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

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Abstract

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.

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Authors and Affiliations

  1. Computer Engineering Department, Middle East Technical University, Ankara, Turkey

    Mustafa Onur Özorhan, İsmail Hakkı Toroslu & Onur Tolga Şehitoğlu

Authors
  1. Mustafa Onur Özorhan
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  2. İsmail Hakkı Toroslu
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  3. Onur Tolga Şehitoğlu
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Corresponding author

Correspondence to Mustafa Onur Özorhan.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Appendix

Appendix

1.1 Formulae regarding computation of precision, recall, accuracy and F-measure

$$\begin{aligned}&\mathrm{Precision}_\mathrm{positive} =\frac{\hbox {TP}}{\hbox {TP}+\hbox {FP}} \end{aligned}$$
(5)
$$\begin{aligned}&\mathrm{Precision}_\mathrm{negative} =\frac{\hbox {TN}}{\hbox {TN}+\hbox {FN}} \end{aligned}$$
(6)
$$\begin{aligned}&\mathrm{Recall}_\mathrm{positive} =\frac{\hbox {TP}}{\hbox {TP}+\hbox {FN}} \end{aligned}$$
(7)
$$\begin{aligned}&\mathrm{Recall}_\mathrm{negative} =\frac{\hbox {TN}}{\hbox {TN}+\hbox {FP}} \end{aligned}$$
(8)
$$\begin{aligned}&\hbox {Accuracy}=\frac{\hbox {TP}+\hbox {TN}}{\hbox {TP}+\hbox {FP}+\hbox {TN}+\hbox {FN}} \end{aligned}$$
(9)
$$\begin{aligned}&\hbox {F-}\hbox {measure}=\frac{2 \times \hbox {Precision} \times \hbox {Recall}}{\hbox {Precision}+\hbox {Recall}} \end{aligned}$$
(10)

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Özorhan, M.O., Toroslu, İ.H. & Şehitoğlu, O.T. A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms. Soft Comput 21, 6653–6671 (2017). https://doi.org/10.1007/s00500-016-2216-9

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

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