Applying machine learning to automate stock portfolio management

Sammanfattning: There are multiple ways to analyze stock companies. One way is using fundamental analysis, which means one is analyzing the company’s business key figures, such as revenue, net income and more. The key figures that were chosen to be analyzed in this report were: revenue, net income, free cash f low, return on invested capital and debt-to-equity ratio. In this thesis, machine learning was implemented to evaluate if it is possible to automate fundamental research of companies and to be able to produce a portfolio that would outperform the Swedish stock index. The data set used for both training and testing the classifiers contained the company’s basic information, 10 years of fundamental history and stock price history from the past 10 years. The companies examined were every stock listed on Nasdaq Stockholm, Nasdaq First North, Spotlight Market, Nordic Growth Market and PepMarket. The data that was gathered stretches from 2012 to 2021 which were split up into f ive-year periods and made up the training and testing period. The training data contained fundamental history from every company from these five-year periods. The classifier’s results from the testing period were used to create the portfolios during the holding period 2021-2022 to benchmark against the Swedish stock index. The results indicate that it is indeed possible to create portfolios using machine learning that will outperform the market over a year of holding the stocks.