Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy

Ensemble of temporal convolutional and long short-term memory neural networks apply to forecasting USDCOP exchange rate

This paper applies a neural network with ensemble of temporal convolutional network (TCN) and long short-term memory (LSTM) layers approach to forecast foreign exchange rates between the US dollar (USD) and Colombian Peso (COP) and obtain a better performance. This study provides evidence on the TCN and LSTM neural network model’s effectiveness and efficiency in forecasting temporal series. It should contribute positively to developing theory, methodology, and practice of using an artificial neural network to develop a forecasting model for financial temporal series

DESIGN OF EXPERIMENT PADA ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI KURS TUKAR MATA UANG IDR/USD

Prediksi kurs tukar mata uang memiliki peranan penting dalam bisnis, salah satunya dalam hal international purchasing. Terdapat beberapa metode yang digunakan dalam melakukan forecasting mata uang, salah satunya adalah Artificial Neural Network (ANN). Berdasarkan beberapa penelitian terdahulu, ANN terbukti superior dibandingkan metode forecasting lainnya dalam memprediksi kurs tukar mata uang. Salah satu kelemahan ANN adalah tidak adanya setting parameter yang baku untuk digunakan, sehingga setting parameter yang berbeda dapat memberikan hasil akurasi yang berbeda. Penelitian ini bertujuan menentukan dua nilai parameter yang memiliki pengaruh signifikan, yaitu jumlah input node dan jumlah hidden node melalui metode design of experiment dalam memprediksi kurs tukar mata uang IDR/USD. Bobot awal dan bias pada replikasi yang memberikan performansi lebih baik dari replikasi sebelumnya disimpan untuk selanjutnya digunakan dalam membangkitkan forecast pada periode selanjutnya. Hasil penelitian menunjukkan bahwa delapan input nodes dan empat hidden nodes memberikan akurasi terbaik yang ditandai dengan nilai MSE test terendah. Selain itu, berdasarkan grafik perilaku MSE test dari setiap arsitektur jaringan yang terbentuk, dapat disimpulkan bahwa dalam memprediksi kurs tukar mata uang IDR/USD, jumlah hidden nodes bersifat lebih sensitif dibanding jumlah input nodes.   Abstract Design of Experiment in Artificial Neural Network to Forecast Foreign Exchange Rate IDR/USD]. Forecasting foreign exchange rate plays a significant role in business, for example in international purchasing. There are several methods used in forecasting foreign exchange rates, one of them is the Artificial Neural Network (ANN). Based on several earlier literatures, ANN has been proven as a superior method in forecasting foreign exchange rate compared to other methods. However, ANN has several weaknesses, for example, there is no standard parameters setting used in ANN, thus different parameters setting could lead to different accuracy. This research aims to determine two crucial parameters that give significant impact to the ANN model built; the number of input nodes and the number of hidden nodes, through the design of an experiment to forecast the IDR/USD exchange rate. Initial weights and bias in replication that give better performance than earlier replication are stored and used to forecast the data for next periods as needed. The result of this research shows that eight input nodes and four hidden nodes give the best accuracy to forecast IDR/USD exchange rate which is proven by the lowest MSE test score. Moreover, based on the MSE test behavior graph, the number of hidden nodes is more sensitive than the number of input nodes in forecasting IDR/USD exchange rate. Keywords: Artificial Neural Network; design of experiment; forecast; foreign exchange rat

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

Automated ANN alerts : one step ahead with mobile support

In this paper, I examine the potential of mobile alerting services empowering investors to react quickly to critical market events. Therefore, an analysis of short-term (intraday) price effects is performed. I find abnormal returns to company announcements which are completed within a timeframe of minutes. To make use of these findings, these price effects are predicted using pre-defined external metrics and different estimation methodologies. Compared to previous research, the results provide support that artificial neural networks and multiple linear regression are good estimation models for forecasting price effects also on an intraday basis. As most of the price effect magnitude and effect delay can be estimated correctly, it is demonstrated how a suitable mobile alerting service combining a low level of user-intrusiveness and timely information supply can be designed

