Core Viewpoint - Citigroup has slightly updated its year-end yield predictions for U.S. Treasury bonds while maintaining confidence in its long-term forecast for the 10-year Treasury yield, which is expected to reach 4.10% by year-end, consistent with previous predictions [1] Group 1 - Citigroup's new baseline forecast for the 2-year U.S. Treasury yield is set at 3.50% [1] - The 5-year Treasury yield is forecasted to be 3.65% [1] - The 30-year Treasury yield is projected to reach 4.70% [1] Group 2 - The adjustments in predictions are made to align better with expectations of a steeper yield curve and lower policy rates by 2026 [1]

1. Report Industry Investment Rating No industry investment rating is provided in the report. 2. Core Viewpoints of the Report - The prediction of the ten - year Treasury bond yield faces complex challenges from the data, model, and market ends, making high - precision prediction difficult to achieve. - XGBoost is the preferred model for predicting the ten - year Treasury bond yield due to its adaptability to bond market characteristics and technical advantages. - A prediction model for the ten - year Treasury bond yield is constructed using XGBoost, which can provide guidance for investment strategies [3][6][7]. 3. Summary by Related Catalogs 3.1 Deconstructing the Full Process of Treasury Yield Prediction - **Data End**: The factors affecting Treasury bond yields include short - term real interest rates, inflation expectations, and term premiums. The relevant data has a "low signal - to - noise ratio" and limited quantity, which may lead to the model capturing false patterns [6][21][27]. - **Model End**: From traditional machine learning to deep learning models, they have formed a complementary technology matrix. However, traditional machine learning has difficulties in dealing with non - linear relationships, and deep learning has problems such as the "black - box" feature and high data requirements [31][33][43]. - **Market End**: The "financial market uncertainty principle" exists in the financial market. Precise observation of the current state may interfere with long - term trend judgment, and trend prediction turning into collective action will reshape the market [54][55]. - **Coupling Resonance of the Three Ends**: The technical logic of the model relying on historical data and pursuing quantitative accuracy conflicts with the "financial market uncertainty principle", making it difficult to achieve accurate prediction [56]. 3.2 Reasons for Choosing the XGBoost Model - **Existing Model Ecology and Core Challenges**: Current models for predicting long - term Treasury bond yields have limitations. The prediction of the ten - year Treasury bond yield faces challenges such as market non - linearity and low volatility [64][65]. - **Model Foundation**: XGBoost is an efficient implementation of GBDT. Its core advantages include second - order Taylor expansion, sparse perception, feature parallelization, explicit regularization, and column sampling [70][74]. - **Comparison with Deep Learning Models**: XGBoost has advantages in data requirements, feature engineering, training efficiency, and interpretability when dealing with medium - and small - scale tabular data [75]. - **Scenario Adaptability**: XGBoost can output the "rise - fall direction", which is suitable for the practical logic of "direction first". It can efficiently process core indicators and ensure robustness and timeliness [77]. 3.3 Building Logic of the Prediction Model for the Ten - Year Treasury Bond Yield Change - **Data Processing**: The data from 2010 to the present is selected. The data is divided according to the "rolling window" principle. More than 200 indicators are selected, and cleaning and pre - processing are carried out, including extreme value truncation, missing value filling, standardization, and one - hot encoding [83][87][93]. - **Sample Weighting**: A "one - model - for - one - category" strategy is adopted to deal with the problem of category imbalance, improving the prediction accuracy of minority categories [97][98]. - **Parameter Tuning**: A "coarse - tuning + fine - tuning" method is used to balance model complexity and generalization ability, and the optimal parameters are determined [100][102][103]. - **Experience Backup**: When the number of samples predicted to be in a volatile market by the model is less than 50%, the prediction results of samples with the top 50% probability of the volatile market predicted by the volatile - market - specialized model are changed to volatile, reducing extreme risks [104]. 3.4 Model Win - rate Analysis and Guidance for Investment Strategies - **Model Win - rate**: The overall accuracy of the model reaches 92.0%, and the Cohen's kappa coefficient is 78.35%, indicating that the model can effectively capture interest rate change rules [9][115]. - **Investment Strategy Guidance**: When the model predicts that the ten - year Treasury bond yield will go "bullish", it is advisable to lengthen the duration; when it predicts "bearish", shorten the duration; when it predicts a "volatile" market, maintain the current investment portfolio [110][112][113].

Core Viewpoint - Bank of America has revised its U.S. Treasury yield forecasts, reflecting expectations that upcoming economic data will prompt the Federal Reserve to alter its risk assessment [1] Group 1: Yield Forecast Adjustments - The two-year Treasury yield forecast for the end of the year has been lowered from 3.75% to 3.5% [1] - The ten-year Treasury yield forecast has been adjusted to 4.25%, down from a previous prediction of 4.5% [1] Group 2: Federal Reserve Outlook - The bank still anticipates that the Federal Reserve will maintain interest rates unchanged until the second half of next year [1] - Weak labor market data has increased the risk of downward adjustments to interest rates [1]

Summary of the Conference Call on the Bond Market Analysis Industry Overview - The analysis focuses on the Chinese bond market, specifically the ten-year government bond yield predictions for 2025 [2][4]. Key Points and Arguments - **Yield Prediction Model**: The model decomposes the ten-year government bond yield into trend and cycle components, achieving a fitting goodness of 0.98. The predicted yields for June and December 2025 are approximately 1.91% and 1.61%, respectively [2][6]. - **Market Behavior**: The market has shown hesitation around the 1.6% yield level, influenced by macroeconomic data improvements and tightening funds. The model aims to analyze these factors to better understand current yield levels and forecast future trends [4][11]. - **CPI and PMI Correlation**: The relationship between CPI and bond market cycles has changed over time. Before 2013, CPI growth was positively correlated with bond cycles. From 2013 to 2019, PMI data became the key indicator, while post-2020, CPI showed a negative correlation with bond cycles due to monetary policy effects [8][10]. - **Interest Rate Predictions**: The model forecasts other maturities based on the ten-year yield, predicting one-year, three-year, five-year, and seven-year yields to be approximately 0.99%, 1.26%, 1.42%, and 1.59% by December 2025 [10][12]. - **Market Sentiment**: The model serves as a neutral anchor, with actual market values expected to fluctuate around this anchor. Expectations of interest rate cuts may lower yields, while rate hikes could increase them [10][11]. - **Model Reliability**: Backtesting shows the model's fitting deviation is within 10%, indicating its reliability. This deviation can help identify overly optimistic market conditions, aiding investors in adjusting strategies [3][13][15]. Additional Important Insights - **Market Dynamics**: The early 2025 market behavior has led to a pessimistic outlook, with the neutral space being consumed early in the year. A breakthrough below 1.6% will require unexpected market stimuli [11][12]. - **Investment Decision Making**: The model provides a reliable benchmark for assessing market sentiment and potential overvaluation. When market deviations exceed 10%, it signals a need for strategy adjustments [14][16]. - **Future Adjustments**: The model is a tool for judgment and should be used alongside market assessments, especially in the face of unexpected events [7][9]. This comprehensive analysis highlights the dynamics of the Chinese bond market and the predictive capabilities of the model developed by the research team.