<div class="csl-bib-body">
<div class="csl-entry">Hämmerle, C. (2019). <i>Vorhersage von Wirtschaftsindikator mittels Sentimentenanalyse von Nachrichtenartikeln und maschinellem Lernen</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.50331</div>
</div>
-
dc.identifier.uri
https://doi.org/10.34726/hss.2019.50331
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/13788
-
dc.description.abstract
The analysis of textual data and their predictive quality has gained the interest of many researchers, especially in the financial domain. This thesis investigates whether newspaper articles contain information to describe the changes of the Austrian Traded Index (ATX). We apply state of the art methods to extract newspaper articles from the online platform of an Austrian newspaper, to perform sentiment analysis of the articles and to build machine learning models in order to predict price and volatility developments of the ATX. As the newspaper articles are written in German, we create a new sentiment lexicon, called German Financial Sentiment Lexicon (GFSL), by extracting sentiments from the SentiWS, a general German sentiment lexicon, and adding financial sentiment words to the lexicon. Our findings show the newspaper articles contain information which allow predictions of price and volatility movements. The GFSL does not clearly outperform the SentiWS lexicon, although in some scenarios it clearly has an advantage over the general lexicon. The results confirm the findings of previous studies such that negative sentiments highly influence the outcome of the model while positive sentiments are hardly relatable to positive development of the index.
en
dc.description.abstract
null
de
dc.language
English
-
dc.language.iso
en
-
dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
-
dc.subject
Sentimentanalysen
de
dc.subject
Rekurrentes neuronales Netz
de
dc.subject
Finanzsystem
de
dc.subject
Nachrichtenartikeln
de
dc.subject
Sentiment analysis
en
dc.subject
Recurrent Neural Network
en
dc.subject
Financial system
en
dc.subject
News
en
dc.title
Vorhersage von Wirtschaftsindikator mittels Sentimentenanalyse von Nachrichtenartikeln und maschinellem Lernen
en
dc.title.alternative
Prediction of an economic indicator using machine learning and sentiment analysis of news articles
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2019.50331
-
dc.contributor.affiliation
TU Wien, Österreich
-
dc.rights.holder
Christoph Hämmerle
-
dc.publisher.place
Wien
-
tuw.version
vor
-
tuw.thesisinformation
Technische Universität Wien
-
dc.contributor.assistant
Rekabsaz, Navid
-
tuw.publication.orgunit
E194 - Institut für Information Systems Engineering
-
dc.type.qualificationlevel
Diploma
-
dc.identifier.libraryid
AC15381189
-
dc.description.numberOfPages
65
-
dc.identifier.urn
urn:nbn:at:at-ubtuw:1-125641
-
dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
-
tuw.assistant.staffStatus
staff
-
tuw.advisor.orcid
0000-0002-7149-5843
-
item.languageiso639-1
en
-
item.fulltext
with Fulltext
-
item.openaccessfulltext
Open Access
-
item.mimetype
application/pdf
-
item.openairetype
master thesis
-
item.grantfulltext
open
-
item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
-
item.cerifentitytype
Publications
-
crisitem.author.dept
E105 - Institut für Stochastik und Wirtschaftsmathematik