Publicado:

2024-01-23

Número:

Vol. 10 Núm. 2 (2022): agosto-diciembre- 2022

Sección:

Artículo corto

Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras

Machine learning techniques for data analysis in financial application

Machine learning techniques for data analysis in financial applications

Autores/as

  • Nicolas Rodriguez Gutierrez Ingeniero de Sistemas, Politécnico Grancolombiano

Palabras clave:

Inteligencia artificial, aprendizaje automático, aplicaciones financieras, computación blanda, aprendizaje profundo, minería de datos (es).

Palabras clave:

Artificial intelligence, machine learning, financial applications, soft computing, deep learning, data mining. (en).

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Resumen (es)

En este artículo se presentarán algunas de las técnicas más relevantes del aprendizaje automático (machine learning) utilizadas en diferentes trabajos de investigación para tratar con aplicaciones financieras como evaluación crediticia, gestión de cartera, predicción de mercados, acciones o divisas y planificación financiera en general, la finalidad de este documento es el de presentar de manera general las técnicas de aprendizaje automático usadas para estas aplicaciones financieras, dando a conocer las conclusiones a las que se llegaron en los diferentes artículos de investigación acerca de las ventajas y los avances que se han tenido al utilizar dichas técnicas al solucionar problemas financieros, no se entrara al detalle de los resultados obtenidos en cada investigación en diferentes aspectos financieros, solo se busca obtener un panorama amplio del análisis del uso en dichos aspectos las siguientes técnicas de aprendizaje automático: redes neuronales, sistemas expertos, sistemas de inteligencia hibrida, minería de datos, técnicas de computación blandas y técnicas de aprendizaje profundo (Deep learning).

Resumen (en)

This article will present some of the most relevant techniques of machine learning used in different research works to deal with financial applications such as credit evaluation, portfolio management, prediction of markets, stocks or currencies and financial planning in general. The purpose of this document is to present in a general way the machine learning techniques used for these financial applications, making known the conclusions reached in the different research articles about the advantages and advances that have been made. When using these techniques when solving financial problems, the results obtained in each investigation in different financial aspects will not be entered into detail, it only seeks to obtain a broad panorama of the analysis of the use in these aspects of the following machine learning techniques: neural networks, expert systems, intelligence systems hybrid AI, data mining, soft computing techniques and deep learning techniques.

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Cómo citar

APA

Rodriguez Gutierrez, N. (2024). Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application. Tecnología Investigación y Academia, 10(2), 167–184. https://revistas.udistrital.edu.co/index.php/tia/article/view/17306

ACM

[1]
Rodriguez Gutierrez, N. 2024. Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application. Tecnología Investigación y Academia. 10, 2 (ene. 2024), 167–184.

ACS

(1)
Rodriguez Gutierrez, N. Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application. Tecnol. Investig. Academia TIA 2024, 10, 167-184.

ABNT

RODRIGUEZ GUTIERREZ, Nicolas. Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application. Tecnología Investigación y Academia, [S. l.], v. 10, n. 2, p. 167–184, 2024. Disponível em: https://revistas.udistrital.edu.co/index.php/tia/article/view/17306. Acesso em: 23 feb. 2024.

Chicago

Rodriguez Gutierrez, Nicolas. 2024. «Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application». Tecnología Investigación y Academia 10 (2):167-84. https://revistas.udistrital.edu.co/index.php/tia/article/view/17306.

Harvard

Rodriguez Gutierrez, N. (2024) «Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application», Tecnología Investigación y Academia, 10(2), pp. 167–184. Disponible en: https://revistas.udistrital.edu.co/index.php/tia/article/view/17306 (Accedido: 23 febrero 2024).

IEEE

[1]
N. Rodriguez Gutierrez, «Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application», Tecnol. Investig. Academia TIA, vol. 10, n.º 2, pp. 167–184, ene. 2024.

MLA

Rodriguez Gutierrez, Nicolas. «Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application». Tecnología Investigación y Academia, vol. 10, n.º 2, enero de 2024, pp. 167-84, https://revistas.udistrital.edu.co/index.php/tia/article/view/17306.

Turabian

Rodriguez Gutierrez, Nicolas. «Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application». Tecnología Investigación y Academia 10, no. 2 (enero 23, 2024): 167–184. Accedido febrero 23, 2024. https://revistas.udistrital.edu.co/index.php/tia/article/view/17306.

Vancouver

1.
Rodriguez Gutierrez N. Técnicas de aprendizaje automático para el análisis de datos en aplicaciones financieras: Machine learning techniques for data analysis in financial application. Tecnol. Investig. Academia TIA [Internet]. 23 de enero de 2024 [citado 23 de febrero de 2024];10(2):167-84. Disponible en: https://revistas.udistrital.edu.co/index.php/tia/article/view/17306

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