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## An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting

#### Mehdi KHASHEİ [1] , Sheida TORBAT [2] , Zahra HAJİ RAHİMİ [3]

Foreign exchange rates are among the most important economic indices in the international monetary markets. Applying forecasting models for forecasting in exchange rate markets and assisting investment decision making has become more indispensable in business practices than ever before. For large multinational firms, which conduct substantial currency transfers in the course of business, being able to accurately forecast movements of currency exchange rates can result in substantial improvement in the overall profitability of the firm. However, the literature shows that predicting the exchange rate movements are largely unforecastable due to their high volatility and noise and still are a problematic task. Many researches in time series forecasting have argued that predictive performance improves in combined models, especially when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most popular hybrid models categories, which have been shown to be successful for single models. However, they have yielded mixed results in some situations in comparison with components models used separately; and hence, it is not wise to apply them blindly to any type of data. In this paper, an enhanced version of hybrid neural based models is proposed, incorporating the autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) for financial time series forecasting. In proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components models used separately. In additional, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternative model for forecasting in exchange rate markets, especially when higher forecasting accuracy is needed.

Time series forecasting, Hybrid models; Artificial Neural Networks (ANNs), Auto-Regressive Integrated Moving Average (ARIMA), Exchange rate forecasting
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Birincil Dil en Matematik August Articles Yazar: Mehdi KHASHEİ Ülke: Iran Yazar: Sheida TORBAT Yazar: Zahra HAJİ RAHİMİ Yayımlanma Tarihi : 23 Ağustos 2017
 Bibtex @araştırma makalesi { forecasting306822, journal = {Turkish Journal of Forecasting}, issn = {}, eissn = {2618-6594}, address = {Giresun Üniversitesi Fen Edebiyat Fakültesi İstatistik Bölümü, Güre Yerleşkesi, 28100 Merkez, Giresun}, publisher = {Giresun Üniversitesi}, year = {2017}, volume = {01}, pages = {16 - 29}, doi = {}, title = {An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting}, key = {cite}, author = {KHASHEİ, Mehdi and TORBAT, Sheida and HAJİ RAHİMİ, Zahra} } APA KHASHEİ, M , TORBAT, S , HAJİ RAHİMİ, Z . (2017). An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting. Turkish Journal of Forecasting , 01 (1) , 16-29 . Retrieved from https://dergipark.org.tr/tr/pub/forecasting/issue/28688/306822 MLA KHASHEİ, M , TORBAT, S , HAJİ RAHİMİ, Z . "An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting". Turkish Journal of Forecasting 01 (2017 ): 16-29 Chicago KHASHEİ, M , TORBAT, S , HAJİ RAHİMİ, Z . "An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting". Turkish Journal of Forecasting 01 (2017 ): 16-29 RIS TY - JOUR T1 - An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting AU - Mehdi KHASHEİ , Sheida TORBAT , Zahra HAJİ RAHİMİ Y1 - 2017 PY - 2017 N1 - DO - T2 - Turkish Journal of Forecasting JF - Journal JO - JOR SP - 16 EP - 29 VL - 01 IS - 1 SN - -2618-6594 M3 - UR - Y2 - 2017 ER - EndNote %0 Turkish Journal of Forecasting An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting %A Mehdi KHASHEİ , Sheida TORBAT , Zahra HAJİ RAHİMİ %T An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting %D 2017 %J Turkish Journal of Forecasting %P -2618-6594 %V 01 %N 1 %R %U ISNAD KHASHEİ, Mehdi , TORBAT, Sheida , HAJİ RAHİMİ, Zahra . "An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting". Turkish Journal of Forecasting 01 / 1 (Ağustos 2017): 16-29 . AMA KHASHEİ M , TORBAT S , HAJİ RAHİMİ Z . An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting. TJF. 2017; 01(1): 16-29. Vancouver KHASHEİ M , TORBAT S , HAJİ RAHİMİ Z . An Enhanced Neural-based Bi-Component Hybrid Model for Foreign Exchange Rate Forecasting. Turkish Journal of Forecasting. 2017; 01(1): 29-16.

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