Summary
We propose a representation of the stock market as a group of rule-based trading agents, with the agents evolved using past prices. We encode each rule-based agent as a genome, and then describe how a steady-state genetic algorithm can evolve a group of these genomes (i.e. an inverted market) using past stock prices. This market is then used to generate forecasts of future stock prices, which are compared to actual future stock prices. We show how our method outperforms standard financial time-series forecasting models, such as ARIMA and Lognormal, on actual stock price data taken from real-world archives.
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Charbonneau, L., Kharma, N. (2010). Inferring Trader’s Behavior from Prices. In: Brabazon, A., O’Neill, M., Maringer, D.G. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13950-5_6
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DOI: https://doi.org/10.1007/978-3-642-13950-5_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13949-9
Online ISBN: 978-3-642-13950-5
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