Forecasting Models for Malaysia KLCI - Price Index Based on Advanced Statistical Models vs. SC Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F15%3A86096476" target="_blank" >RIV/61989100:27510/15:86096476 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting Models for Malaysia KLCI - Price Index Based on Advanced Statistical Models vs. SC Models

Popis výsledku v původním jazyce

We evaluate statistical and machine learning methods for predicting Malaysia KLCI - Price Index and consider the accuracy of forecasting models based on statistical (stochastic), machine learning methods and an intelligent methodology based on soft or granular computing. In this paper, we apply the ARIMA-ARCH methodology on the developing forecast models and compare their forecast accuracy with the class of RBF neural network models. We found that it is possible to enhance forecast accuracy and also achieve significant risk reduction in managerial decision-making by applying intelligent forecasting models based on latest information technologies. In a comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.

Název v anglickém jazyce

Forecasting Models for Malaysia KLCI - Price Index Based on Advanced Statistical Models vs. SC Models

Popis výsledku anglicky

We evaluate statistical and machine learning methods for predicting Malaysia KLCI - Price Index and consider the accuracy of forecasting models based on statistical (stochastic), machine learning methods and an intelligent methodology based on soft or granular computing. In this paper, we apply the ARIMA-ARCH methodology on the developing forecast models and compare their forecast accuracy with the class of RBF neural network models. We found that it is possible to enhance forecast accuracy and also achieve significant risk reduction in managerial decision-making by applying intelligent forecasting models based on latest information technologies. In a comparative study is shown that the presented approach is able to model and predict high frequency data with reasonable accuracy and more efficient than statistical methods.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.20.0296" target="_blank" >EE2.3.20.0296: Výzkumný tým pro modelování ekonomických a finančních procesů na VŠB-TU Ostrava</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Financial Management of Firms and Financial Institutions : 10th international scientific conference : 7th - 8th September 2015, Ostrava, Czech Republic : proceedings. [Part I - IV]

ISBN

978-80-248-3865-6

ISSN

2336-162X

e-ISSN

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Počet stran výsledku

7

Strana od-do

740-746

Název nakladatele

VŠB - Technical University of Ostrava

Místo vydání

Ostrava

Místo konání akce

Ostrava

Datum konání akce

7. 9. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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