Application of machine learning for financialization modeling

Abstract

Research objective: The objective of this article is to present the application of machine learning techniques in modeling the phenomenon of financialization and analyze their effectiveness in predicting and understanding this phenomenon.
 Methodology: The methodology is based on data collection and processing from various sources. Subsequently, machine learning techniques such as regression, classification, decision trees, and neural networks were applied to train predictive models and analyze the phenomenon of financialization.
 Main conclusions: Data analysis using machine learning techniques allowed for the identification of key factors and patterns related to financialization. It has been demonstrated that machine learning models can effectively predict financialization trends and provide insight into the mechanisms and factors influencing this phenomenon.
 Application of the study: The study has significant implications for various fields, such as economics, finance, and economic policy. The application of machine learning techniques in modeling financialization can aid in making better investment decisions, assessing risk, monitoring financial stability, and developing more effective regulatory strategies.
 Originality/Novelty of the study: This article contributes an original perspective to the scientific literature by focusing on the application of machine learning techniques in the context of financialization. The work presents a new insight into this phenomenon and provides evidence of the effectiveness of machine learning-based models in analyzing and forecasting financialization.