Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06

AUTHORS

Dusan Marcek

ABSTRACT

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news. More... »

PAGES

95-104

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40747-017-0056-6

DOI

http://dx.doi.org/10.1007/s40747-017-0056-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091903105


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