Simulação de mercado financeiro com compra e venda otimizadas por Enxame de Partículas

It has been of great interest, both on the part of researchers and investors, to define negotiation rules that make it possible to capture the dynamics of the financial markets. This article presents a negotiation model among financial agents, based on the stock buying and selling process, that form a financial market. For the adaptation of economic agents to the market, a Particle Swarm Optimization (PSO) algorithm was implemented to optimize trading rules between agents aiming at maximizing gains in the market. Times series of artificial markets and real Bovespa brazillian market, descripted by the index Bovespa, were used in the computational simulations. Through the simulations, the dynamics of the interaction of buying and selling between financial agents was explored. The results show that there is a dependence on the gains of the agents in the markets in relation to the trading strategies adopted. On the other hand, in the low markets this dependence was not observed, since no statistically significant differences were found for the amount of wealth accumulated among the market participants. For the Bovespa market, from the sell and purchase thresholds of the trades carried out, the agents that have the best strategies in the negotiations were identified.

Editorial for the Special Issue on ‘Agent-Based Artificial Markets’

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BRIDGING THE GAP BETWEEN ARTIFICIAL MARKET SIMULATIONS AND QUALITATIVE RESEARCH IN DIFFUSION OF INNOVATION

Artificial markets are an emerging form of agent-based simulation in which agents represent individual industries, firms, or consumers interacting under simulated market conditions. While artificial markets demonstrate considerable potential for advancing innovation research, the validity of the method depends on the ability of researchers to construct agents that faithfully capture the key behavior of targeted entities. To date, few such methods have been documented in the academic literature. This article describes a novel method for combining qualitative innovation research (case studies, grounded theory, and sequence analysis) with software engineering techniques to synthesize simulation-ready theories of adoption behavior. A step-by-step example is provided from the transportation domain. The result was a theory of adoption behavior that is sufficiently precise and formal to be expressed in Unified Modeling Language (UML). The article concludes with a discussion of the limitations of the method and recommendations future applications to the study of diffusion of innovation.