The prediction of FX time series is one of the most challenging problems in forecasting. Our main motivation in this chapter is to determine whether regression models and, among these, NNR models can extract any more from the data than traditional techniques. Over the past few years, NNR models have provided an attractive alternative tool for researchers and analysts, claiming improved performance over traditional techniques. However, they have received less attention within financial areas than in other fields. Typically, NNR models are optimised using a mathematical criterion, and subsequently analysed using similar measures. However, statistical measures are often inappropriate for financial applications. Evaluation using financial measures may be more appropriate, such as risk adjusted measures of return. In essence, trading driven by a model with a small forecast error may not be as profitable as a model selected using financial criteria. The motivation for this chapter is to determine the added value, or otherwise, of NNR models by benchmarking their results against traditional regression based and other forecasting techniques. Accordingly, financial trading models are developed for the EUR/USD exchange rate, using daily data from 17 October 1994 to 18 May 2000 for in sample estimation, leaving the period from 19 May 2000 to 3 July 2001 for out of sample forecasting.
1 The trading models are evaluated in terms of forecasting accuracy and in terms
of trading performance via a simulated trading strategy.
Our results clearly show that NNR models do indeed add value to the forecasting
process. The chapter is organised as follows. Section
1.2 presents a brief review of some of the research in FX markets. Section
1.3 describes the data used, addressing issues such as
stationarity. Section
1.4 presents the benchmark models selected and our methodology. Section
1.5 briefly discusses NNR model theory and methodology, raising some issues
surrounding the technique. Section
1.6 describes the out of sample forecasting accuracy and trading simulation results. Finally, Section
1.7 provides some concluding remarks.
1.2 LITERATURE REVIEW
It is outside the scope of this chapter to provide an exhaustive survey of all FX applications.
However, we present a brief review of some of the material concerning financial
applications of NNR models that began to emerge in the late 1980s.
Bellgard and Goldschmidt (1999) examined the forecasting accuracy and trading performance
of several traditional techniques, including random walk, exponential smoothing,
and ARMA models with Recurrent Neural Network (RNN) models. The research was
based on the Australian dollar to US dollar (AUD/USD) exchange rate using half hourly
data during 1996. They conclude that statistical forecasting accuracy measures do not
have a direct bearing on profitability, and FX time series exhibit nonlinear patterns that
are better exploited by neural network models.
Tyree and Long (1995) disagree, finding the random walk model more effective than the
NNR models examined. They argue that although price changes are not strictly random,
in their case the US dollar to Deutsche Mark (USD/DEM) daily price changes from 1990
to 1994, from a forecasting perspective what little structure is actually present may well
be too negligible to be of any use. They acknowledge that the random walk is unlikely
to be the optimal forecasting technique. However, they do not assess the performance of
the models financially.
The USD/DEM daily price changes were also the focus for Refenes and Zaidi (1993).
However they use the period 1984 to 1992, and take a different approach. They developed
a hybrid system for managing exchange rate strategies. The idea was to use a neural
network model to predict which of a portfolio of strategies is likely to perform best
in the current context. The evaluation was based upon returns, and concludes that the
hybrid system is superior to the traditional techniques of moving averages and meanreverting
processes.
El-Shazly and El-Shazly (1997) examined the one month forecasting performance of
an NNR model compared with the forward rate of the British pound (GBP), German
Mark (DEM), and Japanese yen (JPY) against a common currency, although they do not
state which, using weekly data from 1988 to 1994. Evaluation was based on forecasting
accuracy and in terms of correctly forecasting the direction of the exchange rate. Essentially,
they conclude that neural networks outperformed the forward rate both in terms of
accuracy and correctness.
Similar FX rates are the focus for Gen cay (1999). He examined the predictability of
daily spot exchange rates using four models applied to five currencies, namely the French
franc (FRF), DEM, JPY, Swiss franc (CHF), and GBP against a common currency from
1973 to 1992. The models include random walk, GARCH(1,1), NNR models and nearest
neighbours. The models are evaluated in terms of forecasting accuracy and correctness of
sign. Essentially, he concludes that non parametric models dominate parametric ones. Of
the non parametric models, nearest neighbours dominate NNR models.