2012
DOI: 10.1002/isaf.332
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Linear Relationship Between the Aud/Usd Exchange Rate and the Respective Stock Market Indices: A Computational Finance Perspective

Abstract: SUMMARY In the recent era, computational intelligence techniques have found an increased popularity in addressing varied financial issues, including foreign exchange rate prediction. This article, through an intelligent system research framework, relates the Australian dollar (AUD)/US dollar (USD) exchange rate to the Australian and the US stock market indices. Information for exchange rate, All Ordinaries Index (AOI) and Dow Jones Industrial Average (DJI) for the trading days over the period January 1991–May … Show more

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Cited by 5 publications
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“…While most existing works adopt models that linearly relate forecasts to predictors, research has also pointed to linear models' limitations and suggested the effectiveness of nonlinear prediction approaches over a long horizon (Huber, 2016). Another work highlights that a linear model predicts exchange rates well over a short horizon when equity indices are considered (Imam, Tickle, Ahmed, & Guo, 2012). Pavlidis, Paya, and Peel (2012) note that while a nonlinear model outperforms random walk and autoregressive models for $/Franc exchange rate, a linear model outperforms the nonlinear model for $/Sterling exchange rates.…”
Section: Introductionmentioning
confidence: 99%
“…While most existing works adopt models that linearly relate forecasts to predictors, research has also pointed to linear models' limitations and suggested the effectiveness of nonlinear prediction approaches over a long horizon (Huber, 2016). Another work highlights that a linear model predicts exchange rates well over a short horizon when equity indices are considered (Imam, Tickle, Ahmed, & Guo, 2012). Pavlidis, Paya, and Peel (2012) note that while a nonlinear model outperforms random walk and autoregressive models for $/Franc exchange rate, a linear model outperforms the nonlinear model for $/Sterling exchange rates.…”
Section: Introductionmentioning
confidence: 99%
“…In practice, homogeneous ensemble systems are easy and simple to design as finding the optimal combination of different classifiers is not required since only one classifier is employed in an ensemble framework. In general, ensemble models were found to be effective in classification problems (Albanis & Batchelor, ; Leung, Chen, & Mancha, ; Imam, Tickle, Ahmed, & Guo, ; Sun, ; Davalos, Leng, Feroz, & Cao, ; Lahmiri & Boukadoum, , , b; Lahmiri, ).…”
Section: Introductionmentioning
confidence: 99%