Core Viewpoint - The article discusses the application of deep learning models, particularly LSTM, in predicting government bond yields, highlighting its advantages in handling complex financial time series data [4][5]. Group 1: Financial Time Series Prediction - Financial time series prediction has evolved through three main stages: traditional econometric models, traditional machine learning models, and deep learning models [4]. - Deep learning models, especially LSTM, are currently among the mainstream methods for financial time series prediction due to their ability to adapt to non-stationary, non-linear, high-noise, and long-memory characteristics [4]. Group 2: LSTM Model for Bond Yield Prediction - A three-layer stacked LSTM model with Dropout regularization was developed to predict the 10-year government bond yield, exploring the application and effectiveness of deep learning in fixed income quantitative analysis [5]. - The model utilized data from early 2021 to December 12, 2025, with approximately 130,000 adjustable parameters, achieving an average absolute error of 1.43 basis points in predictions [5]. - The model predicts a slight decline in the 10-year government bond yield, with a forecasted value of 1.8330% for December 19, 2025, down from 1.8396% on December 12, 2025 [5]. Group 3: Future Optimization Directions - Future optimizations include adjusting the model design regarding time windows, data processing, network architecture, and training strategies [6]. - Expanding input variables to include macroeconomic, market, and sentiment data will enhance the model's predictive accuracy and economic logic [6]. - Combining LSTM with traditional econometric models or other machine learning models to create hybrid models like ARIMAX-LSTM and CNN-LSTM-ATT can improve prediction precision [7].

1. Report Industry Investment Rating No relevant content provided. 2. Core View of the Report The report systematically reviews the evolution of financial time - series forecasting models and constructs a prediction model for China's 10 - year treasury bond yield using a long - short - term memory (LSTM) neural network with historical time series as the single input variable, initially exploring the application of this deep - learning model in the fixed - income quantitative field [10]. 3. Summary by Relevant Catalog 3.1 Financial Time - Series Forecasting and Neural Network Models 3.1.1 Evolution of Financial Time - Series Forecasting Models Financial time - series forecasting has gone through three main development stages: traditional econometric models, traditional machine - learning models, and deep - learning models. Traditional econometric models have clear forms and strong interpretability but struggle to depict nonlinear and complex dynamic relationships. Traditional machine - learning models can perform nonlinear fitting and automatic feature screening but need manual feature extraction. Deep - learning models can automatically extract features from raw data and capture complex long - term time - series patterns, adapting well to the complex characteristics of financial time series [11][12]. 3.1.2 Neural Network Models and LSTM Models Neural network models are machine - learning models imitating the connection structure of human brain neurons. Recurrent neural networks (RNN) and their variants, such as LSTM, are designed for processing sequence data. LSTM solves the long - term dependence problem of traditional RNN through a "gating mechanism" and memory units, enhancing robustness to irregular data and being suitable for bond yield prediction [13][18]. 3.2 Treasury Bond Yield Prediction Based on Stacked LSTM Model 3.2.1 Stacked LSTM Model Stacked LSTM connects multiple LSTM layers in sequence, having advantages in long - sequence processing and multi - dimensional feature extraction, more suitable for complex time - series forecasting in financial scenarios [23]. 3.2.2 Construction of Treasury Bond Yield Prediction Model The report uses a classic and robust architecture of three - layer stacked LSTM + Dropout regularization to build a neural network model for predicting the 10 - year treasury bond yield. It only uses the historical time series of the 10 - year treasury bond yield as a single variable for prediction. The data is from the beginning of 2021 to December 12, 2025. After data processing and sample construction, a medium - complexity LSTM neural network model with about 130,000 adjustable parameters is built. The optimal model is obtained at the 27th training iteration, with an average absolute error of 1.43BP for the test set. The predicted yield on December 19, 2025, is 1.8330%, slightly lower than 1.8396% on December 12, 2025 [2][24][30]. 3.3 Follow - up Optimization Directions - Optimize model design: Adjust and optimize the design related to time windows, data processing, network architecture, and training strategies [3][36]. - Input multi - dimensional variables: Expand input variables from a single yield sequence to multi - dimensional variables such as macro, market, and sentiment to make the model more in line with economic logic and capture more comprehensive information [3][36]. - Build hybrid models: Combine the LSTM model with traditional econometric models or other machine - learning models to build hybrid models like ARIMAX - LSTM and CNN - LSTM - ATT, enhancing prediction accuracy [3][36]. - Introduce a rolling back - testing mechanism: Use a rolling time - window back - testing mechanism to update the model dynamically and make continuous predictions, improving the model's adaptability to market changes [3][36].

