Authors: Emilian Dobrescu; Dumitru-Iulian Nastac; Elena Pelinescu
Addresses: Romanian Academy, National Institute of Economic Research, Centre for Macroeconomic Modelling, 050711 – Bucureşti, Casa Academiei Române, Calea 13 Septembrie nr. 13, Romania ' Polytechnic University of Bucharest, 060042 – Bucureşti, Splaiul Independenţei nr. 313, Sector 6, Romania ' Romanian Academy, National Institute of Economic Research, Institute for Economic Forecasting, 050711 – Bucureşti, Casa Academiei Române, Calea 13 Septembrie nr. 13, Romania
Abstract: Our purpose is to verify the predictive performances of the artificial neural networks (ANNs) under volatile statistics and possibly incomplete information. Daily forecasts of exchange rate using exclusively primary available information for an emergent economy (such as the Romanian one) could be a proper experimental ground with such a goal. The present paper extends the previous authors' research (Dobrescu et al., 2006; Nastac et al., 2007) on the same issue to improve the accuracy of exchange rate forecasting by using a set of neural predictors in cascade, instead of a single one. The results show that the presented model, despite its proved advantages, could be further improved in order to avoid the translation into residuals of the high serial correlation present in the primary database.
Keywords: exchange rates; process management; financial forecasting; short-term forecasting; ANNs; artificial neural networks; adaptive predictors; cascades; emerging economies; retraining technique; Romania.
International Journal of Process Management and Benchmarking, 2014 Vol.4 No.4, pp.376 - 405
Available online: 28 Oct 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article