【中泰研究丨晨会聚焦】固收吕品：节后还会延续修复吗？ 证券研究报告/晨会聚焦 2026 年 02 月 24 日 分析师：戴志锋 执业证书编号：S0740517030004 Email：daizf@zts.com.cn 欢迎关注中泰研究所订阅号 晨报内容回顾 1、《【中泰研究丨晨会聚焦】银行戴 志锋：银行 2025 年 4 季度经营数据： 净息差保持平稳，净利润增速转正》 2026-02-23 2、《【中泰研究丨晨会聚焦】宏观张 德礼：中国出口份额还有多大提升空 间？》2026-02-11 3、《【中泰研究丨晨会聚焦】固收吕 品：多空互加筹码，债市迎来"验牌 时刻"》2026-02-09 今日预览 【固收】吕品：节后还会延续修复吗？ 在过去一个多月，债市意外的走出了修复的行情，10 年下破 1.8%，TL 也站上 112.8 关键阻力位。 回顾年初以来的债市行情，基本多数品种都较 1 月初高点下行了 10BP 以上，5 年、7 年国债收益 率下行 14bp、13bp，7 年、10 年二永更是下行 20BP 以上。 我们在一月下旬就提示本轮修复行情，并在二月初《避险资产不避险，债市风景独好？》中提示 本轮 10 ...

美国银行（Bank of America）指出，美股走弱可能会对债市产生重大风险。策略师表示，如果再平衡资 金流减弱，债市的一个重要需求来源将会萎缩。 本周的市场可能会因就业增长、CPI数据以及大量财报的发布而产生更多火花。 过去几年，美股涨势如虹。但随着道琼斯指数突破50000点大关，向上的动能可能会逐渐消失。对此， 由Eleanor Xiao领导的美国银行利率策略师团队发布了一份有趣的研究报告。他们发现，自2021年以来 的美股上涨为债市带来了大量资金流入，这是由于投资者需要将投资组合重新平衡至所谓的"60/40平衡 型配置"（即60%的股票和40%的债券）。 根据计算，资产每增加10万亿美元，投资组合每月就会卖出约370亿美元的股票，并买入同等金额的固 定收益资产，包括美国国债、企业债和抵押贷款支持证券。 来源：金十数据 美国银行最新报告揭露了一个连锁反应，当美股涨势熄火，竟然会直接引爆债市需求端的结构性"贫 血"！过去5年，那股支撑市场的神秘力量正在悄然撤退…… 本周的市场可能会因就业增长、CPI数据以及大量财报的发布而产生更多火花。 这听起来数额巨大，事实也确实如此。据他们测算，自2021年以来，这 ...

Macro Environment - The central economic work conference held on December 10-11 emphasized counter-cyclical and cross-cyclical adjustments, indicating a stable macro policy for 2026, focusing on structure and efficiency [10] - The manufacturing PMI for December was 50.1%, up 0.9 percentage points from the previous month, indicating an acceleration in manufacturing activities [12] - The non-manufacturing business activity index rose to 50.2%, returning to the expansion zone [12] Market and Industry Performance - In December, the bond market faced pressure, with the ten-year main contract down 0.05% and the thirty-year bond down 2.66% [50] - The equity market favored growth styles, with the advanced manufacturing sector rising by 5.97% and technology (TMT) by 4.55% [51] - The top five performing industries in December were defense and military (17.22%), non-ferrous metals (13.68%), and telecommunications (12.06%) [59] Monthly Allocation Recommendations - For January 2026, the report suggests focusing on technology sectors (such as electrical equipment and semiconductors), resource products, and high-dividend sectors due to ongoing policy support and a favorable liquidity environment [70]

