Using Deep Learning Techniques to Predict 10-Year US Treasury Yield

Lih Chyun Shu, Ju Kun Chou

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題2021 11th International Conference on Information Science and Technology, ICIST 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面545-552
頁數8
ISBN(電子)9781665412667
DOIs
出版狀態Published - 2021 五月 21
事件11th International Conference on Information Science and Technology, ICIST 2021 - Chengdu, China
持續時間: 2021 五月 212021 五月 23

出版系列

名字2021 11th International Conference on Information Science and Technology, ICIST 2021

Conference

Conference11th International Conference on Information Science and Technology, ICIST 2021
國家/地區China
城市Chengdu
期間21-05-2121-05-23

All Science Journal Classification (ASJC) codes

  • 資訊系統與管理
  • 人工智慧
  • 資訊系統
  • 決策科學(雜項)
  • 控制和優化

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