1. Report Industry Investment Rating No relevant information provided. 2. Core View of the Report The report systematically reviews the evolution of financial time - series prediction models and constructs a prediction model for China's 10 - year Treasury bond yield using the Long Short - Term Memory (LSTM) neural network, with historical time - series as the single input variable, to explore the application of this deep - learning model in the fixed - income quantitative field [10]. 3. Summary by Directory 3.1 Financial Time - Series Prediction and Neural Network Models - **Evolution of Financial Time - Series Prediction Models**: Financial time - series prediction has gone through three main stages: traditional econometric models (e.g., ARIMA, GARCH), traditional machine - learning models (e.g., SVM, RF), and deep - learning models. Traditional econometric models have clear forms but struggle with nonlinear and complex dynamic relationships. Traditional machine - learning models can perform nonlinear fitting but need manual feature extraction. Deep - learning models can automatically extract features and capture long - term patterns, with RNN and its variants like LSTM being mainstream methods [11][12]. - **Neural Network Models and LSTM Models**: Neural network models mimic the human brain's neuron connection structure. RNN is designed for sequence data but has issues with long - term memory. LSTM solves the long - term dependency problem of RNN through a "gating mechanism" and memory units, enhancing robustness to irregular data. It is suitable for bond yield prediction due to its ability to handle long - term time series and filter noise [13][17][18]. 3.2 Treasury Bond Yield Prediction Based on Stacked LSTM Model - **Stacked LSTM Model**: Stacked LSTM connects multiple LSTM layers sequentially, offering advantages in long - sequence processing and multi - dimensional feature extraction, making it more suitable for complex financial time - series prediction [23]. - **Construction of Treasury Bond Yield Prediction Model**: - **Data Processing and Sample Construction**: The data is the yield of the 10 - year Treasury bond from the beginning of 2021 to December 12, 2025. First - order differences are calculated and standardized. Samples are constructed with the first - order differences of the past 60 trading days as input features and the first - order differences of the next week as the prediction target. The samples are divided into a training set (72%), a validation set (8%), and a test set (20%) [27]. - **Model Design and Evaluation**: The model architecture consists of LSTM, Dropout, and Dense layers. The training strategy involves 200 iterations with an early - stopping mechanism. Evaluation metrics include MSE, MAE, and RMSE [28][29]. - **Model Results**: A medium - complexity LSTM neural network model with about 130,000 adjustable parameters is built. The optimal model is obtained at the 27th iteration, and the early - stopping mechanism is triggered at the 77th iteration. The average absolute error for the test set is 1.43BP. The 10 - year Treasury bond yield is predicted to decline from December 15 - 19, 2025, with the predicted value on December 19, 2025, being 1.8330%, slightly lower than 1.8396% on December 12, 2025 [30]. 3.3 Follow - up Optimization Directions - **Model Design Optimization**: Adjust and optimize relevant designs such as time windows, data processing, network architecture, and training strategies [3][36]. - **Input Multi - Dimensional Variables**: Expand input variables from a single yield sequence to multi - dimensional variables such as macroeconomic, market, and sentiment variables to make the model more in line with economic logic and capture more comprehensive information [3][36]. - **Construct Hybrid Models**: Combine the LSTM model with traditional econometric models or other machine - learning models to build hybrid models like ARIMAX - LSTM and CNN - LSTM - ATT, leveraging different model advantages and improving prediction accuracy [3][36]. - **Introduce Rolling Back - testing Mechanism**: Use a rolling time - window back - testing mechanism to update the model dynamically and make continuous predictions, enhancing the model's adaptability to market changes [3][36].