Summary of Conference Call Notes Industry Overview - The discussion primarily revolves around the bond market dynamics and the challenges faced by the financial sector, particularly in relation to the supply and demand of bonds as the year-end approaches [2][3][4]. Key Points and Arguments 1. **Market Conditions**: The bond market is experiencing significant volatility as it approaches year-end, with a notable lack of the traditional December rally seen in previous years. The relationship between interest rates and equities has weakened [2][3]. 2. **Supply and Demand Issues**: There is a growing concern regarding the supply-demand imbalance in the bond market. Factors contributing to this include insufficient insurance company support and banks' inability to absorb long-term bonds [2][3][4]. 3. **Insurance Sector Dynamics**: The insurance sector is undergoing structural changes, with a shift towards dividend insurance products, which now account for 40% of new insurance policies. This trend is expected to rise to 50% next year, impacting the demand for long-term bonds [3][4]. 4. **Banking Sector Concerns**: Banks are reassessing their balance sheets as year-end approaches, leading to potential instability in asset-liability management. The pressure to meet year-end reporting standards is influencing their bond purchasing behavior [4][5]. 5. **Long-term Bond Issuance**: The issuance of long-term bonds has been increasing rapidly, but the growth in premium income from insurance has not kept pace, leading to a mismatch in the market [6][10]. 6. **Market Sentiment**: There is a prevailing sentiment of caution among investors, with many adopting a defensive posture in light of the current market conditions. The expectation of a weak bond market is influencing investment strategies [11][12]. 7. **Liquidity Concerns**: The relationship between liquidity and asset stability is highlighted, with a need for stable liquidity injections to restore balance in the market. The current liquidity situation is described as unstable, affecting trading dynamics [12][15]. 8. **Interest Rate Dynamics**: The yield spread between different bond maturities is under scrutiny, with the current 30-10 year spread reaching 40 basis points, reflecting a return to levels seen in 2022. There is speculation about potential adjustments in the yield curve [12][14]. 9. **Future Outlook**: The market is expected to face continued challenges, with concerns about the sustainability of the current yield levels and the potential for further adjustments in bond issuance strategies [15][16]. Other Important Insights - The discussion emphasizes the need for a flexible approach to investment strategies, particularly in light of the current market volatility and the shifting dynamics between equities and bonds [11][12]. - The impact of external factors, such as global interest rate trends and inflation, is acknowledged as a potential influence on future bond market performance [14][15]. - The importance of understanding the underlying frameworks that govern bond market behavior is stressed, particularly in the context of changing investor sentiment and market expectations [11][12].

Summary of Key Points from the Conference Call Industry or Company Involved - The discussion revolves around asset correlation and its impact on investment strategies, particularly focusing on the bond and equity markets. Core Insights and Arguments 1. **Asset Correlation and Portfolio Returns** - Asset correlation significantly contributes to portfolio returns, especially under daily rebalancing, where negative correlation reduces volatility and enhances geometric mean returns. However, strong trends in assets may weaken the negative contribution, necessitating trend-based optimization in allocation [1][2][4]. 2. **Risk Parity Strategy** - The risk parity strategy should account for risk premiums arising from asset correlations to optimize weight allocation, improving the Calmar ratio and Sharpe ratio. The importance of correlation in pricing should not be underestimated for better allocation outcomes [1][5]. 3. **Diversification Benefits** - Increasing asset diversity can effectively lower maximum drawdowns. In stock-bond combinations, a low equity position shows a symmetrical effect, similar to financial products using a low proportion of convertible bonds and stocks to achieve long-term net value growth while controlling drawdowns [1][7][8]. 4. **Modeling Bond Yields with Correlation** - Asset correlation serves as a crucial feature in modeling single asset returns. Incorporating stock-bond correlation significantly enhances predictive accuracy, outperforming models that rely solely on bond characteristics [1][9]. 5. **Sampling Frequency for Correlation Calculation** - The calculation of asset correlation should consider sampling periods and frequencies, with weekly data being optimal for balancing noise and information. Tail dependency risks should also be monitored using Copula methods [1][10]. 6. **Statistical Significance of Stock-Bond Correlation** - The statistical significance of the negative correlation between stocks and bonds requires careful assessment, especially in the context of self-correlation factors that may distort results [1][11]. 7. **Tail Dependency Risk in Strategies** - Tail dependency risk, particularly in stock price movements, should be observed and characterized using Copula methods, as sudden changes in liquidity can lead to significant shifts in asset correlations [1][12]. 8. **Impact of Macroeconomic Factors** - The relationship between stocks and bonds is influenced not only by absolute inflation levels but also by the uncertainty of inflation and economic growth. Liquidity indicators effectively capture market liquidity stress and stock volatility changes [1][29][30]. 9. **Future Outlook for 2026** - The correlation between stocks and bonds in 2026 is expected to be influenced by macroeconomic policies and liquidity changes, with a recommendation for diversified investment strategies to manage potential volatility [1][34]. Other Important but Possibly Overlooked Content 1. **Market Indicators** - The development of high-frequency market indicators, such as interbank liquidity and volatility measures, provides insights into asset correlations and market conditions [1][31][32]. 2. **Historical Correlation Trends** - Historical data shows a notable negative correlation between stocks and bonds since 2018, with varying influences from inflation, liquidity, and institutional behaviors across different economic cycles [1][15]. 3. **Convertible Bonds and Stock Correlation** - Convertible bonds exhibit a strong positive correlation with underlying stocks, particularly when their valuation is at moderate levels, influenced by market conditions and investor behavior [1][17][20]. 4. **Gold's Relationship with A-Shares** - Gold has shown weak correlation with A-shares and A-class assets, which is significant for risk parity strategies as it aids in effective risk diversification [1][21]. 5. **REITs and A-Shares Correlation** - REITs have recently shown a negative correlation with A-shares, primarily due to the current investor structure focusing on fixed income rather than growth expectations [1][24]. This summary encapsulates the essential insights and findings from the conference call, highlighting the importance of asset correlation in investment strategies and the need for adaptive approaches in response to market dynamics.

