Hybrid wavelet adaptive neuro-fuzzy tool supporting competitiveness and efficiency of predicting the stock markets of the Visegrad Four countries

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F23%3APU148215" target="_blank" >RIV/00216305:26510/23:PU148215 - isvavai.cz</a>

Result on the web

<a href="https://www.cjournal.cz/index.php?hid=clanek&bid=aktualni&cid=475&cp=" target="_blank" >https://www.cjournal.cz/index.php?hid=clanek&bid=aktualni&cid=475&cp=</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7441/joc.2023.01.04" target="_blank" >10.7441/joc.2023.01.04</a>

Result language

angličtina

Original language name

Hybrid wavelet adaptive neuro-fuzzy tool supporting competitiveness and efficiency of predicting the stock markets of the Visegrad Four countries

Original language description

The stock market is influenced by many factors and predicting its development is a difficult and complicated task. Successful and accurate stock market prediction is absolutely essential for countries’ economies as it helps to create a competitive advantage for listed companies through economies of scale. The aim of the paper is to decompose and denoise stock time series using wavelet analysis, detect a smoothed trend and predict future development using an adaptive neuro-fuzzy model. This hybrid fusion model is also referred to as the WANFIS model. The application of the WANFIS model is carried out on less developed stock markets, specifically on the official stock market indices of the Visegrad countries, namely the Czech Republic, Slovak Republic, Poland and Hungary. Recently, wavelet analysis has been among the most promising mathematical tools, which can be used to easily decompose continuous signals or time series in the time and frequency domains. The results show that the proposed WANFIS hybrid model demonstrates a more accurate prediction of the development of stock indices than individual models alone. Experimental results show that the fusion model provides a promising and effective tool for predicting even less liquid and less efficient stock markets, such as those in the V4 countries. A useful and accurate prediction alternative proven in emerging stock markets is offered.

Czech name

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Czech description

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Type

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

CEP classification

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OECD FORD branch

50202 - Applied Economics, Econometrics

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

Journal of Competitiveness

ISSN

1804-171X

e-ISSN

—

Volume of the periodical

15

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

16

Pages from-to

56-72

UT code for WoS article

000971315400010

EID of the result in the Scopus database

2-s2.0-85159075756