COMPARATIVE ANALYSIS OF APPROACHES TO MODELING CHANGES IN THE FINANCIAL MARKETS OF THE TECHNOLOGICAL SECTOR OF THE ECONOMY

UDC 330.47
DOI: 10.36871/2618-9976.2024.01.007

Authors

Darya E. Arbatskaya,
Applicant for a degree, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Dmitry G. Rodionov,
Doctor of Science (Economic), Professor, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Ungvari Laslo,
Candidate of Sciences (Economic), Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Evgeny A. Konnikov,
Candidate of Sciences (Economic), Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Abstract

This study aims to find and examine in detail an effective method for predictive models of price prediction and its nextday change for stocks such as AAPL, AMZN and GOOG. The research methodology used machine learning methods for both the regression model and the classification model for each of the stocks under consideration. Machine learning methods have made great strides in the value of the described variance. However, combining the results obtained, it is impossible to say with sufficient accuracy that trained regression methods can be applied in real conditions.

Keywords

Modeling, Financial markets, Stock prices, Forecasting