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Journal of Information Science and Engineering, Vol. 25 No. 3, pp. 763-778 (May 2009)

Federal Funds Rate Prediction: A Comparison Between the Robust RBF Neural Network and Economic Models*

Chien-Cheng Lee1, Chun-Li Tsai2 and Yu-Chun Chiang3
 1Department of Communications Engineering
3Department of Mechanical Engineering and Yuan Ze Fuel Cell Center
Yuan Ze University
Chungli, 320 Taiwan
E-mail: {cclee; ycchiang}@saturn.yzu.edu.tw
2Department of Economics
National Cheng Kung University
Tainan, 701 Taiwan
E-mail: tchunli@mail.ncku.edu.tw

Neural network forecasting models have been widely used in the analyses of financial time series during the last decade. This paper attempts to fill this gap in the literature by examining a variety of univariate and multivariate, linear, nonlinear Economics empirical modes and neural network. In this paper, we construct an M-estimator based RBF (MRBF) neural network with growing and pruning techniques. Then we compare the forecasting performances of MRBF with six other time-series forecasting models for daily U.S. effective federal funds rate. The results show that the proposed MRBF network can produce the lowest root mean square errors in one-day-ahead forecasting for the federal funds rate. It implies that MRBF can be one good method for the predictions of some financial time series data.

Keywords: federal funds rate, time series prediction, M-estimator, RBF, growing and pruning

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Received February 1, 2008; accepted November 28, 2008.
Communicated by Yau-Hwang Kuo, Pau-Choo Chung and Jar-Ferr Yang,
* This work was also supported by the National Science Council of Taiwan, R.O.C. under project No. NSC 95-2415-H-006-005.