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]