Modeling Exchange Rates With Neural Networks

Jamal, A. M. M.; Sundar, Cuddalore

doi:10.19030/jabr.v14i1.5723

Cited by 17 publications

(4 citation statements)

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“…In her seminal paper, Rossi (2013) reviewed the methodologies and fundamentals proposed in the literature up to that point, and found that the predictability of exchange rates was conditional on several aspects. In spite of the the results of Meese and Rogoff (1983), who found that exchange rates could not be predicted on the basis of past exchange rate changes, recent advances in predictive techniques are refining exchange rate forecasts, thereby reopening the debate regarding the unpredictability of exchange rates (Alvarez-Diaz, 2008;Caporale & Spagnolo, 2004;Clements & Smith, 2001;Enders & Pascalau, 2015;Fernández-Rodríguez et al, 2004;Gharleghi et al, 2014;Gradojevic & Yang, 2006;Hong & Lee, 2003;Jamal & Sundar, 2011;Kiani & Kastens, 2008;Kirikos, 2000;Lee & Chen, 2006;Lin et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

An application of deep learning for exchange rate forecasting

et al. 2022

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Section: Literature Reviewmentioning

confidence: 99%

An application of deep learning for exchange rate forecasting

et al. 2022

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“…As examples, Wilson and Sharda (1994) used a neural network model to predict bankruptcy. Jamal and Sundar (1998) modeled currency exchange rates with neural networks. New product adoption (Parry, Cao, & Song, 2011) and sales forecasting (Lua, Leeb, & Lianc, 2012) are more recent business applications of ANNs.…”

Section: Review Of the Literaturementioning

confidence: 99%

U.S. Counties’ Vulnerability to Methamphetamine Labs

Dalmadge

Cain

2014

Journal of Drug Issues

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This national study analyzes county-level risk factors for methamphetamine manufacture. Neural network and probit models are used to test the effectiveness of county-level characteristics in predicting methamphetamine production levels. Data on all 3,143 counties are drawn from the U.S. DEA's Clandestine Laboratory Surveillance System, the 2000 U.S. Census and health service resources from the 2004 Area Resource File, and the Uniform Crime Reporting Program (UCRP) for the period 2002-2005. The resulting model accurately predicted methamphetamine production levels 85% of the time. The leading variables were existing methamphetamine problems, seizures in contiguous counties, families with "female head of household," home value, and "percentage of White population." Several variables that factored heavily in earlier single-community studies had very little impact in this national study. This study's results suggest a new approach to assessing community vulnerability to drug manufacturer and a need to refocus efforts in fighting the problem.

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“…AMM. Jamal (2011) applies the neural network model to forecast bilateral exchange rates between U.S. and Germany and U.S. and France. The predictions from the neural network model were compared to those based on a standard econometric model.…”

Section: Introductionmentioning

confidence: 99%

Exchange Rate Model Approximation, Forecast and Sensitivity Analysis by Neural Networks, Case Of Iran

Pedram

Ebrahimi

2014

Business and Economic Research

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This paper investigates the model estimation and data forecasting of exchange rate using artificial neural network. Recent studies have shown the classification and prediction power of the neural networks. It has been demonstrated that a neural network can approximate any continuous function. Here, in a technical approach, it has been used ARIMA and neural network for a short-term forecast of daily USD to Rial exchange rate. ANN is employed in training and learning processes and thereafter the forecast performance measured making use of two common loss functions. The comparison demonstrates that neural network is far better than ARIMA, the error is about the half.Thereafter, in a fundamental approach via another neural network the effects of some of the most important economic variables on exchange rate prediction in a long-term sense are studied. By sensitivity analysis, the importance and the weight of each economic variable on exchange rate has determined. The results show that it is possible to estimate a model to forecast the value of exchange rate even by having access to a limited subset of data.

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Modeling Exchange Rates With Neural Networks

Cited by 17 publications

References 0 publications

An application of deep learning for exchange rate forecasting

An application of deep learning for exchange rate forecasting

U.S. Counties’ Vulnerability to Methamphetamine Labs

Exchange Rate Model Approximation, Forecast and Sensitivity Analysis by Neural Networks, Case Of Iran

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