TY - GEN
T1 - Using Deep Learning Techniques to Predict 10-Year US Treasury Yield
AU - Shu, Lih Chyun
AU - Chou, Ju Kun
N1 - Funding Information:
Acknowledgement. This research was partially supported by the ROC Higher Education SPROUT Project and Center for Innovative FinTech Business Models of National Cheng Kung University.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/21
Y1 - 2021/5/21
N2 - The yield to maturity of United States Treasury securities is a decisive indicator of the economic cycle in the United States, and it is also one of the most critical interest rate references for capital markets worldwide. This study investigates the effectiveness of applying deep learning methods in financial prediction. Specifically, a deep learning model is trained by using the yields of various United States Treasury securities of different maturities to predict the 10-year yield.We collect time series data from the daily yields of United States Treasury securities from January 1990 to November 2018, which are subsequently preprocessed for the establishment of a long short-term memory model. By using this model, we predict the 10-year yield with a resulting mean squared error as low as 0.0063 for the test data sets.
AB - The yield to maturity of United States Treasury securities is a decisive indicator of the economic cycle in the United States, and it is also one of the most critical interest rate references for capital markets worldwide. This study investigates the effectiveness of applying deep learning methods in financial prediction. Specifically, a deep learning model is trained by using the yields of various United States Treasury securities of different maturities to predict the 10-year yield.We collect time series data from the daily yields of United States Treasury securities from January 1990 to November 2018, which are subsequently preprocessed for the establishment of a long short-term memory model. By using this model, we predict the 10-year yield with a resulting mean squared error as low as 0.0063 for the test data sets.
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U2 - 10.1109/ICIST52614.2021.9440560
DO - 10.1109/ICIST52614.2021.9440560
M3 - Conference contribution
AN - SCOPUS:85107955358
T3 - 2021 11th International Conference on Information Science and Technology, ICIST 2021
SP - 545
EP - 552
BT - 2021 11th International Conference on Information Science and Technology, ICIST 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Information Science and Technology, ICIST 2021
Y2 - 21 May 2021 through 23 May 2021
ER -