Index prediction on the Swedish stock market using natural language processing methods on Swedish news
Abstract: This master thesis explores if topic modelling and sentiment analysis on Swedish financial newspaper data can be used to predict the direction of the Swedish stock market. A pipeline was set up where full length articles as well as article summaries were fed into a topic model and a sentiment analysis model. Several methods for combining the outputs of these models were explored in order to create data representations. The data representations were fed into four different machine learning models and one deep learning model that predicted the direction of stock index movement for three time periods: daily, weekly and monthly. The performance of the stock market index prediction model showed great promise on the in-sample data, alas, no conclusive answer could be drawn from the results when testing on the out-of-sample data. Allowing for the topic model to be trained on the test period, some encouraging results were obtained that lead to interesting observations which serves as a foundation for future research. This master thesis was written under guidance of Lund University, Faculty of Engineering, Division of Mathematical Statistics and in collaboration with the company Sanctify Financial Technologies.
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