ABSTRACT
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.
1.1 INTRODUCTION
Since the breakdown of the Bretton Woods system of fixed exchange rates in 1971–1973
and the implementation of the floating exchange rate system, researchers have been motivated
to explain the movements of exchange rates. The global FX market is massive with
an estimated current daily trading volume of USD 1.5 trillion, the largest part concerning
spot deals, and is considered deep and very liquid. By currency pairs, the EUR/USD is
the most actively traded.
The primary factors affecting exchange rates include economic indicators, such as
growth, interest rates and inflation, and political factors. Psychological factors also play a
part given the large amount of speculative dealing in the market. In addition, the movement
of several large FX dealers in the same direction can move the market. The interaction
of these factors is complex, making FX prediction generally difficult.
There is justifiable scepticism in the ability to make money by predicting price changes
in any given market. This scepticism reflects the efficient market hypothesis according
to which markets fully integrate all of the available information, and prices fully adjust
immediately once new information becomes available. In essence, the markets are fully
efficient, making prediction useless. However, in actual markets the reaction to new information
is not necessarily so immediate. It is the existence of market inefficiencies that
allows forecasting. However, the FX spot market is generally considered the most efficient,
again making prediction difficult.
Forecasting exchange rates is vital for fund managers, borrowers, corporate treasurers,
and specialised traders. However, the difficulties involved are demonstrated by the fact
that only three out of every 10 spot foreign exchange dealers make a profit in any given
year (Carney and Cunningham, 1996).
It is often difficult to identify a forecasting model because the underlying laws may
not be clearly understood. In addition, FX time series may display signs of nonlinearity
which traditional linear forecasting techniques are ill equipped to handle, often producing
unsatisfactory results. Researchers confronted with problems of this nature increasingly
resort to techniques that are heuristic and nonlinear. Such techniques include the use of
NNR models.