Probability and statistics for the engineering, computing, and physical sciences
Probability and statistics for the engineering, computing, and physical sciences
Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Designing behaviors for information agents
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The Penn-Lehman Automated Trading Project
IEEE Intelligent Systems
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Applied Intelligence
Soft computing techniques applied to finance
Applied Intelligence
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Towards automated trading based on fundamentalist and technical data
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Three automated stock-trading agents: a comparative study
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
A multi-agent decision support system for stock trading
IEEE Network: The Magazine of Global Internetworking
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Despite the fact any investor prefers lower risk and higher return, investors may have different preferences about what would be an acceptable risk or a minimal return. For instance, some investors prefer to have a lower bound risk rather than gaining a higher return. In portfolio theory, it is commonly assumed the existence of one risk free asset that offers a positive return. This theoretical risk free asset combined with a risky portfolio creates a new portfolio that presents a linear relation between risk and return as the risk free asset weight (w f ) changes. Hence, any level of risk or of return is easy to achieve separately, just by changing w f . However, in a world without any risk free assets, the combination between assets creates nonlinear portfolios. Achieving a specific level of risk or return is not a trivial task. In this paper, we assume a risky world rather than the existence of a risk free asset, in order to model an automated asset management system. Furthermore, some automated asset managers give very different results when evolving in different contexts: hence, a very profitable manager can have very bad results in other market situations. This paper presents a multiagent architecture, aiming to tackle these problems. The architecture, named COAST (COmpetitive Agent SocieTy), is based on competitive agents that act autonomously on behalf of an investor in financial asset management. It allows the simultaneous and competitive use of several asset analysis techniques currently applied in the finance field. Some dedicated agents, called advisors, apply a particular technique to a single asset. The results provided by these advisors are then submitted to and analyzed by a special agent called coach, who evaluates its advisors' performance and defines an expectation about the future price of one specific asset. Within COAST, several coaches negotiate to define the best money allocation among different assets, by using a negotiation protocol defined in this paper. We also propose an investor description model that is able to represent different investors' preferences with defined acceptable limits of risk and/or return. The COAST architecture was designed to operate adequately with any possible investor's preference. It was implemented using a financial market simulator called AgEx and tested using real data from the Nasdaq stock exchange. The test results show that the architecture performed well when compared to an adjusted market index.