Hybrid wavelet adaptive neuro-fuzzy tool supporting competitiveness and efficiency of predicting the stock markets of the Visegrad Four countries
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Hybrid wavelet adaptive neuro-fuzzy tool supporting competitiveness and efficiency of predicting the stock markets of the Visegrad Four countries
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Hybrid wavelet adaptive neuro-fuzzy tool supporting competitiveness and efficiency of predicting the stock markets of the Visegrad Four countries
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50202 - Applied Economics, Econometrics
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Competitiveness
ISSN
1804-171X
e-ISSN
—
Svazek periodika
15
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
CZ - Česká republika
Počet stran výsledku
16
Strana od-do
56-72
Kód UT WoS článku
000971315400010
EID výsledku v databázi Scopus
2-s2.0-85159075756