The Analysis of News to Predict Stock Price Movements Using Machine Learning Approach

Student: Dolgushin Valeriy

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2019

Researchers and stock market players mostly use standard technical analysis tools to make decisions. This work offers a new approach in order to minimize the risk of error and increase the income in the case of the trader. Machine learning algorithms-based models are able to predict whether the price will go up or down based on pre-processed and transformed into a computer-friendly type of text data. The object of the research is the process of predicting the dynamics (growth or fall) of the stock by using news text data, as well as to investigate the validity of such process and the possibility of extracting useful relationships. The subject of the study is the sequence from data collection to search for optimal hyperparameters, training of several classifiers. The aim of this work is to study the possibility of using NLP approach to predict the price movement of shares on the stock market using news sources.

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