APPLYING OF RANDOM FOREST AND SUPPORT VECTOR MACHINE IN PREDICTING PRICES OF URANIUM COMPANIES

Abstract

Due to the war in Ukraine and restrictions on the export of hydrocarbons from Russia by the European countries, uranium companies are again becoming an interesting sector in terms of investment. Consequently, it is important for investors to have accurate forecasts of uranium sector. This article applies machine learning algorithms such as the Random Forests and the Support Vector Machine to predict future URA ETF prices for the next five periods. The study was conducted using data on the ETF Global X Uranium for the period from 08/11/2010 to 31/05/2023 was obtained from investing.com. The data contains information about the stock such as High, Low, Open, Close, Adjacent close and Volume and several well-known technical indicators. The research showed that both the Random Forest and the Support Vector Machine forecast prices with less bias than the classic ARIMA model. The Random Forest algorithm forecasted prices with a constant level of bias over the forecasting period, while the error of the forecasts calculated by the Support Vector Machine algorithm for the first three periods was the lowest compared to the rest of the analyzed models. Research showed that the Random Forest algorithm and the Support Vector Machine can be used to make correct predictions for uranium sector.