Creation of an investment strategy using Data Mining techniques in Spanish Stock Exchange

Abstract

This Master Thesis is developed to apply data mining techniques on stock price prediction in Spanish stock exchange. Based on passive investment strategy, in which portfolios are designed to last at least six months, one of the objectives of this work is to understand which variables are important for stock price prediction in Spain. This analysis is carried out applying CART, Random Forests and Bagging techniques to Spanish listed companies over the last twenty years. The used data consist of financial statements from all those companies. Therefore, the results will be the creation of different models based on different data mining techniques, that will allow investors to create different portfolios. Those portfolios will be compared to see what is the prediction capability of each model and if that model improves the profitability of the Spanish index Ibex35.