绝对收益产品及策略周报（260202-260206） [Table_Authors] 郑雅斌(分析师) 上周 161 只固收+基金创新高 本报告导读： 股票端采用小盘价值组合+不择时的股债 10/90 和 20/80 月度再平衡策略，2026 年累 计收益分别为 1.36%和 2.53%。 投资要点： 金 融 工 程 周 报 固收+产品业绩跟踪。截至 2026 年 02 月 06 日，全市场固收+基金 规模 23568.03 亿元，产品数量 1166 只，其中 161 只上周净值创历 史新高。上周（20260202-20260206，下同）共新发 15 只产品，各 类型基金业绩中位数表现分化：混合债券型一级（0.07%）、二级（- 0.15%）、偏债混合型（-0.26%）、灵活配置型（-0.19%）、债券型 FOF （-0.29%）及混合型 FOF（-0.53%）。按风险等级划分，保守型、稳 健型、激进型基金中位数收益分别为 0.04%、-0.17%、-0.27%。 请务必阅读正文之后的免责条款部分 大类资产配置和行业 ETF 轮动策略跟踪。1）大类资产择时观点。 2026Q1 逆周期配置模型给出的宏观环境预 ...

Group 1 - The report indicates that as of January 23, 2026, the total scale of fixed income + funds in the market reached 21,780.36 billion, with 1,157 products, and 824 of them achieved historical net value highs last week [2][18] - The performance median of various fund types for the week of January 19-23, 2026, showed differentiation: mixed bond type I (0.26%), II (0.47%), and other types [14][16] - The conservative, stable, and aggressive fund median returns were 0.32%, 0.47%, and 0.59% respectively [14][16] Group 2 - The macro environment forecast for Q1 2026 indicates a slowdown, with the Shanghai and Shenzhen 300 index, the total wealth index of government bonds, and the AU9999 contract yielding 1.57%, 0.36%, and 14.08% respectively [3] - The industry ETF rotation strategy for January 2026 suggests focusing on coal, steel, securities, and banking ETFs, with a weekly return of 1.77% and a cumulative return of 1.41% for the month [3][4] Group 3 - The mixed stock-bond strategy's performance showed a 0.00% return for the week, with a year-to-date return of 0.51%, while the stock-bond risk parity strategy yielded 0.13% for the week and 0.43% year-to-date [4] - The small-cap value style in the stock-bond 20/80 combination performed best with a year-to-date return of 2.95%, while the PB earnings, high dividend, and small-cap growth strategies yielded 1.08%, 0.78%, and 2.31% respectively [4][10]

Group 1 - The report indicates that the stock side employs a small-cap growth portfolio combined with a non-timing stock-bond rebalancing strategy of 10/90 and 20/80, projecting cumulative returns of 6.21% and 11.30% by 2025 [1][2] - As of December 12, 2025, the total market size of fixed income + funds reached 21,722.64 billion, with 1,148 products, and 20 of these funds achieved historical net value highs last week [2][9] - The performance median of various fund types showed divergence, with mixed bond type I at 0.06%, mixed bond type II at 0.03%, and flexible allocation type at 0.06% [2][14] Group 2 - The macro environment forecast for Q4 2025 suggests an inflationary trend, with the CSI 300 index, total wealth index of government bonds, and AU9999 contract yielding 1.20%, -0.29%, and 1.69% respectively since December [3] - Recommended industry ETFs for December 2025 include Southern CSI Nonferrous Metals ETF, Huabao CSI Bank ETF, Guotai CSI All-Share Securities Company ETF, and others, with a combined return of -0.72% last week [3][4] - The stock-bond mixed strategy showed a return of 0.09% last week, with year-to-date returns of 4.84%, while the stock-bond risk parity strategy yielded 0.11% last week and 2.01% year-to-date [4] Group 3 - The report highlights that the small-cap growth style within the stock-bond 20/80 combination performed best with a year-to-date return of 11.30%, while other strategies saw declines when adjusted to a 10/90 allocation [4][19] - The absolute return strategy tracking indicates that the median performance of mixed bond type I, mixed bond type II, and flexible allocation funds for the year-to-date is 1.78%, 4.18%, and 3.65% respectively [16][17] - The report notes that 20 fixed income + products reached historical net value highs, including 9 mixed bond type I