Forecasting the foreign exchange rates with artificial neural networks: a review. Intl J Inf Tech Decis Mak

Department of Management Sciences, City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong
International Journal of Information Technology and Decision Making (Impact Factor: 1.41). 03/2004; 2(3):145-165. DOI: 10.1142/S0219622004000969


Forecasting exchange rates is an important financial problem that is receiving increas-ing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, prepar-ing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

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Available from: Kin Keung Lai, Oct 13, 2015
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    • "However, artificial neural networks (ANNs) are introduced as promising data mining tools that provide an alternative to statistical techniques in building credit scoring models. Furthermore, artificial neural networks have recently been used successfully in different business applications (Akkoc, 2012; Chen and Huang, 2003; Eletter, 2012; Gao et al., 2006; Huang et al., 2004a; Khashman, 2010; Malhorta and Malhorta, 2003; Martens et al., 2007; Tsai and Wu, 2008; West, 2000). "
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    • "Due to their ability of dealing with non-linear systems through data mapping, machine learning has become a popular choice among researchers in various forecasting scenarios. In particular, models based on Neural Networks (Wang, 2004; Chang et al, 2009; Huang et al, 2004) and Support Vector Machines (Huang, 2010; Kamruzzaman et al, 2003; Trafalis, 2006; Brandl et al, 2009; Zhao et al, 2009) achieved accuracy at least at par with predictions from both structural econometric and naive models. In contrast to ANN estimation, the SVM solution derives from convex optimization making the optimal solution both global and unique. "
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    • "Recently, there is an increasing trend on adopting artificial neural networks (ANN) and its variants, in order to explore non-linearites of the financial data [1]. Even with vast amounts of available financial data, identification of underlying patterns has become difficult due to economic, political, environmental, and even psychological factors that affect the fluctuation of exchange rates. "
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    ABSTRACT: Financial forecasting plays a critical role in present economic context where neural networks have become a good alternative technique over traditional methods. Vast ranges of neural models are developed to achieve better accuracy in forecasting. In addition, the ways to find out a good neural architecture is being explored by the research community. In the literature, main problems are figured out within the area of data preparing and neural network design. In this paper, the reasons that affect the performance of the models are discussed based on empirical and mathematical evidence. Finally, this paper presents the directions towards a more suitable neural model for financial forecasting by combining data preprocessing techniques, clustering techniques and support vector machine.
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