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Combining Machine Learning and Agent Based Modeling for Gold Price Prediction

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Artificial Life and Evolutionary Computation (WIVACE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 900))

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Abstract

A computational approach combining machine learning (simulated annealing) and agent based simulation is shown to approximate financial time series. The agent based model allows to simulate the market conditions that produced the financial time series and simulated annealing optimize the parameters for the agent based model. The originality of our approach stays in the combination of financial market simulation with meta-learning of its parameters. The original contribution of the paper stays in discussing how the methodology can be applied under several meta-learning conditions and its experimentation on the real world SPDR Gold Trust (GLD) timeseries.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Technologies, University of Naples, Naples, Italy

    Filippo Neri

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  1. Filippo Neri
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Correspondence to Filippo Neri .

Editor information

Editors and Affiliations

  1. University of Parma, Parma, Italy

    Stefano Cagnoni

  2. University of Parma, Parma, Italy

    Monica Mordonini

  3. Università degli Studi eCampus, Novedrate, Italy

    Riccardo Pecori

  4. University of Bologna, Cesena, Italy

    Andrea Roli

  5. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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Neri, F. (2019). Combining Machine Learning and Agent Based Modeling for Gold Price Prediction. In: Cagnoni, S., Mordonini, M., Pecori, R., Roli, A., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2018. Communications in Computer and Information Science, vol 900. Springer, Cham. https://doi.org/10.1007/978-3-030-21733-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-21733-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21732-7

  • Online ISBN: 978-3-030-21733-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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