Deep learning as a tool in forecasting the phenomenon of financialization

Abstract

Research objective: The aim of the article is to analyze the effectiveness and accuracy of deep learning in predicting trends and changes related to financialization. Methodology: In preparing this scientific article, the focus was on conducting a literature review and analyzing existing research that utilized deep learning techniques to forecast the phenomenon of financialization. The principles, algorithms, and techniques applied in deep learning were discussed, with a particular emphasis on their potential applications in predicting financialization trends. Main conclusions: The results indicate that deep learning can be a powerful tool for forecasting financialization, demonstrating high predictive accuracy. Application of the study: The discoveries from this article can find practical application in the field of financialization, supporting better investment decision-making and risk management. Originality/Novelty of the study: The work adds value by showcasing the potential of deep learning as an advanced tool for forecasting financialization, which can significantly impact the development of this domain.