Financial stability indicator predictability by support vector machines
The result's identifiers
Result code in 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>
Result on the web
<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
—
Alternative languages
Result language
angličtina
Original language name
Financial stability indicator predictability by support vector machines
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
AH - Economics
OECD FORD branch
—
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 30th International Conference Mathematical Methods in Economics
ISBN
978-80-7248-779-0
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
361-366
Publisher name
Silesian University in Opava
Place of publication
Karviná
Event location
Karviná
Event date
Sep 11, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—