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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

Andersen, Julian Cæsar; Thorshaug, Magnus Glad
Master thesis
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URI
https://hdl.handle.net/11250/3095718
Date
2023
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  • Master Thesis [4657]
Abstract
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.

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