On the Interdependence of Technical Indicators and Trading Rules Based on FOREX EUR/USD Quotations

Abstract

The general aim of this paper is to investigate the interdependence within the wide set (2657) of technical analysis indicators and trading rules based on daily FOREX quotations from 01.01.2004 to 20.09.2018. For the purpose of this paper, we have limited our study to EUR/USD quotations only. The most frequently used methods for FOREX behavior modeling are regression, neural networks, ARIMA, GARCH and exponential smoothing (cf. [1, 3, 6]). They are used to predict or validate inputs. Inputs interdependence may cause the following problems:

• The error term is obviously not normally distributed (for regression it is heteroscedastic). Therefore we lose the main tool for the model validation.

• R-squared becomes a useless measure.

• The obtained model is problematic for forecasting purposes. We would normally like to forecast the probability of a certain set of independent variables to create a certain output - the FOREX observations.

• Regression and neural network methods use the inverses of some matrices. When we use two identical variables as input, the matrix is singular. When we use some dependent variables, the matrix is “almost” singular. It leads to model instability (assuming that computations are possible at all).

• It is meaningless to evaluate which of the two identical inputs is more significant.

Therefore the independence of inputs is a crucial problem for FOREX market investigation. It may be done directly, as shown in this paper, or by means of PCA techniques, where the inputs are mapped into the small set of independent variables. Unfortunately, in the second case, the economical meaning is lost.

The obtained results may be treated as the base for building FOREX market models, which is one of our future goals.