FORECASTING THE INVESTMENT POTENTIAL OF COMPANIES IN THE TECHNOLOGY SECTOR

UDC 338.24
4DOI: 10.36871/2618­-9976.2024.08.005

Authors

Oleg A. Arzumanov,
Student, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Ungvari Laslo,
Doctor of Economic Sciences, Professor, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia, Technical University of Applied Sciences Wildau, Wildau, Germany
Tonni Mayambala Sebaggala,
Senior Lecturer, Makerere University, Kampala, Uganda

Abstract

This article is dedicated to developing a predictive model for assessing the investment potential of companies in the technology sector. In the context of advancing technologies and their impact on economic progress, there is a need for investors to develop reliable recommendations for longterm and mediumterm investments in the stock market. The aim of the study is to build a model based on the random forest method capable of classifying companies and determining their investment potential based on data analysis. The object of the study is companies in the technology sector, with the subject being the forecasting of their investment potential. Research methods include analysis of companies' financial indicators, the use of machine learning methods such as random forest for building a predictive model, and evaluation of the accuracy and significance of models based on various metrics such as accuracy, F1Score, and confusion matrix. The results obtained allow identifying trustworthy companies with a strong foundation, which can assist investors in making informed decisions in the stock market. The conclusion emphasizes the significance of the chosen model for determining investment potential and its potential application in other sectors of the economy.

Keywords

Investments, Investment potential, Technology sector, Financial analysis, Random forest method, Machine learning, FXNUMX-Score, Confusion matrix, Forecasting, Company evaluation, Stock ma