Summary of Key Points from Conference Call Industry Overview - The focus is on the bond market and its relationship with the stock market, highlighting the current weak sentiment in the bond market and the factors influencing it [1][2][4]. Core Insights and Arguments - **Correlation Between Stock and Bond Markets**: The correlation is not constant; when the stock market adjusts, the bond market does not necessarily follow. This indicates that additional capital is needed to support bond yields, rather than relying solely on trading expectations [2][4]. - **Current Yield Range**: The trading range for yields is currently between 1.70% and 1.80%, with a central tendency around 1.75%. This range is influenced by market sentiment and trading strategies [2][4]. - **Policy Expectations**: There are no significant changes in the fundamental outlook, making policy expectations a focal point for traders. Potential new policies, such as anti-involution measures and relaxed real estate policies, could influence market sentiment [2][4]. - **Impact of Shenzhen's Policy Changes**: The relaxation of purchase restrictions in Shenzhen is seen as a symbolic move that may prompt other cities to follow suit. However, the overall impact on the market is expected to be limited and more emotional than structural [5]. Important but Overlooked Content - **Liquidity Concerns**: The banking sector faces significant liquidity pressures due to a large volume of maturing certificates of deposit (CDs) and the need for open market operations to manage these pressures. The central bank's potential actions, such as interest rate cuts and liquidity injections, are critical to monitor [3][6][7]. - **Central Bank's Bond Purchase Strategy**: While not deemed absolutely necessary, the central bank's resumption of bond purchases could alleviate issuance pressures and signal a more positive outlook. The focus will be on whether the central bank will buy bonds of varying maturities [8][9]. - **Mixed Investment Products**: The relationship between stock and bond markets is complex, with mixed investment products affecting capital flows. When stocks perform poorly, these products may face redemption pressures, impacting the bond market negatively [10]. - **Key Monitoring Points**: Important factors to watch include the liquidity pressures faced by large banks, the progress of government bond transactions, and the redemption trends of mixed investment products, all of which will influence asset allocation strategies [11].

Group 1: Report Industry Investment Rating - No information provided Group 2: Core Viewpoints of the Report - The bond market may gradually recover in an oscillatory and progressive manner as the correlation between stocks and bonds weakens and commodity pressure eases, but other markets, seasonal factors, and regulatory policies may cause oscillations during the recovery process. It is recommended to adopt a dumbbell - shaped operation, and long - term bond rates may decline more smoothly in the second half of the fourth quarter, with rates expected to hit new lows this year [4][6][18] Group 3: Summary by Relevant Content Bond Market Performance This Week - This week, both long - term and short - term bonds remained oscillating. The active bonds of 10 - year and 30 - year treasury bonds, 250011.IB and 2500002.IB, changed by - 1.25bps and 0.95bps respectively compared with last week, reaching 1.77% and 2.03%. After the month - end, the capital price remained loose, and the 1 - year AAA certificate of deposit stayed at around 1.67%. Credit interest rates declined slightly, with the 3 - year and 5 - year AAA - secondary capital bonds falling by 1.7bps and 1.9bps respectively compared with last week, reaching 1.92% and 2.05% [1][9] Weakening Impact of the Stock and Commodity Markets on the Bond Market - The impact of the stock and commodity markets on the bond market has gradually weakened. The 10 - day moving correlation coefficient between the daily interest rate change of the 30 - year active bond and the increase of the Shanghai Composite Index dropped from around 0.8 in late July to around 0.15 currently. On one hand, it is due to the change in bond institutional positions; on the other hand, the relative cost - effectiveness of bonds compared with stocks has gradually increased. Since the end of July, the commodity price index has continued to decline, and the Nanhua Industrial Product Price Index on September 4th has cumulatively dropped by 6.3% compared with the high on July 25th [2][10] Factors Protecting the Bond Market - The loose capital and banks' under - allocation are the main protections for the bond market. The fundamentals are still under pressure, the demand is not strong, and the financing demand is insufficient, so the loose capital situation remains unchanged. The future asset supply will further decline, and the net financing of government bonds in the next 4 months may significantly decrease compared with the same period last year. For banks, the deposit growth rate is rising while the credit growth rate is slowing down, so banks need to increase bond allocation to make up for the gap, and they may have a high willingness to increase allocation [3][10] Reasons for the Oscillatory and Progressive Recovery of the Bond Market - Other markets still impact the bond market. Although the seesaw effect between stocks and bonds has weakened, non - banks still hold a relatively high position in long - term bonds, and a significant rise in the stock market may lead to institutional selling and short - term bond market fluctuations. Seasonal factors may restrict the downward speed of interest rates. September is often a period of interest rate adjustment, and October is an oscillatory period. The new regulations on public fund redemption fees may reduce institutional willingness to invest in bond funds, and the redemption behavior may bring short - term adjustment pressure to the market [4][14][17]

