Publicado:
2024-01-23Número:
Vol. 10 Núm. 2 (2022): agosto-diciembre- 2022Sección:
Artículo cortoTécnicas 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
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).Descargas
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.
Referencias
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[22] Ahmet Murat Ozbayoglu, Mehmet Ugur Gudelek and Omer Berat Sezer. Deep Learning for Financial Applications: A Survey, pages 1–52. IEEE, 2020.
[23] Efstathios Kirkos and Yannis Manolopoulos. Data mining in finance and accounting: A review of current research trends. In Proceedings of the 1 st International Conference on Enterprise Systems and Accounting (ICESAcc, pages 63–78, 2004.
[24] V. Ravi, H. Kurniawan, Peter Nwee Kok Thai, and P. Ravi Kumar. Soft computing system for bank performance prediction. Applied Soft Computing, 8(1):305–315, January 2008.
[25] Meryem Duygun Fethi and Fotios Pasiouras. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2):189–198, 2010.
[26] Adel Lahsasna, Raja Noor Ainon, and Ying Wah Teh. Credit scoring models using soft computing methods: A survey. Int. Arab J. Inf. Technol., 7:115–123, 2010.
[27] Ning Chen, Bernardete Ribeiro, and An Chen. Financial credit risk assessment: a recent review. Artificial Intelligence Review, 45(1):1–23, 2015.
[28] AI Marques, Vicente García, and José Salvador Sánchez. A literature review on the application of evolutionary computing to credit scoring. Journal of the Operational Research Society, 64(9):1384– 1399, 2013.
[29] P. Ravi Kumar and V. Ravi. Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review. European Journal of Operational Research, 180(1):1–28, July 2007.
[30] Antanas Verikas, Zivile Kalsyte, Marija Bacauskiene, and Adas Gelzinis. Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing, 14(9):995–1010, 2009.
[31] Jie Sun, Hui Li, Qing-Hua Huang, and Kai-Yu He. Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. KnowledgeBased Systems, 57:41–56, 2014.
[32] W. Lin, Y. Hu, and C. Tsai. Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4):421–436, 2012.
[33] E Kirkos, C Spathis, and Y Manolopoulos. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4):995–1003, 2007.
[34] Dianmin Yue, Xiaodan Wu, Yunfeng Wang, Yue Li, and Chao-Hsien Chu. A review of data miningbased financial fraud detection research. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007.
[35] Shiguo Wang. A comprehensive survey of data mining-based accounting-fraud detection research. In 2010 International Conference on Intelligent Computation Technology and Automation. IEEE, 2010.
[36] Clifton Phua, Vincent C. S. Lee, Kate Smith-Miles, and Ross W. Gayler. A comprehensive survey of data mining-based fraud detection research. CoRR, abs/1009.6119, 2010.
[37] E.W.T. Ngai, Yong Hu, Y.H. Wong, Yijun Chen, and Xin Sun. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3):559–569, 2011.
[38] Anuj Sharma and Prabin Kumar Panigrahi. A review of financial accounting fraud detection based on data mining techniques. International Journal of Computer Applications, 39(1):37–47, February 2012.
[39] Jarrod West and Maumita Bhattacharya. Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57:47–66, 2016.
[40] Yaya Heryadi and Harco Leslie Hendric Spits Warnars. Learning temporal representation of transaction amount for fraudulent transaction recognition using cnn, stacked lstm, and cnn-lstm. In 2017 IEEE International Conference on Cybernetics and Computational Intelligence. IEEE, 2017.
[41] Wei Bao, Jun Yue, and Yulei Rao. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOS ONE, 12(7):e0180944, 2017.
[42] Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. Stock price prediction via discovering multifrequency trading patterns. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD17. ACM Press, 2017.
[43] Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, and Qionghai Dai. Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, 28(3):653–664, March 2017.
[44] David W. Lu. Agent inspired trading using recurrent reinforcement learning and lstm neural networks, 2017.
