A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

Date

2017-11-01

Author

Ozorhan, Mustafa Onur
Toroslu, İsmail Hakkı
Şehitoğlu, Onur Tolga

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This paper addresses problem of predicting direction and magnitude of movement of currency pairs in the foreign exchange market. The study uses Support Vector Machine with a novel approach for input data and trading strategy. The input data contain technical indicators generated from currency price data (i.e., open, high, low and close prices) and representation of these technical indicators as trend deterministic signals. The input data are also dynamically adapted to each trading day with genetic algorithm. The study incorporates a currency strength-biased trading strategy which selects the best pair to trade from the available set of currencies and is an improvement over the previous work. The accuracy of the prediction models are tested across several different sets of technical indicators and currency pair sets, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using trend deterministic technical indicator signals mixed with raw data improves overall performance and dynamically adapting the input data to each trading period results in increased profits. Results also show that using a strength-biased trading strategy among a set of currency pair increases the overall prediction accuracy and profits of the models.

Subject Keywords

Forex, Forecasting, Support, Vector, Machines, Genetic, Algorithms, Trend, Deterministic

URI

https://hdl.handle.net/11511/47735

Journal

SOFT COMPUTING

DOI

https://doi.org/10.1007/s00500-016-2216-9

Collections

Department of Computer Engineering, Article

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Citation Formats

M. O. Ozorhan, İ. H. Toroslu, and O. T. Şehitoğlu, “A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms,” SOFT COMPUTING, pp. 6653–6671, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47735.