Application of Support Vector Machine to Forex Monitoring

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概要

• 論文の詳細を見る

• Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

• 社団法人 電気学会の論文
• 2004-10-01

著者

• Kamruzzaman Joarder

  Faculty Of Information Technology Monash University

• SARKER Ruhul

  School of IT & EE, University of NSW

• Sarker Ruhul

  School Of Information Technology And Electrical Engineering University Of New South Wales At Adfa

• Sarker Ruhul

  School Of It & Ee University Of Nsw

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