Central Bank Sentiment Analysis and Asset Prices : Using Machine Learning and Natural Language Processing to Conduct Sentiment Analysis for Predicting Stock Prices in a Norwegian Financial Context
Master thesis
Permanent lenke
https://hdl.handle.net/11250/3095718Utgivelsesdato
2023Metadata
Vis full innførselSamlinger
- Master Thesis [4487]
Sammendrag
This thesis seeks to investigate the relationship between text sentiment in central bank
communication and stock market returns in a Norwegian context using a machine learning
approach. We collect textual data from Norges Bank (the Norwegian Central Bank) consisting
of monetary policy evaluations spanning the last 24 years and apply a multinomial inverse
regression (MNIR) to create a positive and negative sentiment dictionary. For performance
comparisons, we employ a set of naïve methods, one of which is developed by Kirkeby and
Larsen (2021) and one developed by ourselves. Results indicate that there is no significant
relationship between the sentiment of Norges Bank and stock returns at Oslo Børs (Oslo Stock
Exchange) using test data. Out-of-sample, significant results were only found using a negative
sentiment dictionary constructed by ourselves. Counterintuitively, these results indicate that a
more negative sentiment leads to higher stock returns. We theorise that not finding significant
results with the MNIR-dictionary can be contributed to a few factors. Loss of generality could
explain parts of our results. Only using single terms to capture sentiment means that some
significance might be lost in the process. We also discuss the possibility that our model mainly
captures attributes in the financial landscape that leads to higher or lower stock returns, rather
than capturing actual sentiment. Future research into this field using variations in data or
methodology can be successful in further investigations of the relationship between central
bank communication and asset prices.