Enhanced Symbolic Description in Analyzing Patterns and Volatility on the Forex Market

In this paper, we propose a novel approach for transforming financial time-series values into symbolic representation based on value changes. Such approach seems to have few advantages over the existing approaches; one of the most obvious is noise reduction in the data and another one is possibility to find patterns which are universal for investigating different currency pairs. To achieve the goal we introduce a preprocessing method that allows initial data transformation. We also define a text-based similarity measure which can be used as an alternative for methods allowing to find the exact patterns in the historical data. To effectively evaluate our method, we present a concept that allows to predict the potential price movement direction and compare it with the actual price direction observed in the historical data. Such a method gives an opportunity not only to indicate the different price patterns based on the symbolic representation but also at the same time evaluate the predictive power of such patterns. The proposed approach is experimentally verified on 10 different currency pairs, each covering approximately a period of 10 years.