Evolution of trading strategies in a market with heterogeneously informed agents
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We present an agent-based simulation of an asset market with heterogeneously informed agents. Genetic programming is applied to optimize the agents’ trading strategies. After optimization, insiders are the only agents able to generate small systematic above-average returns. For all other agents, genetic programming finds a rich variety of trading strategies that are predominantly based on exclusive subsets of their information. This limits their price impact and prevents them from making systematic losses. The resulting low noise renders market prices as largely informationally efficient.
KeywordsAgent-based simulation Heterogeneous agents Trading strategies Genetic programming
JEL ClassificationD82 D58 C61 G1
We thank Michael Hanke, Jürgen Huber, Klaus Schredelseker, all discussants at the workshop “Evolution and Market Behavior in Economics and Finance”, and three anonymous referees for very helpful comments on this paper. We also acknowledge the FNR (Fonds National de la Recherche Luxembourg) for financial support on this project.
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