Study and Analysis of Deep Learning Techniques for Solving Financial Problems

Abstract

Financial markets are competitive environments influenced by several variables and sectors. Wrong decisions can compromise several areas and cause chain reactions that could disrupt various sectors of the economy. In recent years, intelligent models have been used as tools to aid decision-making in financial markets. Deep learning models stand out among them, as they can achieve good generalization with large datasets. The main goal of this paper is to introduce and evaluate deep learning for solving financial problems. We document the process and present the techniques employed to develop models using a dataset containing over 2 million financial data observations. We believe this paper could guide researchers working on similar problems by suggesting resources that can be used and steps that can be followed in similar scenarios, narrowing down the search for efficient financial machine learning models.