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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

63
views

0
downloads

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

Suggestions

OpenMETU
Core

Forecasting direction of exchange rate fluctuations with two dimensional patterns and currency strength Özorhan, Mustafa Onur; Toroslu, İsmail Hakkı; Department of Computer Engineering (2017) This thesis presents a method to predict the direction and magnitude of movement of currency pairs in the foreign exchange market. The method uses clustering and classification methods with a combination of two dimensional chart patterns, processed price data and technical indicator data. The input data is adapted to each trading day with a moving time-frame. The accuracy of the prediction models are tested across several different currency pairs. The experimental results suggest that using two dimensional ...
An Empirical model of the international cost of equity Uzunkaya, Mehmet; Küçükkaya, Halit Engin; Department of Business Administration (2015) The aim of the study is to propose an empirical model of the international cost of equity by investigating and analyzing the long-run relation between disaggregated country risk ratings and country stock market index returns for a large panel of countries. The study tests the hypothesis that, given the available theoretical and empirical evidence, country risk ratings and country stock market index returns should move together in the long-run and there should be a long-run equilibrium between them; thus cou...
An affine term structure model for Turkish interest rate swap market: do swaps span volatility risk? Özbekler, Ali Genca; Uğur, Ömür; Department of Financial Mathematics (2019) We follow novel procedure of [4] to assess presence of unspanned stochastic volatility (USV) phenomenon in the Turkish lira interest rate swap (IRS) market. The estimations reveal that IRS yield curve dynamics fail to span volatility in IRS market and thus volatility risk cannot be hedged using only IRS instruments. The major implication of USV is then used to investigate the systemic volatility in domestic markets. In this scope, we employ USV condition as a speciﬁcation for afﬁne term structure (AFTS) mod...
A STOCHASTIC APPROACH TO MODEL HOUSING MARKETS: THE US HOUSING MARKET CASE YILMAZ, BİLGİ; Kestel, Sevtap Ayşe (2018-12-01) This study aims to estimate the price changes in housing markets using a stochastic process, which is defined in the form of stochastic differential equations (SDEs). It proposes a general SDEs system on the price structure in terms of house price index and mortgage rate to establish an effective process. As an empirical analysis, it applies a calibration procedure to an SDE on monthly S&P/Case-Shiller US National Home Price Index (HPI) and 30-year fixed mortgage rate to estimate parameters of differentiabl...
A factor approach to realized volatility forecasting in the presence of finite jumps and cross-sectional correlation in pricing errors Atak Atalık, Alev (2013-08-01) There is a growing literature on the realized volatility (RV) forecasting of asset returns using high-frequency data. We explore the possibility of forecasting RV with factor analysis; once considering the significant jumps. A real high-frequency financial data application suggests that the factor based approach is of significant potential interest and novelty. (C) 2013 Elsevier B.V. All rights reserved.

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.