Artificial economic life: a simple model of a stockmarket
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Evolutionary Computation in Economics and Finance
Evolutionary Computation in Economics and Finance
Genetic Algorithms and Genetic Programming in Computational Finance
Genetic Algorithms and Genetic Programming in Computational Finance
On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach
Computational Economics
Flexible neural trees ensemble for stock index modeling
Neurocomputing
Dynamic population variation in genetic programming
Information Sciences: an International Journal
A novel approach for multi-agent-based Intelligent Manufacturing System
Information Sciences: an International Journal
Short run dynamics in an artificial futures market with human subjects
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Analysis of learning types in an artificial market
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
Automated traders in commodities markets: Case of producer-consumer institution
Expert Systems with Applications: An International Journal
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
Identification of stock market forces in the system adaptation framework
Information Sciences: an International Journal
| Hi-index | 0.00 |
From the perspective of the agent-based model of stock markets, this paper examines the possible explanations for the presence of the causal relation between stock returns and trading volume. Using the agent-based approach, we find that the explanation for the presence of the stock price volume relation may be more fundamental. Conventional devices such as information asymmetry, reaction asymmetry, noise traders or tax motives are not explicitly required. In fact, our simulation results show that the stock price volume relation may be regarded as a generic property of a financial market, when it is correctly represented as an evolving decentralized system of autonomous interacting agents. One striking feature of agent-based models is the rich profile of agents' behavior. This paper makes use of the advantage and investigates the micro-macro relations within the market. In particular, we trace the evolution of agents' beliefs and examine their consistency with the observed aggregate market behavior. We argue that a full understanding of the price volume relation cannot be accomplished unless the feedback relation between individual behavior at the bottom and aggregate phenomena at the top is well understood.