# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 6 Conclusions  
The U.S. 10-year Treasury bond yield remains a central financial indicator, influencing global investment decisions, interest rate benchmarks, and macroeconomic policy. This study examined its short-term forecasting using a comparative approach across four models: linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP). By integrating key macroeconomic indicators, including the federal funds rate, core CPI, GDP growth, and federal debt growth, we evaluated each model's ability to predict bond yield changes over 1- to 3-month horizons.  
The results highlight the superior performance of the random forest model, which achieved the lowest forecasting error and the highest R\({}^{2}\) value among the models tested. This finding is consistent with previous research demonstrating the effectiveness of ensemble learning in financial forecasting tasks [6,9]. In contrast, the LR model, while interpretable and simple to implement, was less effective in capturing the nonlinear dynamics of bond yields, an observation also supported by [PERSON] and [PERSON] [8].  
This study's results carry several practical and theoretical implications. First, they suggest that machine learning models, particularly ensemble and neural network approaches, offer meaningful improvements over traditional econometric methods for yield forecasting. These models can better accommodate the complexity of modern financial systems, especially in the presence of structural breaks, regime shifts, and nonlinear interactions among economic variables [7].  
Second, the findings support the ongoing integration of artificial intelligence (AI) into financial decision-making systems. Bond traders, risk managers, and institutional investors can incorporate these models into real-time analytics tools to enhance market responsiveness and improve risk-adjusted returns. Moreover, central banks and policymakers may benefit from model outputs as supplementary indicators of market sentiment and yield expectations.  
However, several limitations must be acknowledged. The scope of the model is restricted to a specific set of macroeconomic indicators and a particular bond maturity. Broader inclusion of global economic and sentiment indicators could enrich predictive power. Furthermore, deep learning methods such as LSTM or hybrid models combining econometrics and AI might yield superior performance in long-term forecasting [14].  
In conclusion, this research affirms the viability of machine learning methods for short-term bond yield forecasting, particularly in environments characterized by economic uncertainty and nonlinear dynamics. Future studies should continue exploring the fusion of interpretable AI models and economic theory to build more robust and transparent forecasting systems. As the financial landscape becomes increasingly data-driven, the strategic application of ML offers a compelling path forward for yield modeling and macro-financial analysis.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.