Group 1 - The report highlights that domestic equity assets performed well in July 2025, with the risk parity strategy achieving a year-to-date return of 2.65% [2][5][20] - The report provides a summary of various asset allocation strategies, indicating that the domestic asset BL strategy 1 and 2 yielded returns of 2.40% and 2.34% respectively, while the risk parity strategy and macro factor-based strategy returned 2.65% and 2.59% respectively [21][41][42] - The report notes that the domestic equity market saw significant gains, with the CSI 1000 index rising by 4.8% and the Hang Seng Index increasing by 2.78% in July [8][9][10] Group 2 - The report discusses the correlation between different asset classes, indicating that the correlation between the CSI 300 and the total wealth index of government bonds was -38.08%, suggesting a potential for diversification [15][16] - The report outlines the performance of various asset allocation models, with the domestic risk parity strategy showing a maximum drawdown of 0.76% and an annualized volatility of 1.46% [41][42] - The macroeconomic outlook suggests downward risks for growth factors, while inflation expectations may stabilize due to recent policy measures [45][47]

- 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]

Group 1 - The report emphasizes that growth has become a resonant factor in the current economic environment, with a focus on selecting factors that are insensitive to economic conditions but sensitive to credit [2][7][9] - The report suggests that the current economic indicators are weak, leading to a preference for growth-oriented stocks in the investment strategy, particularly in the CSI 300 and CSI 1000 indices [2][9][10] - The macroeconomic outlook indicates a potential short-term recovery in economic indicators, with a forecasted slight increase in the economic leading indicators in August 2025 [12][13][14] Group 2 - The liquidity environment is described as relatively stable, with interest rates showing slight increases but remaining below historical averages, indicating a slightly loose liquidity condition [19][20][22] - Credit indicators are noted to be weak, with a decline in credit volume and structure, although the overall credit indicators remain positive [23][24] - The report advocates for an increase in stock allocation, reflecting a positive trend in equity markets, while reducing allocations in other asset classes [2][24][25] Group 3 - The report identifies liquidity as the primary focus of market attention, especially following recent market movements driven by liquidity conditions [26][27] - In terms of industry selection, the report recommends focusing on sectors that are less sensitive to economic fluctuations but more responsive to credit conditions, highlighting industries with growth attributes [4][31][28] - The report lists specific industries with high scores for economic insensitivity and credit sensitivity, including electronics, media, and beauty care, indicating a strategic focus on growth-oriented sectors [28][31]

Core Viewpoint - The report highlights the performance of various domestic asset allocation strategies in May 2025, indicating a mixed performance across different strategies and asset classes, with a notable focus on the risk parity strategy achieving the highest year-to-date return of 1.73% [1][3]. Group 1: Asset Strategy Performance - Domestic Asset BL Strategy 1 recorded a May return of -0.22% and a year-to-date return of 0.96% [1][3]. - Domestic Asset BL Strategy 2 had a May return of -0.1% and a year-to-date return of 1.05% [1][3]. - The Domestic Asset Risk Parity Strategy achieved a May return of 0.29% and a year-to-date return of 1.73% [1][3]. - The Macro Factor-Based Asset Allocation Strategy reported a May return of 0.27% and a year-to-date return of 1.45% [1][3]. Group 2: Major Asset Trends - In May 2025, domestic equity assets performed well, with the Hang Seng Index, CSI 300, and others showing significant gains, while gold experienced a pullback [2]. - The Hang Seng Index rose by 3.96%, CSI 300 by 1.85%, and the total wealth index of corporate bonds by 0.41% [2]. - The South China Commodity Index and SHFE gold saw declines of 2.4% and 1.39%, respectively [2]. - Correlation analysis indicated a -36.97% correlation between CSI 300 and the total wealth index of government bonds over the past year [2]. Group 3: Macroeconomic Insights - As of the end of May 2025, the manufacturing PMI was at 49.5%, indicating a slight improvement in manufacturing sentiment [4]. - The PPI for April showed a year-on-year decline of -2.7%, with expectations for May at -3.17%, indicating ongoing deflationary pressures [4]. - The central bank conducted a MLF operation of 550 billion yuan, net injecting 400 billion yuan to support special bond issuance [4]. - The social financing scale stood at 424 trillion yuan at the end of April 2025, reflecting the credit environment [4].

