Title: Vorhersage von Wirtschaftsindikator mittels Sentimentenanalyse von Nachrichtenartikeln und maschinellem Lernen
Other Titles: Prediction of an economic indicator using machine learning and sentiment analysis of news articles
Language: English
Authors: Hämmerle, Christoph 
Qualification level: Diploma
Advisor: Hanbury, Allan  
Assisting Advisor: Rekabsaz, Navid 
Issue Date: 2019
Number of Pages: 65
Qualification level: Diploma
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.

Keywords: Sentimentanalysen; Rekurrentes neuronales Netz; Finanzsystem; Nachrichtenartikeln
Sentiment analysis; Recurrent Neural Network; Financial system; News
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-125641
Library ID: AC15381189
Organisation: E194 - Institut für Information Systems Engineering 
Publication Type: Thesis
Appears in Collections:Thesis

Files in this item:

Page view(s)

checked on Jul 25, 2021


checked on Jul 25, 2021

Google ScholarTM


Items in reposiTUm are protected by copyright, with all rights reserved, unless otherwise indicated.