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Employing computational intelligence techniques and Big Data analytics in a multi-agent system experiment of finance

Abstract

We propose a research within the Agent-Based Computational Finance area. This area has been studying the application of multi-agent system models and computational intelligence techniques in finance. In this context, we intend to employ different computational intelligence techniques (reinforcement learning, genetic algorithm and fuzzy logic) and big data analytics in a multi-agent system experiment (agent-based model simulation of a double auction market) of finance. The research aims are to investigate of the ability of reinforcement learning to model the agents learning and evolution process in the financial markets, to study the use of multi-criteria performance indexes in order to model the agent learning process, and to analyse the consequence of this agents' behaviour to the financial markets as a whole. Our plan is the introduction of different computational intelligence techniques in an agent-based model simulation of the financial market, and the test of a list of hypotheses about the agents and the financial markets micro-structure. This research is relevant because its outcome can help researchers, financial regulators, policymakers and practitioners to better understand the agents learning and evolution process, and its consequences to the financial market micro-structure as a whole. We are requesting fellowships for one postdoctoral researcher with expertise in Computational Finance. In terms of internationalization, this research has an international collaboration with the Institute for Analytics and Data Science and the School of Computer Science and Electronic Engineering of the University of Essex, UK. As a part of this project, we intend to intensify our relationship though a series of researchers' visits and interchange. (AU)

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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications (10)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
LELES, MICHEL C. R.; SBRUZZI, ELTON F.; DE OLIVEIRA, JOSE M. P.; NASCIMENTO JR, CAIRO L.; IEEE. Trading Switching Setup Based on Reinforcement Learning Applied to a Multiagent System Simulation of Financial Markets. 2019 13TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON), v. N/A, p. 8-pg., . (17/20248-8, 16/04992-6)
LELES, MICHEL C. R.; CARDOSO, ADRIANO S. V.; MOREIRA, MARIANA G.; SBRUZZI, ELTON F.; NASCIMENTO, CAIRO L., JR.; GUIMARAESF, HOMERO N.; IEEE. A Singular Spectrum Analysis based Trend-Following Trading System. 12TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON2018), v. N/A, p. 5-pg., . (17/20248-8, 16/04992-6)
SBRUZZI, ELTON F.; LELES, MICHEL C. R.; NASCIMENTO, CAIRO L., JR.; IEEE. Introducing Learning Automata to Financial Portfolio Components Selection. 12TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON2018), v. N/A, p. 6-pg., . (17/20248-8, 16/04992-6)
RODRIGUES LELES, MICHEL CARLO; MOZELLI, LEONARDO AMARAL; SBRUZZI, ELTON FELIPE; NASCIMENTO JUNIOR, CAIRO LUCIO; GUIMARAES, HOMERO NOGUEIRA. A Multicriteria Trading System Based on Singular Spectrum Analysis Trading Rules. IEEE SYSTEMS JOURNAL, v. 14, n. 1, p. 1468-1478, . (16/04992-6, 17/20248-8)
LELES, MICHEL C. R.; VALE-CARDOSO, ADRIANO S.; MOREIRA, MARIANA G.; SBRUZZI, ELTON F.; NASCIMENTO JR, CAIRO L.; MOZELLI, LEONARDO A.; GUIMARAES, HOMERO N.; IEEE. Recursive Singular Spectrum Analysis Applied to the Design of a Trading System. 2019 13TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON), v. N/A, p. 7-pg., . (16/04992-6, 17/20248-8)
LELES, MICHEL C. R.; SBRUZZI, ELTON E.; P DE OLIVEIRA, JOSD M.; NASCIMENTO, CAIRO L., JR.; IEEE. A MatLab (TM) Computational Framework for Multiagent System Simulation of Financial Markets. 2019 13TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON), v. N/A, p. 8-pg., . (16/04992-6, 17/20248-8)
LELES, MICHEL C. R.; MOREIRA, MARIANA G.; VALE-CARDOSO, ADRIANO S.; NASCIMENTO JUNIOR, CAIRO L.; SBRUZZI, ELTON F.; GUIMARAES, HOMERO N.. Comparison between Basic and Toeplitz SSA applied to non-stationary time-series. STATISTICS AND ITS INTERFACE, v. 12, n. 4, p. 527-536, . (16/04992-6, 17/20248-8)
RODRIGUES LELES, MICHEL CARLO; MOZELLI, LEONARDO AMARAL; NASCIMENTO JUNIOR, CAIRO LUCIO; SBRUZZI, ELTON FELIPE; GUIMARAES, HOMERO NOGUEIRA. Study on Singular Spectrum Analysis as a New Technical Oscillator for Trading Rules Design. FLUCTUATION AND NOISE LETTERS, v. 17, n. 4, . (16/04992-6, 17/20248-8)
MAGALHAES, RACHEL F.; RANGEL, LUIS A. D.; SBRUZZI, ELTON F.; NASCIMENTO JR, CAIRO L.; LELES, MICHEL C. R.; IEEE. Analysis of the Brazilian Research Agencies using a Multicriteria Decision Aid known as TODIM. 2019 13TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON), v. N/A, p. 6-pg., . (16/04992-6, 17/20248-8)
SILVA, EMERSON A.; MOZELLI, LEONARDO A.; LELES, MICHEL C. R.; CAMPOS, VICTOR C. S.; PALAZZO, GUILHERME; IEEE. A study on Non-parametric Filtering in Linear and Nonlinear Control Loops using the Singular Spectrum Analysis. 2023 IEEE INTERNATIONAL SYSTEMS CONFERENCE, SYSCON, v. N/A, p. 8-pg., . (16/04992-6)

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