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Inferring trader’s behavior from prices. (English) Zbl 1198.91233

Brabazon, Anthony (ed.) et al., Natural computing in computational finance. Vol. 3. Some papers based on the presentations at the presentation 3rd European workshop on evolutionary computation in finance and economics (EvoFIN 2009), Tübingen, Germany, April 15–17, 2009. Berlin: Springer (ISBN 978-3-642-13949-9/hbk; 978-3-642-13950-5/ebook). Studies in Computational Intelligence 293, 85-105 (2010).
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.
For the entire collection see [Zbl 1194.91015].

MSC:

91G70 Statistical methods; risk measures
68T42 Agent technology and artificial intelligence
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62M20 Inference from stochastic processes and prediction
90C59 Approximation methods and heuristics in mathematical programming
91-08 Computational methods for problems pertaining to game theory, economics, and finance
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References:

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