Group 1: Macroeconomy - The core of the "Great Beautiful Act" signed by Trump includes significant tax cuts for corporations and individuals, reductions in clean energy subsidies, and cuts to Medicaid and SNAP, which will increase the fiscal deficit in the future [3] - The act is projected to boost the actual GDP by less than 0.5 percentage points and has an inflationary impact of no more than 0.15 percentage points by 2026 [3] - Over the next decade, the combination of tariffs and tax cuts is expected to increase the net deficit by approximately $1.3 trillion, maintaining a deficit rate around 6% [3] - Current economic conditions, including low unemployment and moderate inflation, suggest that the U.S. government debt does not face immediate risks [3] Group 2: Strategy - The passage of the "Great Beautiful Act" is anticipated to increase bond supply, which may lead to higher U.S. Treasury yields, potentially affecting market sentiment and stock prices in the short term [7] - Despite short-term liquidity disturbances, the overall credit cycle recovery and the Federal Reserve's interest rate reduction trajectory remain unchanged, providing better buying opportunities for both U.S. stocks and bonds [7] Group 3: Quantitative & ESG - A real-time forecasting model driven by large language models (LLMs) is proposed to address the lag in macroeconomic indicators, allowing for timely adjustments in investment strategies based on economic changes [11] Group 4: Strategy - A forecast for the mid-year report indicates that A-share earnings growth may slow compared to the first quarter, but the second half of the year could see improved performance, particularly in the non-bank financial sector due to high market activity [14] - In the non-financial sector, midstream and upstream companies may face performance pressures due to price impacts, while sectors like gold, consumer upgrades, and tech hardware are expected to show structural strengths [14] Group 5: Food and Beverage Industry - The food and beverage sector is expected to stabilize in demand in the second half of 2025, driven by government policies aimed at boosting consumption and encouraging births [17] - The mass food segment has shown signs of improvement since March, with new consumption trends in snacks and health drinks likely to drive valuation increases in the sector [17] - The liquor sector is currently in a valuation correction phase, but the basic valuation has reflected pessimistic expectations, indicating emerging investment value [17]

Core Viewpoint - The article discusses the development of a real-time forecasting framework driven by large language models (LLMs) to predict macroeconomic indicators, addressing the inherent lag in traditional macroeconomic data collection and reporting processes [1][7]. Group 1: Real-time Forecasting Methods - Macroeconomic indicators typically experience delays due to the time-consuming data collection and validation processes, often resulting in the release of data in the following month or quarter [2][7]. - Three common methods for addressing the lag in macroeconomic data are outlined: 1. **Periodic Lagging Method**: Using previously published data, which is reliable but relies on linear extrapolation [8]. 2. **Dynamic Lagging Method**: Adjusting data based on historical release patterns, which also relies on linear extrapolation [8]. 3. **Real-time Forecasting Method**: Building models for real-time state predictions, which may introduce randomness [8]. Group 2: Specific Forecasting Techniques - The article details various forecasting techniques: 1. **High-Frequency Data Splitting**: Involves using dynamic high-frequency macro data to update low-frequency macro data predictions, exemplified by the GDPNow model. This method is interpretable but requires extensive domain knowledge and may lead to overfitting due to noise in high-frequency data [9]. 2. **SARIMAX Model**: A seasonal autoregressive integrated moving average model that incorporates seasonal parameters and exogenous variables to enhance predictive power. It is suitable for stable, high-frequency indicators with limited external shocks [10][14]. 3. **LLMs for Text Interpretation**: Utilizing LLMs to analyze unstructured text data (e.g., macro news, analyst reports) to generate predictive signals based on semantic relationships and logical reasoning. This method captures market reactions to sudden events more quickly than traditional models [3][15]. Group 3: Performance of Forecasting Models - The effectiveness of real-time forecasting methods is evaluated: 1. **Autoregressive Predictions**: Limited improvement in predictive accuracy for indicators with weak correlation to previous values, such as CPI month-on-month and new RMB loans. Strongly correlated indicators (≥0.8) can simply use lagged data without modeling [4][27]. 2. **LLMs Enhancements**: Significant improvements in predictive accuracy for various indicators when using LLMs, with notable increases in correlation for new RMB loans (from -0.1 to 0.9) and export amounts (from 0.37 to 0.72) [5][35]. Group 4: Conclusion and Recommendations - The article concludes with a recommended approach for real-time forecasting of lagging macroeconomic data: 1. For indicators with high correlation to previous values, use lagged data directly. 2. For stable indicators with weak trends, apply the SARIMAX model with seasonal adjustments. 3. Utilize LLMs in conjunction with news or report data for real-time predictions when other methods are unsuitable [45].