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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F18%3A10236389" target="_blank" >RIV/61989100:27510/18:10236389 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s40747-017-0056-6" target="_blank" >http://dx.doi.org/10.1007/s40747-017-0056-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s40747-017-0056-6" target="_blank" >10.1007/s40747-017-0056-6</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Complex &amp; Intelligent Systems

ISSN

2199-4536

e-ISSN

—

Volume of the periodical

4

Issue of the periodical within the volume

2

Country of publishing house

DE - GERMANY

Number of pages

10

Pages from-to

95-104

UT code for WoS article

000432236000002

EID of the result in the Scopus database

—