In October 2018 I submitted my dissertation at City, University of London. This was the last project in order to obtain my Master degree in MSc Data Science.
This research project aims to predict stock price trends (buy or sell) of FTSE 100 companies after an item of news has been released by explaining stock returns using sentiment analysis. In doing so, this project uses two different state-of-the-art Deep Neural Networks: Long-Short- Term Memory and Convolutional Neural Networks as well as a novel approach to determine the ground truth of the sentiment. Both models use pre-trained word embeddings and news headlines as input data to analyse the sentiment of a piece of news. Contrary to other academic projects, this thesis only considers news headlines instead of full-text data, which increases the challenge to get significant results with only limited data. Predicting stock price movements is always challenging. The Efficient Market Hypothesis theory suggests that no model can predict stock price returns with an accuracy above 50% since the stock market is efficient and follows a random walk.
The results are compared with a baseline model and demonstrate the predictive power of Neural Networks. Whereas a standard bag-of-words-approach with a conventional machine learning algorithm did not have any predictive power, the Deep Neural Network models achieved an accuracy of nearly 60%. When breaking down the input data into industry- and company-level, the models classified the news correctly with an accuracy of 66%. This thesis does not only suggest that Deep Neural Networks are better than conventional machine learning models, but also that the selection of the input data is very important since training the data with sector- specific news increased the accuracy significantly.
Shoot me an eMail if you are interested in this project.