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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Complex &amp; Intelligent Systems

ISSN

2199-4536

e-ISSN

—

Svazek periodika

4

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

95-104

Kód UT WoS článku

000432236000002

EID výsledku v databázi Scopus

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