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The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name ‘Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.

Huge amount of liquidity flows into a number of financial instruments such as stocks, commodities, currencies, futures, and so on every day. Investment decisions are mainly based on predicting the future movements of the instrument(s) in question. However, high frequency financial data are somewhat hard to model or predict. It would be valuable information for the investor if he or she knew which financial instruments were quantitatively more predictable. The data used in the model consisted of intraday frequencies covering the period between 1993 and 2013. An Artificial Neural Network model using Radial Basis Functions containing only past data of three different types of instruments (stocks, currencies, and commodities) to predict future high values on six different frequencies was applied. A total of 72 different artificial neural networks representing 12 different instruments were trained five times each, and their prediction performances were recorded on average. Considerably clear distinctions were observed on prediction performances of different financial instruments.