Financial decisions support using the supervised learning method based on random forests

Abstract Financial decision supporting is a very important and complex problem. The aim of this paper is to develop the Supervised Learning method based on the random forest algorithm for decision support on stock exchange. Contemporarily, machine learning methods, including decision trees, are often used. Many research works and practical implementation projects focus on supporting decisions on stock markets. However, most of them are related to developed markets in the USA or Western Europe, or Asian stock markets. There is a lack of research related, for example, to the Warsaw Stock Exchange (WSE). The findings concern determining which of the most popular technical analysis indicators have the greatest predictive power for a successful transaction with the feature importance method.