Forecasting daily foreign exchange rates using genetically optimized neural networks

Abstract

Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non-linear techniques. A number of non-linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non-linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non-linear time series techniques and also fixed-geometry neural network models.

Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model

“…One common hybrid method is one that combines NN and GA. In these applications, a GA is used to optimize several aspects of a NN architecture [2], [6], [53], [58], [64], [70], [74], [89]. The optimized NN is then used to produce the desired forecasts.…”

Timing Foreign Exchange Markets

“…Such attempts have, by and large, met with little success. One class of approaches involves using more sophisticated model selection criteria (see e.g., [7,15,16]); others expand the range of exchange rates studied to include smaller country cross rates with the dollar or another major currency ( [17][18][19]). A more detailed discussion of these papers, none of which manages to beat the random walk at the monthly frequency of greatest interest to the literature, can be found in [6].…”

Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms

“…They find that the return from investment (ROI) from trading decisions based on GP forecasting is greater than ROI from investment decisions based on NNs forecasting in five of the six stocks, though they do not consider risk adjusted returns, such as an information ratio, as this paper does. In addition Nag and Mitra (2002) combined Neural Networks with GP to forecast several exchange rates with impressive results. Likewise Kwon and Moon (2003) combined NNs and GP to trade successfully 36 stocks over a 9 year period.…”