Evolving Strategies and Complexity in an Artificial Stock Market

Summary

This study describes a multi-agent artificial stock market in which agents use limited memories of past prices to choose trades. Their trading strategies change through a self-teaching process, allowing the model to examine how market behavior evolves as the population grows.

Simulations produce frequent large price movements compared with a normal process. Returns have a Lévy-shaped central distribution with an approximately exponential truncation in the tails. The authors also define a measure of system complexity and report a transition from a simpler to a more complex phase as the number of agents increases. These are simulation results; the excerpt gives no calibration to real markets or details about the model parameters, so their empirical reach is unclear.

Key ideas

• Agents base their actions on limited histories of stock prices and evolving strategies.
• A self-teaching mechanism changes agents' strategies over time.
• The simulated market produces frequent large price events relative to a normal process.
• Returns show a Lévy-like center and approximately exponential tail truncation.
• Measured system complexity shifts from a simpler to a more complex phase as the agent population grows.

Tags

# Study on Evolvement Complexity in an Artificial Stock Market


# Study on Evolvement Complexity in an Artificial Stock Market









An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is Lévy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.