Yao et al. (1996) also analysed the predictability of the GBP, DEM, JPY, CHF, and AUD against the USD, from 1984 to 1995, but using weekly data. However, they take an ARMA model as a benchmark. Correctness of sign and trading performance were used to evaluate the models.
They conclude that NNR models produce a higher correctness
of sign, and consequently produce higher returns, than ARMA models. In addition, they
state that without the use of extensive market data or knowledge, useful predictions can
be made and significant paper profit can be achieved.
Yao et al. (1997) examine the ability to forecast the daily USD/CHF exchange rate
using data from 1983 to 1995. To evaluate the performance of the NNR model, "buy and
hold" and "trend following" strategies were used as benchmarks. Again, the performance
was evaluated through correctness of sign and via a trading simulation. Essentially, compared
with the two benchmarks, the NNR model performed better and produced greater
paper profit.
Carney and Cunningham (1996) used four data sets over the period 1979 to 1995
to examine the single step and multi step prediction of the weekly GBP/USD, daily
GBP/USD, weekly DEM/SEK (Swedish krona) and daily GBP/DEM exchange rates.
The neural network models were benchmarked by a naive forecast and the evaluation
was based on forecasting accuracy. The results were mixed, but concluded that neural
network models are useful techniques that can make sense of complex data that defies
traditional analysis.
A number of the successful forecasting claims using NNR models have been published.
Unfortunately, some of the work suffers from inadequate documentation regarding
methodology, for example El-Shazly and El-Shazly (1997), and Gen cay (1999). This
makes it difficult to both replicate previous work and obtain an accurate assessment of
just how well NNR modelling techniques perform in comparison to other forecasting
techniques, whether regression based or not.
Notwithstanding, it seems pertinent to evaluate the use of NNR models as an alternative
to traditional forecasting techniques, with the intention to ascertain their potential added
value to this specific application, namely forecasting the EUR/USD exchange rate.
1.3 THE EXCHANGE RATE AND RELATED FINANCIAL DATA
The FX market is perhaps the only market that is open 24 hours a day, seven days a
week. The market opens in Australasia, followed by the Far East, the Middle East and
Europe, and finally America. Upon the close of America, Australasia returns to the market
and begins the next 24 hour cycle. The implication for forecasting applications is that in
certain circumstances, because of time zone differences, researchers should be mindful
when considering which data and which subsequent time lags to include.
In any time series analysis it is critical that the data used is clean and error free since
the learning of patterns is totally data dependent. Also significant in the study of FX time
series forecasting is the rate at which data from the market is sampled. The sampling
frequency depends on the objectives of the researcher and the availability of data.
For example, intraday time series can be extremely noisy and "a typical off floor trader...
would most likely use daily data if designing a neural network as a component of an
overall trading system" (Kaastra and Boyd, 1996: 220). For these reasons the time series
used in this chapter are all daily closing data obtained from a historical database provided
by Datastream.