Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches
As a matter of fact, price prediction for financial underlying assets always been a hot topic in financial fields in recent years with high volatility. With this in mind. this study looks into the usage of machine learning models to predict the yield spread between 10-year and 2-year US Treasury bon...
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04017.pdf |
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| _version_ | 1825206598115524608 |
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| author | Leu Ying Chen |
| author_facet | Leu Ying Chen |
| author_sort | Leu Ying Chen |
| collection | DOAJ |
| description | As a matter of fact, price prediction for financial underlying assets always been a hot topic in financial fields in recent years with high volatility. With this in mind. this study looks into the usage of machine learning models to predict the yield spread between 10-year and 2-year US Treasury bonds (T10Y2Y). Based on the data from the Federal Reserve Economic Data (FRED) database (1976-2024), this study assesses the performance of four different models: multi-layer perceptron (MLP) regression, LSTM, ARIMA model and Facebook Prophet model. Each model’s performance is measured using MAE, MSE, RMSE as well as F2 score. The results show that both MLP regression and LSTM models achieve high accuracy in predicting the yield spread. However, MLP regression outperforms LSTM in terms of producing more reasonable future predictions, particularly over longer time periods. ARIMA and Prophet, while effective for linear forecasts, were confused by the data and made unreasonable and incorrect predictions |
| format | Article |
| id | doaj-art-d3771c47f16b476f9253256b16c74e75 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-d3771c47f16b476f9253256b16c74e752025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700401710.1051/itmconf/20257004017itmconf_dai2024_04017Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple ApproachesLeu Ying Chen0American International School of Cape TownAs a matter of fact, price prediction for financial underlying assets always been a hot topic in financial fields in recent years with high volatility. With this in mind. this study looks into the usage of machine learning models to predict the yield spread between 10-year and 2-year US Treasury bonds (T10Y2Y). Based on the data from the Federal Reserve Economic Data (FRED) database (1976-2024), this study assesses the performance of four different models: multi-layer perceptron (MLP) regression, LSTM, ARIMA model and Facebook Prophet model. Each model’s performance is measured using MAE, MSE, RMSE as well as F2 score. The results show that both MLP regression and LSTM models achieve high accuracy in predicting the yield spread. However, MLP regression outperforms LSTM in terms of producing more reasonable future predictions, particularly over longer time periods. ARIMA and Prophet, while effective for linear forecasts, were confused by the data and made unreasonable and incorrect predictionshttps://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04017.pdf |
| spellingShingle | Leu Ying Chen Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches ITM Web of Conferences |
| title | Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches |
| title_full | Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches |
| title_fullStr | Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches |
| title_full_unstemmed | Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches |
| title_short | Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches |
| title_sort | forecasting the treasury yield spread for fred t10y2y data based on multiple approaches |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04017.pdf |
| work_keys_str_mv | AT leuyingchen forecastingthetreasuryyieldspreadforfredt10y2ydatabasedonmultipleapproaches |