Stock Returns Distributions: The Emergent Behaviour of a Multi-Agent Artificial Stock Market
نویسنده
چکیده
We present an artificial stock market in which simple trading agents enter an asynchronous double auction market to trade in a single stock. Beginning with a population of random trading agents drawing their bid prices from a normal distribution around the current price, we compare the statistical properties of the emergent stock price return distribution to that observed on a real price series, the daily returns distribution for GE stock listed on the NYSE. We then introduce simple technical trading agents to this market, and use a genetic algorithm to evolve a population mix of agents, using the Kullback-Leibler (KL) distance to the GE distribution as our fitness function. We show that the agent populations that produce the most realistic returns distribution show predominance of non-random, rule based trading behaviours.
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