Abstract
Integrating agent based modeling with learning results in a promising methodology to model the behavior of financial markets. We discuss here how partial and full knowledge learning setups can be combined with agent based modeling to approximate the behavior of financial time series. Partial knowledge learners operate with limited knowledge of the domain, usually only the initial conditions are used. While full knowledge learners use any domain data any time it is made available to adjust their predictions.
We report in this paper an experimental study of our learning system L-FABS, introduced in previous works, in order to show how it can discover models for approximating time series working in partial knowledge and full knowledge learning scenarios.
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Neri, F. (2012). A Comparative Study of a Financial Agent Based Simulator Across Learning Scenarios. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds) Agents and Data Mining Interaction. ADMI 2011. Lecture Notes in Computer Science(), vol 7103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27609-5_7
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DOI: https://doi.org/10.1007/978-3-642-27609-5_7
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