Using Recurrent Neural Networks To Forecasting of Forex

This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO. Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed.Comment: 23 pages, 13 figure

Forecasting the Polish Zloty with Non-Linear Models

The literature on exchange rate forecasting is vast. Many researchers have tested whether implications of theoretical economic models or the use of advanced econometric techniques can help explain future movements in exchange rates. The results of the empirical studies for major world currencies show that forecasts from a naive random walk tend to be comparable or even better than forecasts from more sophisticated models. In the case of the Polish zloty, the discussion in the literature on exchange rate forecasting is scarce. This article fills this gap by testing whether non-linear time series models are able to generate forecasts for the nominal exchange rate of the Polish zloty that are more accurate than forecasts from a random walk. Our results confirm the main findings from the literature, namely that it is dificult to outperform a naive random walk in exchange rate forecasting contest.exchange rate forecasting, Polish zloty, Markov-switching models, artificial neural networks

Forecasting The Exchange Rate Series With Ann: The Case Of Turkey

As it is possible to model both linear and nonlinear structures in time series by using Artificial Neural Network (ANN), it is suitable to apply this method to the chaotic series having nonlinear component. Therefore, in this study, we propose to employ ANN method for high volatility Turkish TL/US dollar exchange rate series and the results show that ANN method has the best forecasting accuracy with respect to time series models, such as seasonal ARIMA and ARCH models. The suggestions about the details of the usage of ANN method are also made for the exchange rate of Turkey.Activation function, ARIMA, ARCH, Artificial neural network, Chaotic series, Exchange rate, Forecasting, Time series

Proceedings of the Conference on Human and Economic Resources

Recent development of information technologies and telecommunications have given rise to an extraordinary increase in the data transactions in the financial markets. In large and transparent markets, with lower transactions and information costs, financial participants react more rapidly to changes in the profitability of their assets, and in their perception of the risks of the different financial instruments. In this respect, if the rapidity of reaction of financial players is the main feature of globalized markets, then only advanced information technologies, which uses data resources efficiently are capable of reflecting these complex nature of financial markets. The aim of this paper is to show how the new information technologies affect modelling of financial markets and decisions by using limited data resources within an intelligent system. By using intelligent information systems, mainly neural networks, this paper tries to show how the the limited economic data can be used for efficient economic decisions in the global financial markets. Advances in microprocessors and software technologies make it possible to enable the development of increasingly powerful systems at reasonable costs. The new technologies have created artificial systems, which imitate people’s brain for efficient analysis of economic data. According to Hertz, Krogh and Palmer (1991), artificial neural networks which have a similar structure of the brain consist of nodes passing activation signals to each other. Within the nodes, if incoming activation signals from the others are combined some of the nodes will produce an activation signal modified by a connection weight between it and the node to which it is linked. By using financial data from international foreign exchange markets, namely daily time series of EUR/USD parity, and by employing certain neural network algorithms, it has showed that new information technologies have advantages on efficient usage of limited economic data in modeling. By investigating the “artificial” works on modeling of international financial markets, this paper is tried to show how limited information in the markets can be used for efficient economic decisions. By investigating certain neural networks algorithms, the paper displays how artificial neural networks have been used for efficient economic modeling and decisions in global F/X markets. New information technologies have many advantages over statistics methods in terms of efficient data modeling. They are capable of analyzing complex patterns quickly and with a high degree of accuracy. Since, “artificial” information systems do not make any assumptions about the nature of the distribution of the data, they are not biased in their analysis. By using different neural network algorithms, the economic data can be modeled in an efficient way. Especially if the markets are non-linear and complex, the intelligent systems are more powerful on explaining the market behavior in the chaotic environments. With more advanced information technologies, in the future, it will be possible to model all the complexity of the economic life. New researches in the future need a more strong interaction between economics and computer science.neural networks,knowledge, information technology, communication technology