relation: http://eprints.imtlucca.it/1636/
title: Application of Feed-Forward Neural Networks Smoothing Transition Autoregressive Models in Stock Returns Forecasting 
creator: Giovanis, Eleftherios
subject: HB Economic Theory
subject: HD28 Management. Industrial Management
description: In this paper we propose and examine new approaches in smoothing transition autoregressive (STAR) models. Firstly, a new STAR function is proposed, which is the hyperbolic tangent sigmoid function. Secondly, we propose Feed-Forward Neural Networks Smoothing Transition Autoregressive (FFNN-STAR) models. We examine the stock returns of US S&P 500, FTSE-100 in UK stock index, DAX index in Germany and CAC-40 in France and we apply bootstrapping ordinary least squares simulated regressions, while also GARCH models with bootstrapping simulations can be applied as well. The results are in favor of neural networks, while in almost all cases the forecasting performance of Feed-Forward Neural Networks STAR models is superior to conventional STAR models. This paper can be a guide and set up the fundamentals for further advanced research in econometrics and time-series analysis. 
publisher: Nova Science Publishers
date: 2010
type: Article
type: PeerReviewed
identifier:   Giovanis, Eleftherios  Application of Feed-Forward Neural Networks Smoothing Transition Autoregressive Models in Stock Returns Forecasting.  Journal of Computational Optimization in Economics and Finance, 2 (1).  pp. 25-44.  ISSN 1941-3971      (2010)  
relation: https://www.novapublishers.com/catalog/product_info.php?products_id=19560