Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree

Abstract

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. In this chapter, we apply Higher Order Flexible Neural Trees (HOFNTs), which are capable of designing flexible Artificial Neural Network (ANN) architectures automatically, to forecast the foreign exchange rates. To demonstrate the efficiency of HOFNTs, we consider three different datasets in our forecast performance analysis. The data sets used are daily foreign exchange rates obtained from the Pacific Exchange Rate Service. The data comprises of the US dollar exchange rate against Euro, Great Britain Pound (GBP) and Japanese Yen (JPY). Under the HOFNT framework, we consider the Gene Expression Programming (GEP) approach and the Grammar Guided Genetic Programming (GGGP) approach to evolve the structure of HOFNT. The particle swarm optimization algorithm is employed to optimize the free parameters of the two different HOFNT models. This chapter briefly explains how the two different learning paradigms could be formulated using various methods and then investigates whether they can provide a reliable forecast model for foreign exchange rates. Simulation results showed the effectiveness of the proposed methods.