Title: Short-term financial forecasting using ANN adaptive predictors in cascade

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.

DOI: 10.1504/IJPMB.2014.065519

International Journal of Process Management and Benchmarking, 2014 Vol.4 No.4, pp.376 - 405

Available online: 28 Oct 2014 *

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