- The report references the Fama-MacBeth method to simulate macro risk factors, transforming the logic of configuring macro risks through asset allocation into a logic of configuring macro risks by configuring assets[1][12][18] - Real macro factor data uses forecast values of relevant monthly macro indicators or asset monthly returns (interest rates/credit), performing univariate time series regression with each asset to obtain risk loadings, and applying a half-life weighting to historical loadings to smooth out instability caused by asset volatility[1][18][22] - The macro factor risk is decomposed into underlying asset portfolios to construct a macro factor risk parity portfolio[1][18][22] - The optimization results of risk parity for macro factors show certain economic growth elasticity, with both returns and volatility higher than those based on asset risk parity[2][39] - The report also discusses the factors influencing stock-bond correlation, referencing AQR's approach, which decomposes stock-bond correlation into economic growth volatility, inflation volatility, and the correlation between economic growth and inflation[3][42][43] - The study finds that economic growth volatility negatively contributes to stock-bond correlation, while interest rate volatility positively contributes, and the correlation between economic growth and interest rates is a positive contributing variable in domestic asset research[3][42][48] - Adding the inflation level factor further improves the explanatory power, with domestic data showing that the inflation level is a significant positive variable for stock-bond correlation[3][48][51] - Using a three-year historical window to calculate the coefficients of each variable, the study combines real values and consensus forecast data to calculate the change in stock-bond correlation for the next month, showing that the estimated and predicted values have the same trend and consistent signs with the real values[3][48][54] Quantitative Models and Construction Methods 1. **Model Name**: Macro-Factor Mimicking - **Construction Idea**: Transform the logic of configuring macro risks through asset allocation into configuring macro risks by configuring assets[1][12][18] - **Construction Process**: - Use forecast values of relevant monthly macro indicators or asset monthly returns (interest rates/credit) - Perform univariate time series regression with each asset to obtain risk loadings - Apply a half-life weighting to historical loadings to smooth out instability caused by asset volatility - Decompose macro factor risk into underlying asset portfolios to construct a macro factor risk parity portfolio[1][18][22] - **Formula**: $$ r_{t}=\alpha_{t}+B\cdot f_{t}+\varepsilon_{t} $$ $$ \Sigma=B\cdot F\cdot B^{T}+E $$ $$ \sigma_{P}{}^{2}\ =\ w^{T}\cdot\Sigma\ \cdot w=\ (w^{T}\cdot B)\cdot F\cdot(B^{T}\cdot w)+w^{T}\cdot E\cdot w $$ $$ \%\text{RC}\ =(w^{T}\cdot B)_{i}\cdot\frac{\partial\sigma_{P}}{\partial(w^{T}\cdot B)_{i}}/\sigma_{P}=\frac{(w^{T}\cdot B)_{i}\cdot(F\cdot(B^{T}\cdot w))_{i}}{w^{T}\cdot\Sigma\ \cdot w} $$ where B is the time-series calculated risk loadings, f is the factor returns, Σ is the asset risk matrix, and F is the macro factor return risk matrix[23][24] - **Evaluation**: The optimization results of risk parity for macro factors show certain economic growth elasticity, with both returns and volatility higher than those based on asset risk parity[2][39] Model Backtest Results 1. **Macro-Factor Mimicking Model**: - **Annualized Return**: 9.86% (12-month half-life), 9.46% (no half-life)[29] - **Annualized Volatility**: 9.55% (12-month half-life), 9.44% (no half-life)[29] - **Maximum Drawdown**: -14.30% (12-month half-life), -15.20% (no half-life)[29] - **2016 Return**: 37.24% (12-month half-life), 18.65% (no half-life)[29] - **2017 Return**: 2.17% (12-month half-life), 7.29% (no half-life)[29] - **2018 Return**: -5.02% (12-month half-life), -7.45% (no half-life)[29] - **2019 Return**: 14.61% (12-month half-life), 14.23% (no half-life)[29] - **2020 Return**: 12.20% (12-month half-life), 7.57% (no half-life)[29] - **2021 Return**: 14.63% (12-month half-life), 10.27% (no half-life)[29] - **2022 Return**: 0.36% (12-month half-life), 8.15% (no half-life)[29] - **2023 Return**: 5.41% (12-month half-life), 3.68% (no half-life)[29] - **2024 Return**: 6.83% (12-month half-life), 15.40% (no half-life)[29] - **2025.07.31 Return**: 7.44% (12-month half-life), 11.53% (no half-life)[29]