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.
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.