This chapter examines and analyses the use of regression models in trading and investment
with an application to foreign exchange (FX) forecasting and trading models. It is not
intended as a general survey of all potential applications of regression methods to the
field of quantitative trading and investment, as this would be well beyond the scope of
a single chapter. For instance, time-varying parameter models are not covered here as
they are the focus of another chapter in this book and Neural Network Regression (NNR)
models are also covered in yet another chapter.
In this chapter, NNR models are benchmarked against some other traditional regressionbased
and alternative forecasting techniques to ascertain their potential added value as a
forecasting and quantitative trading tool.
In addition to evaluating the various models using traditional forecasting accuracy
measures, such as root-mean-squared errors, they are also assessed using financial criteria,
such as risk-adjusted measures of return.
Having constructed a synthetic EUR/USD series for the period up to 4 January 1999, the
models were developed using the same in-sample data, leaving the remainder for out-ofsample
forecasting, October 1994 to May 2000, and May 2000 to July 2001, respectively.
The out-of-sample period results were tested in terms of forecasting accuracy, and in
terms of trading performance via a simulated trading strategy. Transaction costs are also
taken into account.
It is concluded that regression models, and in particular NNR models do have the ability
to forecast EUR/USD returns for the period investigated, and add value as a forecasting
and quantitative trading tool.