[45] Matthew Francis Dixon, Diego Klabjan, and Jin Hoon Bang. Classification-based financial markets prediction using deep neural networks. SSRN Electronic Journal, 2016.
[46] Jerzy Korczak and Marcin Hernes. Deep learning for financial time series forecasting in a-trader system. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017.
[47] Bin Li and Steven C. H. Hoi. Online portfolio selection: A survey. ACM Comput. Surv., 46(3): 35:1–35:36, 2014.
[48] K. Metaxiotis and K. Liagkouras. Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert Systems with Applications, 39(14):11685–11698, 2012.
[49] Anthony Grace. Can deep learning techniques improve the risk adjusted returns from enhanced indexing investment strategies. Master’s thesis, 2017.
[2] Basheer IA, Hajmeer M, Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 43:3–31, 2010.
[3] Haykin S (1988) Neural networks—a comprehensive foundation. McMillan College Publishing, New York, 1988.
[4] Touzet CF, Neural reinforcement learning for behaviour synthesis. Robotics Autonom Sys 22:251–281, 1997.
[5] Harmon P, King D, Artificial intelligence in business– expert systems. Wiley, New York, 1985.
[6] Ellis C, Willson P, Expert system portfolios of Australian and UK securitized property investments. Pacific Rim Property Res J 12(1):107–127, 2005.
[7] Razieh A, Zahra A, A Review on Expert Systems and Their Usage In Management 7(8): 1460-1465, 2013.
[8] Taha IE, A hybrid intelligent architecture for revising domain knowledge. Ph. D. Report. University of Texas at Austin, 1997.
[9] Lertpalangsunti N, An implemented framework for the construction of hybrid intelligent forecasting systems. Ph. D. Report. University of Regina, 1997.
[10] Goonatilake S, Khebbal S, Intelligent hybrid systems: issue, classifications and future directions in intelligent hybrid systems. Wiley, London, 1995.
[11] D. Zhang and L. Zhou. Discovering golden nuggets: Data mining in financial application. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 34(4):513–522, 2004.
[12] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI Mag., vol. 17, pp. 37–54, 1996.
[13] Mochón, A., Quintana, D., Sáez, Y. et al. Soft computing techniques applied to finance. Appl Intell 29, 111–115 (2008).
[14] Zadeh LA, Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84, 1994.
[15] Klir GJ, Yuan B, Clair USt Fuzzy set theory: foundations and applications. Prentice Hall, New York, 1997.
[16] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, 2015.
[17] Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527–1554, 2006.
[18] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735– 1780, 1997.
[19] Xueheng Qiu, Le Zhang, Ye Ren, P. Suganthan, and Gehan Amaratunga. Ensemble deep learning for regression and time series forecasting. In 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL), pages 1–6, 2014.
[20] Yoshua Bengio. Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning, pages 17–36, 2012
[21] Qinxue Meng, Daniel Catchpoole, David Skillicom, and Paul J Kennedy. Relational autoencoder for feature extraction. In 2017 International Joint Conference on Neural Networks (IJCNN), pages 364–371. IEEE, 2017.
[22] Ahmet Murat Ozbayoglu, Mehmet Ugur Gudelek and Omer Berat Sezer. Deep Learning for Financial Applications: A Survey, pages 1–52. IEEE, 2020.
[23] Efstathios Kirkos and Yannis Manolopoulos. Data mining in finance and accounting: A review of current research trends. In Proceedings of the 1 st International Conference on Enterprise Systems and Accounting (ICESAcc, pages 63–78, 2004.
[24] V. Ravi, H. Kurniawan, Peter Nwee Kok Thai, and P. Ravi Kumar. Soft computing system for bank performance prediction. Applied Soft Computing, 8(1):305–315, January 2008.
[25] Meryem Duygun Fethi and Fotios Pasiouras. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2):189–198, 2010.
