分析师：徐至 登记编码：S0730525040001 继续关注科技成长及高股息"哑铃"策略 ——月度策略 相关报告 《月度策略：平稳收官, 高股息防御与科技成 长布局——月度策略》 2025-12-03 《月度策略：增配价值资产，等待成长资产性 价比回归——月度策略》 2025-11-05 《月度策略：均衡配置成长与价值风格，防范 联系人：李智 证券研究报告-月度策略 发布日期：2026 年 01 月 07 日 投资要点： 风险提示：政策及经济数据不及预期，风险事件冲击市场流动性。报 告样本数据有限，历史过往数据不代表未来表现。 本报告版权属于中原证券股份有限公司 www.ccnew.com 请阅读最后一页各项声明 第1页 / 共20页 电话： 0371-65585629 地址： 郑州郑东新区商务外环路10号18楼 地址： 上海浦东新区世纪大道1788号T1座22楼 风格切换——月度策略》 2025-10-09 宏观方面。宏观数据仍指向内需偏弱、通胀走势平稳，12 月举行 的中央经济工作会议定调 2026 年实施"更加积极的财政政策"与 "适度宽松的货币政策"，加大逆周期和跨周期调节力度，并着重 于扩大内需、 ...

会议助理 2: 本会议信息仅供参考，不代表任何投资建议。 吕品 中泰证券固收分析师: 尊敬的各位投资人，大家好。那个，我是中泰固收的吕品，然后跟各位领导汇报一下。我 们对战赏的一个观点吧，然后这个近期市场，年末，这个问题还是比较大的。这个，这一 周，线圈出现了一些修复性的行情。整体看，短端的修复是大于一个长短的，那线圈表现 这个强于期货。临近年末，债市的波动又放得比较大，没有了过去几年大家熟悉的这种 12 月份开门红这种。流畅的利率下行，而且又明显和今年和权益之间的相关关系又失效了， 它跟股票又没什么太大关系了。 四季度，周均的净买入现券是 680 亿。20~30 线圈的净买入规模，在 12 月份比 1 月份 还是多一些的，这个其实是比较符合保险正常的一个 12 月到 1 月的开门红节奏的。还有 一点就是说真的从配置盘的角度来讲，我们其实也很难去让保险去锚定一个 30 年，因为 保险做 30 年，它也是一个交易性的需求，大账或者是一些摊余的产品户，还是以同期限 地方债作为配置性品种。前者，其实我们也做了一些特征，就是说超长，其实保险做超长 的，它随着市场的，随行就市的情况也是比较明显的。 但是，就是说地方债的 ...

如何平视固收+相关性 20251203 我们最近发布了一篇关于资产间相关性的报告，旨在探讨 2026 年资产收益率 预期的差异及其对投资组合的影响。我们注意到，尽管市场普遍关注资产收益 率，但资产间相关性这一点往往被忽视。尤其是今年（2025 年）股债之间深 度负相关的格局是否会在明年（2026 年）发生变化，这一点值得关注。 报告 涵盖了六个部分内容。首先，我们定义了资产相关性对组合的贡献，并通过定 量方法进行整理和分析。其次，我们回顾了大类资产之间的相关性，包括股票、 债券、转债、黄金、美债、美股和 REITs 等。在股债这两类资产中，我们进一 步探讨了期限利差和信用利差与股票收益率之间的关系。 我们还发现，通胀水 平对国债相关性的解释力度并不强，而通胀和经济增长波动对股债相关性的解 股债负相关关系自 2018 年以来较为明显，受通胀、流动性和机构行为 等因素驱动，呈现不同周期特征。2020 年以来，股票表现对未来一个 月资产间关联产生单向影响。 高频市场中，股债相关性受宏观政策和投资者结构影响，呈现状态变化。 流动性指标和沃夫指标能有效刻画市场流动性紧张程度及股票波动变化， 从而解释股债间关系。若流动性 ...

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]