A comparison between neural networks and chaotic models for exchange rate prediction

Lisi, Francesco; Schiavo, Rosa A.

doi:10.1016/s0167-9473(98)00067-x

Cited by 68 publications

(45 citation statements)

References 26 publications

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“…They took daily data from 17 October 1994 to 18 may 2000 for insample estimation and from 19 May 2000 to 3 July 2001 for outof-sample forecasting of EUP/USD exchange rates which clearly showed that NNR models confirm the potential of accurate forecasting. Lisi et al [7] have compared the performance of neural networks and chaotic models over common data sets and variables. It is an attempt to verify whether they can predict better under the same experimental condition.…”

Section: Annmentioning

confidence: 99%

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A survey on exchange rate prediction using neural network based methods

Barik¹,

Das²,

Mishra³

2018

IJET

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Forecasting exchange rate has always been in demand as it is very important for the international traders to predict how their money will perform against other currencies. So different methods have been applied by the researchers to accurately predict the exchange rates so that it can assist in taking decision while trading. From all the models the Artificial Neural Network (ANN) has given consistent performance in prediction by overcoming the limitations of other models and has outperformed all the models in terms of efficiency. The evolution of ANN is remarkable. In this paper, we have given the performance of different network models used by researchers to predict the exchange rates of major currencies in the future.

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Section: Annmentioning

confidence: 99%

“…Then Random Walk model was formulated. From practical and theoretical standpoints [7] the suitability of this model was criticized. A number of studies indicate that various techniques have been applied to predict the FOREX rate and estimate its volatility.…”

Section: Introductionmentioning

confidence: 99%

A survey on exchange rate prediction using neural network based methods

Barik¹,

Das²,

Mishra³

2018

IJET

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“…These architectures include: multilayer perceptron (MLP) (Lisi and Schiavo, 1999;Faraway and Chatfield, 1998;Stern, 1996;Hill et al, 1996;Lachtermacher and Fuller, 1995;Jayawardena and Fernando, 1995); recurrent networks (Freeman, 1994, section 6.2); radial basis functions (RBF) Hutchinson, 1994); comparison of MLP and RBF Jayawardena et al, 1996).…”

Section: Feed-forward Neural Network Models In Time Series Predictionmentioning

confidence: 99%

“…Lisi and Schiavo (1999) used a FFNN model for predicting European exchange rates. The FFNN model was found to perform as well as the best model, which was a chaos model.…”

Section: Feed-forward Neural Network Models In Time Series Predictionmentioning

confidence: 99%

“…Both the FFNN and chaos models outperformed the classical random walk model for one-step-ahead forecasting of daily exchange rate data. According to Lisi and Schiavo (1999), based on a statistical test, there was no significant difference between FFNN and the chaos models, but both of these models performed significantly better than the traditional random walk model, which is usually the best model for such data. Lachtermacher and Fuller (1995) mention that Farber (1987, 1988) generated two deterministic nonlinear time series, which look chaotic, and found neural networks performed excellently in generating forecasts.…”

Section: Feed-forward Neural Network Models In Time Series Predictionmentioning

confidence: 99%

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Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

2005

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The forecasting capabilities of feed-forward neural network (FFNN) models are compared to those of other competing time series models by carrying out forecasting experiments. As demonstrated by the detailed forecasting results for the Canadian lynx data set, FFNN models perform very well, especially when the series contains nonlinear and non-Gaussian characteristics. To compare the forecasting accuracy of a FFNN model with an alternative model, Pitman's test is employed to ascertain if one model forecasts significantly better than another when generating one-step-ahead forecasts. Moreover, the residual-fit spread plot is utilized in a novel fashion in this paper to compare visually out-of-sample forecasts of two alternative forecasting models. Finally, forecasting findings on the lynx data are used to explain under what conditions one would expect FFNN models to furnish reliable and accurate forecasts.

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Forecasting daily foreign exchange rates using genetically optimized neural networks

Nag

Mitra

2002

Journal of Forecasting

148

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Forecasting currency exchange rates is an important financial problem that has received much attention especially because of its intrinsic difficulty and practical applications. The statistical distribution of foreign exchange rates and their linear unpredictability are recurrent themes in the literature of international finance. Failure of various structural econometric models and models based on linear time series techniques to deliver superior forecasts to the simplest of all models, the simple random walk model, have prompted researchers to use various non-linear techniques. A number of non-linear time series models have been proposed in the recent past for obtaining accurate prediction results, in an attempt to ameliorate the performance of simple random walk models. In this paper, we use a hybrid artificial intelligence method, based on neural network and genetic algorithm for modelling daily foreign exchange rates. A detailed comparison of the proposed method with non-linear statistical models is also performed. The results indicate superior performance of the proposed method as compared to the traditional non-linear time series techniques and also fixed-geometry neural network models.

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A comparison between neural networks and chaotic models for exchange rate prediction

Cited by 68 publications

References 26 publications

A survey on exchange rate prediction using neural network based methods

A survey on exchange rate prediction using neural network based methods

Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

Forecasting daily foreign exchange rates using genetically optimized neural networks

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