Prediction of long-term government bond yields using statistical and artificial intelligence methods

Abstract

This chapter investigates the use of different artificial intelligence and classical techniques for forecasting the monthly yield of the US 10-year Treasury bonds from a set of four economic indicators. The task is particularly challenging due to the sparseness of the data samples and the complex interactions amongst the variables. At the same time, it is of high significance because of the important and paradigmatic role played by the US market in the world economy. Four data-driven artificial intelligence approaches are considered: a manually built fuzzy logic model, a machine learned fuzzy logic model, a self-organising map model, and a multi-layer perceptron model. Their prediction accuracy is compared with that of two classical approaches: a statistical ARIMA model and an econometric error correction model. The algorithms are evaluated on a complete series of end-month US 10-year Treasury bonds yields and economic indicators from 1986:1 to 2004:12. In terms of prediction accuracy and reliability, the best results are obtained by the three parametric regression algorithms, namely the econometric, the statistical, and the multi-layer perceptron model. Due to the sparseness of the learning data samples, the manual and the automatic fuzzy logic approaches fail to follow with adequate precision the range of variations of the US 10-year Treasury bonds. For similar reasons, the self-organising map model performs unsatisfactorily. Analysis of the results indicates that the econometric model has a slight edge over the statistical and the multi-layer perceptron models. This suggests that pure data-driven induction may not fully capture the complicated mechanisms ruling the changes in interest rates. Overall, the prediction accuracy of the best models is only marginally better than the prediction accuracy of a basic one-step lag predictor. This result highlights the difficulty of the modelling task and, in general, the difficulty of building reliable predictors for financial markets. © 2014 Springer International Publishing Switzerland.