Recent research has documented that learning and evolution are capable of generating many well-known features in financial times series. We extend the results of LeBaron and Yamamoto (2007) to explore the impact of varying amounts of imitation and agent learning in a simple order-driven market. We show that in our framework, imitation is critical to the generation of long memory persistence in many financial time series. This shows that imitation across trader behavior is probably crucial for understanding the dynamics of prices and trading volume.
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