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

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.

A Review on Recent Advancements in FOREX Currency Prediction

“…Mustafaa Onur Ozorhan et al [61] proposed a model for forecasting the direction and movement of currency rates in the FOREX market. They present a novel approach based on SVM and genetic algorithms for creating a FOREX rate forecasting model.…”

“…SVMs combined with neural networks have never been explored, so leave a future path open for the researchers. The best feature of the SVM is that it can be used as a classifier [58] and a regressor for forecasting [59,61]. The literature review suggested that when a SVM is incorporated with the genetic algorithm the model can yield a greater ROI [58].…”

Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining

“…Özorhan MO et al have employed a hybrid approach based on SVM and GA (Genetic Algorithm) to predict the best currency pair for exchange. Authors have used primary technical indicators for their analysis and found that by mixing the raw data with a technical financial indicator, one is able to achieve more accurate results [26]. Khedr et al have predicted the stock value using news sentiment analysis.…”

Simulation-based forecasting of the real effective exchange rate of the ruble

“…For the forecasting of exchange rates it is proposed to use ARMA models (Rout, Majhi B., Majhi R, Panda , 2014), including continuous ARMA models (Arratia, Cabaña, A., Cabaña, E., 2016), GARCH models and their modifications (Gupta, Kashyap, 2016;Barunik, Krehlik, Vacha, 2016). There are proposals to link the use of ARIMA models with chaos algorithms (Yonghong, Zhiyong, Mingye, 2016), to apply neural networks (Liu, Hou, Liu, 2017; Zhenhua; Zezheng, Chao; 2016), to use the support vector machines and genetic algorithms (Özorhan, Toroslu, Şehitoğlu, 2017), to apply panel data analysis, taking into account macroeconomic indicators and market volatility (Morales-Arias, Moura, 2013) for forecasting exchange rates. It is worth noting the use of simulation, though on the basis of the training method of support vectors (Yuan, 2013) as well as to improve the quality of the forecast using random processes (Moosa, 2013).…”