Group 1: Tactical Asset Allocation - The tactical allocation view for Hong Kong stocks has been upgraded to overweight due to the ongoing enthusiasm for Chinese technology breakthroughs and emerging industries, with international funds increasingly favoring Hong Kong stocks [2][3] - The tactical allocation view for government bonds has been downgraded to neutral, as the imbalance between financing demand and credit supply limits the upward potential of interest rates [3][4] - The tactical allocation view for gold has been upgraded to overweight, as geopolitical tensions and economic recession fears make gold an attractive hedge against risks [3][4] Group 2: Export and Trade Industry Insights - In May, export growth slowed to 4.8% year-on-year, impacted by tariff shocks and high base effects from the previous year, but the resilience of foreign trade remains evident [5][6] - The recent US-China trade talks in London are expected to yield results in terms of tariff reductions and easing of technical restrictions, which could benefit companies with high exposure to the US market [6][7] - Cross-border e-commerce companies are entering a critical period of export acceleration to the US, driven by recent tariff reductions and upcoming sales events [7] Group 3: Automotive Industry Performance - In May, wholesale sales of passenger vehicles increased by 12.8% year-on-year, with a notable rise in new energy vehicle sales, which accounted for 52.6% of total sales [16][17] - The export of passenger vehicles, including new energy vehicles, showed significant growth, with new energy vehicle exports increasing by 80.9% year-on-year [16][17] - The automotive sector is expected to benefit from policies supporting new energy vehicles and the ongoing recovery in consumer demand [16][17] Group 4: Technology and AI Developments - The report highlights Apple's strategy to enhance its AI ecosystem by allowing third-party developers to integrate its foundational models into their applications, strengthening its competitive position [17][18] - The introduction of new features in Apple's iOS, such as real-time translation and visual intelligence, is expected to enhance user experience and drive further adoption of its devices [18][19] Group 5: Industrial Software and Robotics - The company is positioned as a leader in the industrial software sector, with projected revenues of 147.38 billion to 183.68 billion yuan from 2025 to 2027, driven by AI applications in the steel industry [24][25] - The development of humanoid robots and AI solutions is expected to accelerate automation in various industrial sectors, with significant market growth anticipated [25][26] Group 6: Smart Transportation Sector - The smart transportation industry is experiencing high growth driven by policy support and market demand, with significant contracts signed for digital transformation projects [27][28] - The company is expected to see a surge in orders as it capitalizes on opportunities in the smart transportation sector, with a strong pipeline of projects [29]

Group 1 - The report indicates a weakening of profit expectation indicators, leading to a preference for growth-oriented asset allocation. The economic recovery is noted, but the micro mapping shows a shift towards weaker profit expectations, resulting in a focus on factors that are less sensitive to economic changes and more sensitive to credit conditions [4][7][9] - The economic outlook is positioned at the late stage of an upward trend, with expectations of reaching a peak in June 2025 and entering a downward cycle thereafter. Recent PMI data shows a decline, indicating a potential slowdown [11][14] - Liquidity is maintained at a slightly tight level, with short-term interest rates showing a slight decline while long-term rates have decreased more significantly. Overall liquidity indicators remain neutral to slightly tight [24][27] Group 2 - The report suggests reducing commodity positions in the asset allocation strategy, with a slight increase in A-share positions and a minor recovery in US stock positions. The commodity allocation has been reduced to zero [31] - Market focus has shifted towards liquidity, which has become a significant variable influencing market performance, especially following the recent upward trends in September [32] - In terms of industry selection, the report emphasizes choosing sectors that are less sensitive to economic fluctuations but more sensitive to credit conditions, highlighting industries with growth attributes [33]