Abstract
A stock market is a highly complex dynamical system. Stock-price movements are not solely driven by fundamental values but in particular influenced by short term trading behaviour. Chartists use trends to forecast future price directions, whereas fundamentalists estimate stock prices based on dividend payouts or company earnings. Such strategies can similarly be deployed in automatic trading agents, which already account for a large portion of current trading activity. It is therefore vital to understand how these trading strategies behave in different scenarios, and how the interplay of strategies may lead to various market outcomes. In this paper we analyse the dynamics of three different trading strategies: fundamentalist, chartist, and zero-information traders, who base their trading behaviour on the current market price only. We simulate stock markets with various constellations of trading agents, and compare their evolutionary strength, using heuristic payoff tables and the replicator dynamics of evolutionary game theory. Our results show that it is not straightforward to predict in advance which trading strategy will perform best. Fundamentalists outperform other traders, and drive them out of the market, when information is freely available. If fundamental information is costly, chartists may thrive. As such, this paper sheds light on the various factors that play a role in determining success or failure of trading strategies in a complex market.
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