funds and 4 mixed bond type II funds [19]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model 1: Macro Timing Driven Stock-Bond 20/80 Rebalancing Strategy - **Construction Idea**: This model aims to balance a portfolio with 20% stocks and 80% bonds, driven by macroeconomic timing signals[4] - **Construction Process**: - The model uses macroeconomic indicators to determine the optimal timing for rebalancing the portfolio - The rebalancing is done monthly to maintain the 20/80 stock-bond ratio - The performance metrics include weekly, monthly, and year-to-date returns, annualized volatility, maximum drawdown, and Sharpe ratio[4][30] - **Evaluation**: The model is designed to provide a stable return with lower volatility by leveraging macroeconomic indicators for timing[4] - **Formula**: Not explicitly provided Model 2: Macro Timing Driven Stock-Bond Risk Parity Strategy - **Construction Idea**: This model aims to balance the risk between stocks and bonds based on macroeconomic timing signals[4] - **Construction Process**: - The model uses macroeconomic indicators to determine the optimal timing for rebalancing the portfolio - The rebalancing is done to achieve risk parity between stocks and bonds - The performance metrics include weekly, monthly, and year-to-date returns, annualized volatility, maximum drawdown, and Sharpe ratio[4][30] - **Evaluation**: The model aims to achieve a balanced risk exposure between stocks and bonds, providing a more stable return profile[4] - **Formula**: Not explicitly provided Model 3: Macro Timing + Sector ETF Rotation Enhanced Stock-Bond 20/80 Rebalancing Strategy - **Construction Idea**: This model enhances the stock-bond 20/80 rebalancing strategy by incorporating sector ETF rotation based on macroeconomic timing signals[4] - **Construction Process**: - The model uses macroeconomic indicators to determine the optimal timing for rebalancing the portfolio - Sector ETFs are selected based on historical fundamentals, expected fundamentals, sentiment, technical factors, and macroeconomic factors - The rebalancing is done monthly to maintain the 20/80 stock-bond ratio - The performance metrics include weekly, monthly, and year-to-date returns, annualized volatility, maximum drawdown, and Sharpe ratio[4][30] - **Evaluation**: The model aims to enhance returns by rotating into favorable sector ETFs while maintaining a balanced stock-bond ratio[4] - **Formula**: Not explicitly provided Model 4: Macro Timing + Sector ETF Rotation Enhanced Stock-Bond Risk Parity Strategy - **Construction Idea**: This model enhances the stock-bond risk parity strategy by incorporating sector ETF rotation based on macroeconomic timing signals[4] - **Construction Process**: - The model uses macroeconomic indicators to determine the optimal timing for rebalancing the portfolio - Sector ETFs are selected based on historical fundamentals, expected fundamentals, sentiment, technical factors, and macroeconomic factors - The rebalancing is done to achieve risk parity between stocks and bonds - The performance metrics include weekly, monthly, and year-to-date returns, annualized volatility, maximum drawdown, and Sharpe ratio[4][30] - **Evaluation**: The model aims to achieve a balanced risk exposure between stocks and bonds while enhancing returns through sector ETF rotation[4] - **Formula**: Not explicitly provided Model Backtesting Results Macro Timing Driven Stock-Bond 20/80 Rebalancing Strategy - **Weekly Return**: -0.01%[4] - **Monthly Return**: -0.37%[4] - **Year-to-Date Return**: 4.83%[4] - **Annualized Volatility**: 3.47%[4] - **Maximum Drawdown**: 1.78%[4] - **Sharpe Ratio**: 1.54[4] Macro Timing Driven Stock-Bond Risk Parity Strategy - **Weekly Return**: -0.08%[4] - **Monthly Return**: -0.30%[4] - **Year-to-Date Return**: 2.07%[4] - **Annualized Volatility**: 1.77%[4] - **Maximum Drawdown**: 1.50%[4] - **Sharpe Ratio**: 1.30[4] Macro Timing + Sector ETF Rotation Enhanced Stock-Bond 20/80 Rebalancing Strategy - **Weekly Return**: 0.23%[4] - **Monthly Return**: -0.52%[4] - **Year-to-Date Return**: 7.98%[4] - **Annualized Volatility**: 5.46%[4] - **Maximum Drawdown**: 2.54%[4] - **Sharpe Ratio**: 1.62[4] Macro Timing + Sector ETF Rotation Enhanced Stock-Bond Risk Parity Strategy - **Weekly Return**: -0.02%[4] - **Monthly Return**: -0.33%[4] - **Year-to-Date Return**: 3.17%[4] - **Annualized Volatility**: 2.21%[4] - **Maximum Drawdown**: 1.45%[4] - **Sharpe Ratio**: 1.59[4] Quantitative Factors and Construction Methods Factor 1: PB Earnings - **Construction Idea**: This factor aims to capture the value premium by focusing on stocks with low price-to-book ratios and high earnings[4] - **Construction Process**: - Select stocks with low price-to-book ratios - Filter for stocks with high earnings - Rebalance the portfolio monthly to maintain the factor exposure[4] - **Evaluation**: The factor aims to capture the value premium by focusing on undervalued stocks with strong earnings[4] - **Formula**: Not explicitly provided Factor 2: High Dividend Yield - **Construction Idea**: This factor aims to capture the income premium by focusing on stocks with high dividend yields[4] - **Construction Process**: - Select stocks with high dividend yields - Rebalance the portfolio monthly to maintain the factor exposure[4] - **Evaluation**: The factor aims to provide stable income through high dividend-paying stocks[4] - **Formula**: Not explicitly provided Factor 3: Small Cap Value - **Construction Idea**: This factor aims to capture the small-cap premium by focusing on small-cap stocks with low valuations[4] - **Construction Process**: - Select small-cap stocks with low valuations - Rebalance the portfolio monthly to maintain the factor exposure[4] - **Evaluation**: The factor aims to capture the higher growth potential of small-cap stocks with low valuations[4] - **Formula**: Not explicitly provided Factor 4: Small Cap Growth - **Construction Idea**: This factor aims to capture the growth premium by focusing on small-cap stocks with high growth potential[4] - **Construction Process**: - Select small-cap stocks with high growth potential - Rebalance the portfolio monthly to maintain the factor exposure[4] - **Evaluation**: The factor aims to capture the higher growth potential of small-cap stocks with strong growth prospects[4] - **Formula**: Not explicitly provided Factor Backtesting Results PB Earnings - **Weekly Return**: 0.11%[37] - **Monthly Return**: -0.28%[37] - **Year-to-Date Return**: 2.93%[37] - **Annualized Volatility**: 2.27%[37] - **Maximum Drawdown**: 1.82%[37] - **Sharpe Ratio**: 0.03[37] High Dividend Yield - **Weekly Return**: 0.08%[37] - **Monthly Return**: 0.02%[37] - **Year-to-Date Return**: 2.63%[37] - **Annualized Volatility**: 2.01%[37] - **Maximum Drawdown**: 1.39%[37] - **Sharpe Ratio**: -0.05[37] Small Cap Value - **Weekly Return**: 0.44%[37] - **Monthly Return**: -0.09%[37] - **Year-to-Date Return**: 6.14%[37] - **Annualized Volatility**: 3.42%[37] - **Maximum Drawdown**: 3.69%[37] - **Sharpe Ratio**: 0.52[37] Small Cap Growth - **Weekly Return**: 0.60%[37] - **Monthly Return**: 0.24%[37] - **Year-to-Date Return**: 6.50%[37] - **Annualized Volatility**: 3.49%[37] - **Maximum Drawdown**: 3.86%[37] - **Sharpe Ratio**: 0.56[37]

