美國公債殖利率可以作為預測景氣循環之重要指標，也是全球資本市場最重要的利率參考指標。本研究旨在探討深度學習技術應用於財務金融領域之預測效果，以不同年期之美國公債殖利率使用深度學習模型預測10年期美國公債殖利率。
本研究蒐集1990年1月至2018年11月間各年期美國公債殖利率日資料，建立時間序列資料，透過資料預處理後建立長短期記憶模型(Long Short-Term Memory, LSTM)，依據模型訓練結果預測10年期美國公債殖利率。
本研究實證結果發現，在1個月期公債殖利率到30年期公債殖利率所有變數都加入訓練模型，並在資料長度為20，測試比率為0.2，每次處理128個資料，2層隱藏層，並將資料重複訓練10次，在學習率0.005時MSE為0.0063，最為準確。

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.

This study establishes 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. The model’s training results are used for the prediction of the 10-year yield.

The empirical results of this study indicate that the model is at its most accurate under the following conditions: the yields of securities with maturities that range from 1 month to 30 years are all included in the training model; data length is 20; test ratio is 0.2; 128 data are processed per batch; the model has 2 hidden layers; the training is repeated 10 times; and mean-square error is 0.0063 at the learning rate of 0.005.

翁嘉謙，2014，探討美國政府長短期債券利差於次貸風暴前後期間預測美國經濟成長之解釋性。
陳建德，2014，美國十年期公債殖利率是否為景氣領先指標？。
Ang,Andrew, Monika Piazzesi and Min Wei (2004) What Does the Yield Curve Tell Us about GDP Growth, Journal of Econometrics, 131, 359-403.

Alex Graves et al. (2013) Speech recognition with deep recurrent neural networks , 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Chuah, K. L. (1992) A Nonlinear Approach to Return Predictability in the Securities Markets Using Feedforward Neural Network, Ph.D. dissertation, Washington State University.

Cox, J. C., Ingersoll, J. E., & Ross, S. A. (1985). A theory of the term structure of interest rates.Econometrica,53, 385–407.

Dai, Q., & Singleton, K. (2000). Specification analysis of affine term structure models. Journal of Finance,55, 1943–1978.

Diebold, F. X., & Li, C. (2006). Forecasting the term structure of government bond yields. Journal of Econometrics,130,337–364.

Duddy, S. and Shekhar, S. (1988). Bond Rating: A Non-Conservative Application of Neural Networks, Proc. of the IEEE International Conference on Neural Networks, 443-450.

Duffee , G. (2002). Term premia and interest rates forecasts in affine models. Journal of Finance,57, 405–443.

G. E. Hinton, S. Osindero, Y. W Teh. (2006). A fast learning algorithm for deep belief nets. Neural computation , 18(7),1527-1554

Ho, T. S. Y., & Lee, S.-B. (1986). Term structure movements and pricing interest rate contingent claims. Journal of Finance,41,1011–1029.

J J Hopfield (1982). Neural networks and physical systems with emergent collective computational abilities. PNAS. 79 (8) ,2554-2558

Kamijo, K. and Tanigawa, T. (1990) Stock Price Pattern Recognition: A Recurrent Neural Network Approach, Proc. of the IJCNN, San Diego, Ca, 215-221.

Kimoto, T., and Asakawa, K. (1990) Stock Market Prediction System with Modular Neural Networks, Proc. of the IJCNN, San Diego, Ca, 1-6.

Kunihiko Fukushima (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics , 36(4), 193–202

LeCum Y, Boser B , & Denker J S , et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation , 1(4),541-551

McNown, R. and Wallace, M. S. (1992) Cointegration Tests of a Long-Run Relation between Money Demand and the Effective Exchange Rate, Journal of International Money & Finance 11, 107-114.

MD Odom, R Sharda.(1990) A Neural Network Model for Bankruptcy Prediction. Proc. of the IJCNN, San Diego, Ca,.

Mishkin, F.S (1988) The Information in the Term Structure : Some Future Results. Journal of Applied Econometrics, No.3 , 307-314.

Nelson, C. R., & Siegel, A. (1987). Parsimonious modeling of yield curves. Journal of Business,60(4), 473–489.

S Hochreiter, J Schmidhuber (1997) Long short-term memory. Neural Computation. 9(8), 1735-1780

S Hochreiter et al. (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies.

Sim,ChristopherA. (1992) Interpreting the Macroeconomic Time series Facts : The Effects of Monetary Policy, European Economic review 36 , 975-1011.

Siong Lin Ho et al. (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction, Computers & Industrial Engineering
Volume 42, Issues 2–4, Pages 371-375

T Mikolov et al. (2011) Extensions of recurrent neural network language model, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Wu Tao (2006) Globalization 's Effect on Interest Rates and the Yield Curve , Economic Letter – Insights from the Federal Reserve Bank of Dallas, 1(9).

Yoshua Bengio (2009). Learning Deep Architectures for AI. Foundations and Trends in Machine Learning. 2(1),1-127