[26] Adel Lahsasna, Raja Noor Ainon, and Ying Wah Teh. Credit scoring models using soft computing methods: A survey. Int. Arab J. Inf. Technol., 7:115–123, 2010.
[27] Ning Chen, Bernardete Ribeiro, and An Chen. Financial credit risk assessment: a recent review. Artificial Intelligence Review, 45(1):1–23, 2015.
[28] AI Marques, Vicente García, and José Salvador Sánchez. A literature review on the application of evolutionary computing to credit scoring. Journal of the Operational Research Society, 64(9):1384– 1399, 2013.
[29] P. Ravi Kumar and V. Ravi. Bankruptcy prediction in banks and firms via statistical and intelligent techniques – a review. European Journal of Operational Research, 180(1):1–28, July 2007.
[30] Antanas Verikas, Zivile Kalsyte, Marija Bacauskiene, and Adas Gelzinis. Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing, 14(9):995–1010, 2009.
[31] Jie Sun, Hui Li, Qing-Hua Huang, and Kai-Yu He. Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. KnowledgeBased Systems, 57:41–56, 2014.
[32] W. Lin, Y. Hu, and C. Tsai. Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4):421–436, 2012.
[33] E Kirkos, C Spathis, and Y Manolopoulos. Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4):995–1003, 2007.
[34] Dianmin Yue, Xiaodan Wu, Yunfeng Wang, Yue Li, and Chao-Hsien Chu. A review of data miningbased financial fraud detection research. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007.
[35] Shiguo Wang. A comprehensive survey of data mining-based accounting-fraud detection research. In 2010 International Conference on Intelligent Computation Technology and Automation. IEEE, 2010.
[36] Clifton Phua, Vincent C. S. Lee, Kate Smith-Miles, and Ross W. Gayler. A comprehensive survey of data mining-based fraud detection research. CoRR, abs/1009.6119, 2010.
[37] E.W.T. Ngai, Yong Hu, Y.H. Wong, Yijun Chen, and Xin Sun. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3):559–569, 2011.
[38] Anuj Sharma and Prabin Kumar Panigrahi. A review of financial accounting fraud detection based on data mining techniques. International Journal of Computer Applications, 39(1):37–47, February 2012.
[39] Jarrod West and Maumita Bhattacharya. Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57:47–66, 2016.
[40] Yaya Heryadi and Harco Leslie Hendric Spits Warnars. Learning temporal representation of transaction amount for fraudulent transaction recognition using cnn, stacked lstm, and cnn-lstm. In 2017 IEEE International Conference on Cybernetics and Computational Intelligence. IEEE, 2017.
[41] Wei Bao, Jun Yue, and Yulei Rao. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOS ONE, 12(7):e0180944, 2017.
[42] Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. Stock price prediction via discovering multifrequency trading patterns. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD17. ACM Press, 2017.
[43] Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, and Qionghai Dai. Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, 28(3):653–664, March 2017.
[44] David W. Lu. Agent inspired trading using recurrent reinforcement learning and lstm neural networks, 2017.
[45] Matthew Francis Dixon, Diego Klabjan, and Jin Hoon Bang. Classification-based financial markets prediction using deep neural networks. SSRN Electronic Journal, 2016.
[46] Jerzy Korczak and Marcin Hernes. Deep learning for financial time series forecasting in a-trader system. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017.
[47] Bin Li and Steven C. H. Hoi. Online portfolio selection: A survey. ACM Comput. Surv., 46(3): 35:1–35:36, 2014.
[48] K. Metaxiotis and K. Liagkouras. Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert Systems with Applications, 39(14):11685–11698, 2012.
[49] Anthony Grace. Can deep learning techniques improve the risk adjusted returns from enhanced indexing investment strategies. Master’s thesis, 2017.
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: 12 may. 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: 12 mayo 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 mayo 12, 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 12 de mayo de 2024];10(2):167-84. Disponible en: https://revistas.udistrital.edu.co/index.php/tia/article/view/17306
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