Agent-based modeling under partial and full knowledge learning settings to simulate financial markets

Article type: Research Article

Authors: Neri, Filippo

Affiliations: Department of Computer Information Systems, University of Malta, Msida, MSD 2080, Malta. E-mail: [email protected]

Abstract: In the paper we show how L-FABS can be applied in a partial knowledge learning scenario or a full knowledge learning scenario to approximate financial time series. L-FABS combines agent-based simulation with machine learning to model the behavior of financial time series. We also discuss why Partial Knowledge and Full Knowledge learning scenario are relevant to the modeling of financial time series and how they can be used to assess the robustness of a modeling system for financial time series. In a Partial Knowledge learning setting usually only the initial conditions of the time series are provided, while in a Full Knowledge learning scenario any value of the financial time series is exploited as soon as it is available. An extensive experimental analysis of L-FABS is reported under a variety of financial time series and time frames.

Keywords: Agent-based modeling and simulation, partial knowledge learning, full knowledge learning, simulated annealing, financial markets, prediction of SP500, DJIA, Google, SPY time series, 1 minute, 10 minutes, 1 hour, daily time series

DOI: 10.3233/AIC-2012-0537

Journal: AI Communications, vol. 25, no. 4, pp. 295-304, 2012