Testing Currency Predictability Using An Evolutionary Neural Network Model (original) (raw)
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Forecasting daily foreign exchange rates using genetically optimized neural networks
Journal of Forecasting, 2002
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.
An artificial neural network model to forecast exchange rates
2011
For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the exchange rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.
The Usefulness of Artificial Neural Networks in Forecasting Exchange Rates
Academic Journal of Interdisciplinary Studies, 2016
This article contributes to the neural network literature by demonstrating how potent and useful they can be as a tool in the process of economic and financial decision makings. We probe into the usefulness of Nonlinear Autoregressive Networks (NAR) in comparison to the ARIMA models that are commonly used as a benchmark for forecasting exchange rates. To demonstrate it we chose the USD/EUR exchange rate, as a considerably volatile and a highly transacted asset in the international financial market, yet very disputed in academic works due to its often large divergences from the fundamental levels suggested by economic theories. Although through a modest application, our findings show that neural network models can add value and possibly outperform traditional models used to forecast exchange rates. The results were affirmative that the nonlinear autoregressive net consistently beat the ARIMA (and the random walk) static forecasts of the USD/EUR exchange rate.
Forecasting foreign exchange rates using artificial neural networks: a trader's approach
International Journal of Monetary Economics and Finance, 2012
This study investigates the use of two different types of the Artificial Neural Networks (ANNs), Feed-Forward (FF) Neural Network and Nonlinear Autoregressive with Exogenous Input (NARX) neural network, in forecasting the exchange rate of the US dollar against the three major currencies: the Euro, the Pound and the Yen. Although the ANNs technique is not very common in economic discipline, the results are expected to be more accurate in terms of market timing ability and sign prediction than those of the standard econometric techniques such as ARMA. ANNs are, in fact, capable of dealing with high-frequency data as well as the nonlinearities in exchange rate movements. Our results support the notion that ANNs is an effective method in forecasting the exchange rates. The NARX networks output shows a significant market timing ability. Both FF and NARX proved to forecast at a higher accuracy (sign prediction) than random walk and ARMA models.
Foreign exchange rate forecasting by artificial neural networks
APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 11th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’19
Forecasting exchange rates are an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative technique for a forecasting task because of several distinguishing features. Neural networks were originally developed in cognitive science and later were used in engineering for pattern recognition and classification. Neural networks are used because they can model nonlinear behavior in financial markets, in contrast to traditional linear models which are more restrictive. Neural networks can approximate any nonlinear function and are capable of dealing with "noisy" data. In this work, we present an approach of forecasting the exchange rate of the Euro against the US dollar by Nonlinear Autoregressive with Exogenous Input (NARX) Neural Network.It was used the Neural network toolbox of Matlab 2016 software.Different kinds of algorithms and network structures are tested to find the best model for the prediction of foreign currency exchange rates. It is shown how to receive closed price one step ahead.
Exchange rate forecasting using an adaptive neural technique
The paper advances an original artificial intelligence based mechanism for specific economic predictions. The time series under discussion are non-stationary; therefore the distribution of the time series changes over time. The algorithm establishes how a viable structure of an artificial neural network (ANN) at a previous moment of time could be retrained in an efficient manner, in order to support modifications of a complex input-output function of financial forecasting. A "remembering process" for the old knowledge achieved in the previous learning phase is used to enhance the accuracy of the predictions. The results show that the first training (which includes the searching phase for the optimal architecture) always takes a relatively long time, but then the system can be very easily retrained, as there are no changes in the structure. The advantage of retraining procedure is that some relevant aspects are preserved (remembered) not only from the immediate previous training phase, but also from the previous but one phase, and so on. A kind of slow forgetting process also occurs, thus it is much easier for the ANN to remember specific aspects of the previous training instead of the first training. The experiments reveal the high importance of retraining phase as an upgrading/updating process and the effect of ignoring it, also. There has been a decrease in the test error, when successive retraining phases were performed. JEL Classification: C45, C53, F47
A survey on exchange rate prediction using neural network based methods
International Journal of Engineering & Technology
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.
Forecasting Foreign Exchange Rates with Artificial Neural Networks: A Review
International Journal of Information Technology & Decision Making, 2004
Forecasting exchange rates is an important financial problem that is receiving increasing 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, preparing 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 ...
2014
Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92% for predicting a 1000 days’ exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields.
AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2011
Genetic algorithms (GAs) are computer programs that mimic the processes of biological evolution in order to solve problems and to model evolutionary systems. In this study, we apply GAs for technical models of exchange rate determination in exchange rate market. In this framework, we estimated auto regressive (AR), moving average (MA), auto regressive with moving average (ARMA) and mean reversion (MR) as technical models for the Iran's Rial against the European Union's (EU) Euro (Rial/Euro) using monthly data from January 1992 to December 2008. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria; R-squared (R 2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Results showed that for explanation of the Iran's Rial against the European Union's Euro exchange rate behavior, auto regressive (AR) and auto regressive with moving average (ARMA) are better than other technical models.