Predicting federal funds target rate using neural network

The determination of federal funds target rate is the-most important decision for the Federal Reserve. As it is a complex decision making process, with the exact functions unknown, it is always difficult for a researcher, using traditional statistical methods, to model the thoughts of the FOMC, and hence, predict the changes in the federal funds target rate. However, with neural networks fast evolving as a promising prediction tool, it may finally be able to emulate the FOMCs decision making. In fact, neural networks have already been successfully applied in various business fields, ranging from marketing and tourism to stock picking and derivative applications. The aim of this research is to apply established neural network architectures in the forecasting of changes in the federal funds target rate. The period of study is during the term of Chairman Greenspan, where the Fed emphasizes on a large set of economic time series to make their decisions. A successful application of the neural networks will have widespread benefits to global financial market players.