Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

Kiani, Khurshid M.; Kastens, Terry L.

doi:10.1007/s10614-008-9144-4

Cited by 39 publications

(19 citation statements)

References 80 publications

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“…Results showed that multilayer perceptron and learning vector quantization can be considered as the most successful models in predicting the financial failure of banks. Other examples of this type of studies are done by [112,113]. Table 7 presents the brief results of these comparisons.…”

Section: Financial Prediction and Planningmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

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Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.

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Section: Financial Prediction and Planningmentioning

confidence: 99%

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Bahrammirzaee

2010

Neural Comput & Applic

442

192

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“…Yang and Gradojevic [12] construct a neural network that never performs worse than a linear model but always performs better than the random walk model when predicting Canadian dollar/dollar exchange rate. Kiani and Kastens [6] have successfully employed neural networks to forecast the exchange rate. The studies experimenting on forecasting exchange rates so far have not included the data for the period of the current financial meltdown.…”

Section: Introductionmentioning

confidence: 99%

Exchange rate forecasting: comparison of various architectures of neural networks

Dhamija

Bhalla²

2010

Neural Comput & Applic

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This paper evaluates the predictive accuracy of neural networks in forecasting exchange rate. The multilayer perceptron (MLP) and radial basis function (RBF) networks with different architectures are used to forecast five exchange rate time series. The results of each prediction are evaluated and compared according to the networks and architectures used. It is found that neural networks can be effectively used in forecasting exchange rate and hence in designing trading strategies. RBF networks performed better than MLP networks in our simulation experiment. This experiment suggests that it is possible to extract information hidden in the exchange rate and predict it into future.

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“…(2008) use NNs to forecast and trade European options with disappointing results. On the other hand, Kiani and Kastens (2008) forecast the GBP/USD, the CAD/USD and the JPY/USD exchange rates with feedforward and recurrent NNs having as benchmarks several ARMA models. In their application, NNs outperform in statistical terms their ARMA benchmarks in forecasting the GBP/USD and USD/JPY but not in forecasting the USD/CAD exchange rate.…”

Section: Literature Reviewmentioning

confidence: 99%

“…al. (2012)), the RNN (Kiani and Kastens (2008) and Bekiros and Georgoutsos (2008)) and the MLP (Panda andNarasimhan (2007), Adeodato et. al.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

Sermpinis

Theofilatos

Karathanasopoulos

et al. 2013

European Journal of Operational Research

141

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Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

Abstract: Exchange rate forecasts, Feed forward neural networks, Recurrent neural network, In-sample forecasts, Out-of-sample forecasts, ARMA, State space, C32, C45, E37, F31,

Cited by 39 publications

References 80 publications

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

Exchange rate forecasting: comparison of various architectures of neural networks

Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

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