Group 1: Fixed Income + Product Performance Tracking - As of November 21, 2025, the total market size of fixed income + funds reached 21,846.96 billion, with 1,151 products, and 23 products achieved historical net value highs last week [2][20] - The median performance of various fund types for the week of November 17-21, 2025, showed mixed results: mixed bond type I (-0.04%), mixed bond type II (-0.72%), and flexible allocation type (-0.60%) [2][13] - The median returns for conservative, balanced, and aggressive funds were -0.13%, -0.59%, and -0.93%, respectively [2][13] Group 2: Major Asset Allocation and Industry ETF Rotation Strategy Tracking - The macro environment forecast for Q4 2025 indicates inflation, with the Shanghai Composite Index, China Government Bond Total Wealth Index, and AU9999 contract yielding -4.03%, -0.10%, and 0.63% respectively since November [3] - Recommended industry ETFs for November 2025 include semiconductor, securities companies, communication equipment, new energy vehicle batteries, and animation game ETFs, with a weekly return of -5.15% and a cumulative return of -7.92% for the month [3] Group 3: Absolute Return Strategy Performance Tracking - The macro timing-driven stock-bond 20/80 rebalancing strategy yielded -0.38% last week, with a year-to-date return of 4.84% [4] - The small-cap growth style within the stock-bond 20/80 combination showed a notable annual return of 10.57%, while the PB earnings, high dividend, and small-cap value strategies returned 4.35%, 3.81%, and 10.20% respectively [4] - The cumulative return for the small-cap growth combination based on a macro momentum model was 12.70% [4]

Group 1: Core Insights - The report highlights that the stock side employs a small-cap growth portfolio combined with a non-timing stock-bond monthly rebalancing strategy, projecting cumulative returns of 5.93% and 11.15% by 2025 [1][4] - As of August 15, 2025, the total market size of fixed income plus funds reached 1,784.66 billion, with 1,177 products, of which 412 achieved historical net value highs last week [2][9] - The report indicates a divergence in performance among various fund types, with median returns for mixed bond type funds being -0.07% for level one, 0.17% for level two, and 0.33% for mixed bond type funds [2][12] Group 2: Asset Allocation and ETF Rotation - The macro environment forecast for Q3 2025 suggests an inflationary trend, with the CSI 300 index, the total wealth index of government bonds, and AU9999 contracts yielding 3.11%, -0.32%, and 1.03% respectively since August [3][4] - Recommended industry ETFs for August 2025 include those focused on artificial intelligence, semiconductors, non-ferrous metals, banking, and major consumer sectors, with a weekly return of 4.01% and a cumulative return of 5.81% for the month [3][4] Group 3: Absolute Return Strategy Performance - The macro-timing driven stock-bond 20/80 rebalancing strategy yielded a return of 0.47% last week, while the stock-bond risk parity strategy returned -0.02% [4][9] - The small-cap growth style within the stock-bond 20/80 combination showed the most significant performance, with a year-to-date return of 11.15% [4][9] - The report notes that the cumulative return for the small-cap growth portfolio, when adjusted for timing strategies, reached 12.81% [4][9]

- The Black-Litterman model (BL model) is used for asset allocation, combining investor views with market equilibrium[17][20] - The construction process of the BL model involves adjusting the expected returns based on investor views and then optimizing the portfolio using mean-variance optimization[17][20] - The Risk Parity model aims to allocate risk equally across all assets in a portfolio, rather than allocating capital equally[27][30] - The construction process of the Risk Parity model involves calculating the risk contribution of each asset and solving an optimization problem to equalize these contributions[28][29][30] - The Counter-Cyclical Allocation model