Application of a machine learning model to maximize the success rate in day trade operations on the American Stock Exchange

Author:

Carvalho Wagner A.,Cerqueira Marcelo Henrique C.,de Azevedo de Oliveira Luana,Santos Simões Carlos Francisco,Fávero Luiz Paulo,dos Santos Marcos

Publisher

Elsevier BV

Reference18 articles.

1. Campos, Álvaro. 2019. More than 90% of people do not make profits in day trading. Valor Econômico newspaper, São Paulo, SP, Brazil. Available at: . Accessed on: 07 mar. 2023.

2. Velez, Oliver; Capra, Creg. 2000. Tools and Tactics for Master Day Trader. McGraw-Hill Professional, Chicago, IL, United States.

3. Felipe Dias Paiva, Rodrigo Tomás Nogueira Cardoso, Gustavo Peixoto Hanaoka, Wendel Moreira Duarte, Decision-making for financial trading: A fusion approach of machine learning and portfolio selection, Expert Systems with Applications,Vol. 115, 2019, Pages 635-655, ISSN 0957-4174, doi:10.1016/j.eswa.2018.08.003.

4. Vlah Jeric, Silvija. Rule extraction from random forest for intra-day trading using crobex data. Proceedings of FEB Zagreb International Odyssey Conference on Economics and Business [2671-132X]. v.:2 n.:1 p.:411 -419.

5. J. Fior and L. Cagliero, "Exploring the Use of Data at Multiple Granularity Levels in Machine Learning-Based Stock Trading," 2020 International Conference on Data Mining Workshops (ICDMW), Sorrento, Italy, 2020, pp. 333-340, doi: 10.1109/ICDMW51313.2020.00053.

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