An Agent-Based Framework for Artificial Stock Markets

Stock markets strive to provide an efficient trading platform for investors. Trading rules and mechanisms issued to accomplish this differ among stock markets, and are subject to modification over time. Furthermore, market participants assume a broad range of roles and trading strategies. Such variation poses problems to those involved in the study of market dynamics, when developing an artificial stock market for experimentation and analysis. More than once, the resulting artificial stock markets, and thus the experimental results, are based on very restrictive assumptions. This paper introduces an agent-based framework for artificial stock market development and experimentation. The framework is flexible in the sense that multiple market structures are supported, and an infinite range of trading strategies by market participants can be captured. Such features are accomplished through the configuration of framework properties, and the appropriate hooks for extension of the framework’s components.