Ensemble Learning in Stock Market Prediction

Abstract

In recent years, the increasing influence of machine learning in different industries had inspired many traders to benefit from it in the world of finance, stock trading is one of the most important activities. Predicting the direction of stock prices is a widely studied subject in many fields including trading, finance, statistics and computer science. The main concern for Investors is to maximize their profit if they determine when to buy/sell an investment they apply Analytical methods that makes use of different sources ranging from news to price data, all aiming at predicting the company's future stock price ML applications have presented investors with something new. A combination of technologies that could entirely reshape the way they make investment decisions. The purpose of this thesis is to leverage the aggregation of technical, fundamental, and sentiment analysis with stacked machine learning models capable of predicting profitable actions to be executed.