Behavioral finance, agent-based models and power laws

• Grant number: 14/19534-8
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2015
• End date: August 31, 2017
• Field of knowledge: Applied Social Sciences - Economics - Fiscal and Monetary Policies
• Principal Investigator: Mario Augusto Bertella
• Grantee: Mario Augusto Bertella
• Host Institution: Faculdade de Ciências e Letras (FCL). Universidade Estadual Paulista (UNESP). Campus de Araraquara. Araraquara , SP, Brazil
• Associated researchers: Felipe Roberto Pires ; Hênio Henrique Aragão Rêgo

Abstract

This research has two purposes. The first is to simulate computationally an environment with heterogeneous agents where their behaviors differ from the optimizer and rational pattern of the conventional economic theory incorporating psychological or emotional biases. The Behavioral Finance, in conjunction with Psychology, has revealed that successful agents are not as rational as neoclassical theory assumes. On the other hand, the Agent-Based Computational Economics verifies which result prevails when various agents with different behaviors interact. In this sense, the instrumental agent based is an important tool to test and simulate whether micro aspect predominates and which factors may explain the divergent results under the macro point of view. Additionally, this research aims to develop and apply techniques of Econophysics to analyze those behavioral variables under the complex system point of view. A better understanding of the psychological variables is an important step towards the development and formalization of mathematical and computational model itself and for future studies in this field. (AU)

Scientific publications

(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)

BERTELLA, MARIO A.; PIRES, FELIPE R.; REGO, HENIO H. A.; SILVA, JONATHAS N.; VODENSKA, IRENA; STANLEY, H. EUGENE. Confidence and self-attribution bias in an artificial stock market. PLoS One, v. 12, n. 2, FEB 23 2017. (14/19534-8)