Modeling federal funds target rate using neural networks

The paper aims to find an effective model for predicting US federal funds target rate using neural networks. The model is based on macroeconomic data with fifteen input variables, while the output was the federal funds target rate, used by The Federal Reserve bank to control the monetary stability in the country. Different neural network architectures were tested using multilayer perceptron, radial basis, and generalized regression algorithms. The best neural network model is selected on the basis of generalization ability. Generalization error of all models is examined using 10-fold cross validation resempling. The results show that neural networks are able to incorporate the relationship among input variables with a high accuracy. The created model revealed that artificial intelligence methods have great potential in the area of interest rate predictions and could be used for future research in that area.

Federal funds rate (FFR) is an interest rate determined by the Federal reserve bank (Fed) and used for over-night loans conducted among banks in order to ensure the bank liquidity and enhance its efficiency. It is also used as a baseline for determining other interest rates (short-term and long-term treasury loans, short-term and long-term credits, and mortgage loans. The importance of predicting the federal funds target rate is emphasized by the research of Lobo [8], who found that "target change announcements convey new information to the stock market." According to [8], announcements of a joint target and discount rate change especially influence the risk aversion and the market volatility. Most of the research in financial modeling was focused on parametric methods, while nonparametric methods, such as neural neural networks (NNs) were not investigated enough.
The paper is focused on finding an effective model for predicting the US federal funds target rate using NNs. The dataset covered the period from January 1959 to December 2005. Three NN algorithms were tested: multilayer perceptron, radial basis function network, and general regression network. The best model selection is based on the generalization error, which was estimated by a 10-fold cross-validation procedure.
The rest of the paper describes previous research results, NN methodology used, data, modeling procedure including sampling and cross validation, followed by the results and conclusion.