Systemic approach on the dynamics of the stock market

Abstract

The stock market constitutes a highly complex and dynamic system, characterized by multiple interconnected elements and emerging behavioral patterns. This research analyzes the implications of adopting a systemic perspective, which allows modeling the interactions and patterns that arise within the dynamics of the stock market. Through methodologies such as agent-based modeling, systemic network analysis and complex systems theory, it is possible to represent the interconnections between various financial assets and evaluate how fluctuations in a specific asset can propagate and influence other components of the asset. market. Likewise, the importance of integrating approaches such as neural networks and machine learning with systemic analysis is emphasized, allowing the non-linear relationships inherent in financial data to be unraveled and predictive models to be developed with greater precision. This methodological synergy provides a deeper understanding of the underlying dynamics of the stock market. The main objective of this study is to explore how simulation can guide the search for the optimal portfolio, maximizing return and minimizing risk by evaluating hypothetical scenarios and making informed decisions in the field of stock investments.