Study and Analysis of Deep Learning Techniques for Solving Financial Problems

Wendell Avila, R. Salgado
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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.
研究和分析解决金融问题的深度学习技术
金融市场是受多个变量和部门影响的竞争环境。错误的决策可能会损害多个领域,并引起连锁反应,从而扰乱各个经济部门。近年来,智能模型已被用作辅助金融市场决策的工具。深度学习模型在其中脱颖而出,因为它们可以在大型数据集上实现良好的泛化。本文的主要目标是介绍和评估用于解决金融问题的深度学习。我们记录了这一过程,并介绍了使用包含 200 多万个金融数据观测值的数据集开发模型所采用的技术。我们相信,本文可以为研究类似问题的研究人员提供指导,提出在类似情况下可以使用的资源和可以遵循的步骤,从而缩小高效金融机器学习模型的搜索范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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