Exchange rate prediction using hybrid neural networks and trading indicators

Ni, He; Yin, Hujun

doi:10.1016/j.neucom.2008.09.023

Cited by 75 publications

(39 citation statements)

References 19 publications

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“…They concluded that in the ANN option-pricing model, the Grey-GJR-GARCH volatility provides higher predictability than other volatility approaches. Other examples of this type of comparisons are done by [220][221][222][223][224][225][226][227][228][229][230][231]. Table 16 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

431

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

431

192

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“…The results obtained show that short-term forecasting method used for prediction gives better accuracy and the method can be used in forecasting time series data using neural networks. Researchers [21][22][23][24] have proposed different ANN models to forecast the currency exchange rates and have concluded that the proposed models have outperformed the conventional model taken for comparison. (Table 1) …”

Section: Annmentioning

confidence: 99%

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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“…We propose the scientific findings and methods of artificial intelligence because most studies have found superior results, especially in stock returns and economic data prediction than the common Logit models and Multiple discriminant Analysis among others (Salchenberger et al 1992, coats and Fant 1993, Zhang et al 1999, Fan and Palaniswami 2000, Brockett et al 2006, Ni and Yin 2009, Giovanis 2010. Thus, economists and financial managers should adopt in their portfolio of research tools the artificial intelligence methods and approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA

Giovanis¹

2012

Economic Analysis and Policy

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Abstract:In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.

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Exchange rate prediction using hybrid neural networks and trading indicators

Cited by 75 publications

References 19 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

A survey on exchange rate prediction using neural network based methods

Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA

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