensitivity to economic changes [29]

Group 1 - The report indicates a shift towards a weaker economic outlook, with liquidity remaining slightly loose and credit indicators showing slight improvement. The macro dimensions suggest a continued trend of weak economy, loose liquidity, and credit contraction [2][8][14] - The asset allocation strategy emphasizes high dividend and low volatility configurations, focusing on factors that are insensitive to economic and credit conditions. The top scoring factors are centered around profitability and dividends, with significant improvements in dividend scores [5][9][30] - The report maintains a high allocation to gold, suggesting a 20% upper limit due to ongoing momentum, while bond views have improved but remain low due to other asset influences [2][27] Group 2 - Economic forward indicators are trending weak, entering the initial phase of a decline since December 2025, with expectations of continued downward movement. Key indicators such as PMI and retail sales are in a downward cycle [14][19] - Liquidity conditions have returned to a slightly loose state, with interest rates stabilizing and short-term rates slightly declining, indicating a shift back to a neutral signal [21][24] - Credit indicators show slight improvement in social financing year-on-year, although the structure of loans to households and enterprises has decreased, indicating a preference in credit indicators [25][26] Group 3 - The market focus remains on PPI, which has surpassed economic indicators in attention, highlighting market concerns regarding future demand recovery [28][29] - Industry selection is biased towards weak cyclical sectors, with top scoring industries including computer and food and beverage sectors, which are less sensitive to economic and credit fluctuations [30][31]

Group 1 - The core view of the report indicates that the large stock pool allocation remains biased towards value, with a focus on economic recovery, slightly tight liquidity, and credit contraction [3][6][9] - The report emphasizes the selection of factors sensitive to the economy but insensitive to credit, with a preference for value and low volatility in macroeconomic selections [9][10][31] - The allocation viewpoint for major assets suggests an increase in gold allocation to 20%, while A-shares allocation decreases due to economic conditions [27][28] Group 2 - Economic leading indicators are maintained at an upward trend, with predictions indicating a potential downturn starting in the next period [15][19] - Liquidity indicators show a slight tightening, with monetary supply remaining above zero but overall liquidity pointing towards a slightly tight condition [22][25] - Credit indicators are weak, with a low level of credit volume and structure, although there are signs of improvement in the proportion of loans to households and enterprises [26][27] Group 3 - The market focus has shifted to PPI, which has become the most concerning variable, surpassing economic indicators in recent observations [30][29] - The report suggests industry selection should favor sectors sensitive to economic changes but less affected by credit conditions, maintaining a value bias [31][32]

Group 1 - The core view of the report indicates that the large stock pool allocation remains biased towards value, with economic recovery observed, liquidity slightly tight, and credit indicators showing slight improvement. The macro dimensions suggest a direction of economic improvement, weak liquidity, and credit contraction [3][9][15] - The report emphasizes that the allocation of major assets has shifted, with an increased proportion of gold allocation to 20% due to economic upturn, while A-shares allocation has decreased [3][28] - Economic leading indicators are maintained at an upward trend, with predictions indicating that December 2025 will be at the end of a rising cycle since September, although the strength of the indicators is not high [3][15][19] Group 2 - The liquidity environment is slightly tight, with monetary indicators showing a decline. The overall interest rates have remained stable, and the excess reserve ratio has dropped below historical levels [3][23][26] - Credit indicators are weak, with low levels of credit volume and structure. The report notes that the total social financing stock year-on-year remains weak, although there is some improvement in the structure of loans to households and enterprises [3][27][28] - The market focus has shifted to PPI, which has become the most concerning variable, surpassing economic indicators. This reflects the market's heightened attention to future demand recovery [3][30][31] Group 3 - The industry selection from a macro perspective favors sectors that are sensitive to economic changes but insensitive to credit fluctuations, maintaining a value bias [3][32] - The report identifies the highest scoring industries based on economic sensitivity and credit insensitivity, including utilities, coal, and construction decoration as top sectors [3][32]

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]