Financial stability indicator predictability by support vector machines

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F12%3A10124482" target="_blank" >RIV/00216208:11230/12:10124482 - isvavai.cz</a>

Výsledek na webu

<a href="http://mme2012.opf.slu.cz/proceedings/pdf/062_Ivankova.pdf" target="_blank" >http://mme2012.opf.slu.cz/proceedings/pdf/062_Ivankova.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Financial stability indicator predictability by support vector machines

Popis výsledku v původním jazyce

Support Vector Machines are a successful machine-learning algorithm used for classication, regression and prediction of time series. We optimize learning parameters and select the feature set with the smallest predictive error. We also explore the development of errors for predictions with larger time skips. We've applied the method for the prediction of CISS (Composite Indicator of Systemic Stress), a stability indicator created by the European Central Bank. We've chosen this indicator among other state-of-the-art indicators because of its high frequency, which indicates a quick response to distinctive changes on the market. The results show that CISS can be partially explained just by its past values up to six to eight weeks ahead, but large behaviour shifts are still surprising for the model. We've also discovered that including data over three months of age into the prediction context won't improve the results.

Název v anglickém jazyce

Financial stability indicator predictability by support vector machines

Popis výsledku anglicky

Support Vector Machines are a successful machine-learning algorithm used for classication, regression and prediction of time series. We optimize learning parameters and select the feature set with the smallest predictive error. We also explore the development of errors for predictions with larger time skips. We've applied the method for the prediction of CISS (Composite Indicator of Systemic Stress), a stability indicator created by the European Central Bank. We've chosen this indicator among other state-of-the-art indicators because of its high frequency, which indicates a quick response to distinctive changes on the market. The results show that CISS can be partially explained just by its past values up to six to eight weeks ahead, but large behaviour shifts are still surprising for the model. We've also discovered that including data over three months of age into the prediction context won't improve the results.

Druh

D - Stať ve sborníku

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 30th International Conference Mathematical Methods in Economics

ISBN

978-80-7248-779-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

361-366

Název nakladatele

Silesian University in Opava

Místo vydání

Karviná

Místo konání akce

Karviná

Datum konání akce

11. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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