Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Research output: Contribution to journalArticle

Abstract

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modellingapproaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organisingmodelling methods for the daily prediction of the exchangerate market. We also propose acombinedapproach where the parametric and nonparametricself-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchangerates: the American Dollar and the Deutche Mark against the British Pound.

LanguageEnglish
Pages12001-12011
Number of pages11
JournalExpert Systems with Applications
Volume36
Issue number10
DOIs
Publication statusPublished - Dec 2009

Fingerprint

Neurons
Neural networks
Financial markets

Keywords

  • neural networks with active neurons
  • forecasting
  • exchange rates
  • GMDH

Cite this

@article{7f0da2c488e74a1ea92201e45e0170ff,
title = "Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach",
abstract = "Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modellingapproaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organisingmodelling methods for the daily prediction of the exchangerate market. We also propose acombinedapproach where the parametric and nonparametricself-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchangerates: the American Dollar and the Deutche Mark against the British Pound.",
keywords = "neural networks with active neurons, forecasting, exchange rates, GMDH",
author = "Leonidas Anastasakis and Neil Mort",
year = "2009",
month = "12",
doi = "10.1016/j.eswa.2009.03.057",
language = "English",
volume = "36",
pages = "12001--12011",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier",
number = "10",

}

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach. / Anastasakis, Leonidas; Mort, Neil.

In: Expert Systems with Applications, Vol. 36, No. 10, 12.2009, p. 12001-12011.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

AU - Anastasakis, Leonidas

AU - Mort, Neil

PY - 2009/12

Y1 - 2009/12

N2 - Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modellingapproaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organisingmodelling methods for the daily prediction of the exchangerate market. We also propose acombinedapproach where the parametric and nonparametricself-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchangerates: the American Dollar and the Deutche Mark against the British Pound.

AB - Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modellingapproaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organisingmodelling methods for the daily prediction of the exchangerate market. We also propose acombinedapproach where the parametric and nonparametricself-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchangerates: the American Dollar and the Deutche Mark against the British Pound.

KW - neural networks with active neurons

KW - forecasting

KW - exchange rates

KW - GMDH

UR - http://www.scopus.com/inward/record.url?scp=69249231010&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2009.03.057

DO - 10.1016/j.eswa.2009.03.057

M3 - Article

VL - 36

SP - 12001

EP - 12011

JO - Expert Systems with Applications

T2 - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 10

ER -