Data Mining in the Stock Market

Abstract

This thesis is to analyse the data from the stock exchanges with the help of data mining techniques and methods. The knowledge that was gained during analyses has been compared to the behaviour tactics on the stock exchange that have already existed and studied at Economics Universities. There were used such data mining techniques in this work: visualization, decision tree method, machine-learning (moving average, autoregressive integrated moving average and long short-term memories models). The methods were implemented in Python (Pandas, NumPy, Sklearn, Pmdarima, Keras libraries). The additional tools that were used include Power BI, Google Colaboratory. The results are described in the conclusion.