Detail práce

News Feed Classifications to Improve Volatility Predictions

Autor: Mgr. Ksenia Pogodina
Rok: 2018 - zimní
Typ práce: Diplomová
Finance, finanční trhy a bankovnictví
Jazyk: Anglicky
Stránky: 83
Abstrakt: This thesis analyzes various text classification techniques in order to assess
whether the knowledge of published news articles about selected companies
can improve its’ stock return volatility modelling and forecasting. We examine
the content of the textual news releases and derive the news sentiment (polarity
and strength) employing three different approaches: supervised machine
learning Naive Bayes algorithm, lexicon-based as a representative of linguistic
approach and hybrid Naive Bayes. In hybrid Naive Bayes we consider only the
words contained in the specific lexicon rather than whole set of words from the
article. For the lexicon-based approach we used independently two lexicons one
with binary another with multiclass labels. The training set for the Naive Bayes
was labeled by the author. When comparing the classifiers from the machine
learning approach we can conclude that all of them performed similarly with a
slight advantage of the hybrid Naive Bayes combined with multiclass lexicon.
The resulting quantitative data in form ofsentiment scores will be then incorporated
into GARCH volatility modelling. The findings suggest that information
contained in news feeds does bring an additional explanatory power to traditional
GARCH model and is able to improve it’s forecast. On the contrary, we
could not provide enough evidence for favouring specific sentiment-derivation
method. While the model employing hybrid Naive Bayes approach provided
a bitter in-sample fit, the preferred model in the out-of-sample evaluation was
the one employing multiclass lexicon. We also showed an asymmetric news
effect, where both positive and negative news increase volatility with a latter
having a more pronounced effect.




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