adjusts asset allocation based on economic cycles, aiming to reduce risk during downturns and increase exposure during upturns[11][43] - The Macro Momentum Timing model uses macroeconomic indicators to time market entries and exits, aiming to capture trends and avoid downturns[11][60] - The Sentiment Timing model uses investor sentiment indicators to time market entries and exits, aiming to capitalize on market overreactions[67] Model Performance Metrics - **Black-Litterman Model**: Annualized return 6.58%, maximum drawdown 3.18%, annualized volatility 2.15%, Sharpe ratio 1.86, Calmar ratio 2.07[22][24] - **Risk Parity Model**: Annualized return 6.07%, maximum drawdown 3.78%, annualized volatility 2.26%, Sharpe ratio 1.58, Calmar ratio 1.61[31] - **Counter-Cyclical Allocation Model**: Annualized return 7.36%, maximum drawdown 8.85%, annualized volatility 6.12%, Sharpe ratio 1.13, Calmar ratio 0.85[43][47] - **Macro Momentum Timing Model**: Annualized return 7.06%, maximum drawdown 6.60%, annualized volatility 6.06%, Sharpe ratio 1.13, Calmar ratio 1.97[60] - **Sentiment Timing Model**: Annualized return 7.74%, maximum drawdown 24.91%, annualized volatility 17.49%, Sharpe ratio 1.01, Calmar ratio 0.62[67][87]

Group 1 - The report indicates that the stock side employs a small-cap value portfolio combined with a non-timing stock-bond monthly rebalancing strategy of 10/90 and 20/80, with cumulative returns of 4.97% and 9.28% respectively by 2025 [1][4] - As of July 25, 2025, the total market size of fixed income + funds reached 1,775.714 billion, with 1,173 products, and 136 of them reached historical net value highs last week [2][9] - The performance of various fund types showed divergence, with median returns for mixed bond type funds being -0.15% for level one, 0.09% for level two, and 0.19% for biased bond mixed funds [2][14] Group 2 - The macro environment forecast for Q3 2025 suggests an inflationary trend, with the CSI 300 index rising by 4.85% since July, while the China Government Bond Index fell by 0.43% [3] - Recommended industry ETFs for July 2025 include those focused on securities companies, semiconductors, non-ferrous metals, and major consumer sectors, with a weekly return of 4.72% and a cumulative return of 6.97% for the month [3][4] - The absolute return strategy performance showed that the macro-timing driven stock-bond 20/80 rebalancing strategy yielded a return of 0.20% last week, while the stock-bond risk parity strategy had a return of -0.20% [4][16] Group 3 - The small-cap value style within the stock-bond 20/80 combination performed notably well, achieving a year-to-date return of 9.28%, while other strategies like PB earnings and high dividend stocks yielded 4.01% and 2.65% respectively [4][16] - The report highlights that 136 fixed income + products reached historical net value highs, with a breakdown of 30 level one mixed bond funds, 41 level two mixed bond funds, and 35 biased bond mixed funds [18][20] - The report also provides insights into the performance of conservative, balanced, and aggressive funds, with median returns of -0.09%, 0.09%, and 0.29% respectively [14][18]

Quantitative Models and Construction Methods 1. Model Name: Macro Timing Model - **Model Construction Idea**: The model predicts future macroeconomic environments using proxy variables and selects optimal assets for absolute return portfolios based on these predictions[25] - **Model Construction Process**: - The model uses proxy variables to forecast macroeconomic conditions such as inflation, economic growth, interest rates, exchange rates, and risk sentiment[25] - Based on these forecasts, the model selects assets that are expected to perform best in the predicted environment[25] - Example formula: $ \text{Expected Return} = \alpha + \beta \times \text{Macro Variable} $ where $\alpha$ is the intercept and $\beta$ is the coefficient representing the sensitivity to the macro variable[25] - **Model Evaluation**: The model is effective in predicting macroeconomic conditions and selecting optimal assets for different environments[25] - **Model Test Results**: - Q1 2025 predictions: Inflation environment - Asset returns: CSI 300: 0.10%, CSI 2000: 6.05%, Nanhua Commodity Index: 3.26%, China Bond Total Wealth Index: 0.51%[25] 2. Model Name: Macro Momentum Model - **Model Construction Idea**: The model uses multiple dimensions such as economic growth, inflation, interest rates, exchange rates, and risk sentiment to time major asset classes like stocks and bonds[25] - **Model Construction Process**: - The model constructs macro momentum indicators based on economic growth, inflation, interest rates, exchange rates, and risk sentiment[25] - These indicators are used to time investments in major asset classes[25] - Example formula: $ \text{Momentum Score} = \sum_{i=1}^{n} w_i \times \text{Indicator}_i $ where $w_i$ is the weight of the $i$-th indicator and $\text{Indicator}_i$ is the value of the $i$-th indicator[25] - **Model Evaluation**: The model is effective in timing investments based on macroeconomic conditions[25] - **Model Test Results**: - February 2025 returns: CSI 300: 3.19%, China Bond Total Wealth Index: 0.08%, Shanghai Gold Exchange AU9999 contract: 6.43%[25] Model Backtest Results 1. Macro Timing Model - **Weekly Return**: -0.12%[32] - **Monthly Return**: 0.36%[32] - **Year-to-Date Return**: -0.31%[32] - **Annualized Volatility**: 2.71%[32] - **Maximum Drawdown**: 0.51%[32] - **Sharpe Ratio**: -0.95[32] 2. Macro Momentum Model - **Weekly Return**: -0.20%[32] - **Monthly Return**: 0.18%[32] - **Year-to-Date Return**: 0.15%[32] - **Annualized Volatility**: 1.50%[32] - **Maximum Drawdown**: 0.47%[32] - **Sharpe Ratio**: 0.87[32] Quantitative Factors and Construction Methods 1. Factor Name: PB Profitability - **Factor Construction Idea**: The factor selects stocks based on their price-to-book (PB) ratio and profitability metrics[38] - **Factor Construction Process**: - Stocks are ranked based on their PB ratio and profitability metrics[38] - The top-ranked stocks are selected for the portfolio[38] - Example formula: $ \text{PB Profitability Score} = \frac{\text{Net Income}}{\text{Book Value}} $ where $\text{Net Income}$ is the company's net income and $\text{Book Value}$ is the company's book value[38] - **Factor Evaluation**: The factor is effective in selecting stocks with high profitability relative to their book value[38] 2. Factor Name: High Dividend Yield - **Factor Construction Idea**: The factor selects stocks based on their dividend yield[38] - **Factor Construction Process**: - Stocks are ranked based on their dividend yield[38] - The top-ranked stocks are selected for the portfolio[38] - Example formula: $ \text{Dividend Yield} = \frac{\text{Annual Dividends}}{\text{Stock Price}} $ where $\text{Annual Dividends}$ is the total dividends paid annually and $\text{Stock Price}$ is the current stock price[38] - **Factor Evaluation**: The factor is effective in selecting stocks with high dividend yields[38] Factor Backtest Results 1. PB Profitability Factor - **Weekly Return**: 0.09%[39] - **Monthly Return**: 0.36%[39] - **Year-to-Date Return**: 0.38%[39] - **Annualized Volatility**: 2.63%[39] - **Maximum Drawdown**: 1.82%[39] - **Sharpe Ratio**: -0.44[39] 2. High Dividend Yield Factor - **Weekly Return**: 0.03%[39] - **Monthly Return**: 0.15%[39] - **Year-to-Date Return**: 0.01%[39] - **Annualized Volatility**: 2.34%[39] - **Maximum Drawdown**: 1.39%[39] - **Sharpe Ratio**: -0.64[39]