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Financial Market Data Simulation Using Deep Intelligence Agents

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Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection (PAAMS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11523))

Abstract

Trading strategies are often assessed against historical financial data in an effort to predict the profits and losses a strategy would generate in future. However, using only data from the past ignores the evolution of market microstructure and does not account for market conditions outside historical bounds. Simulations provide an effective supplement. We present an agent-based model to simulate financial market prices both under steady-state conditions and stress situations. Our new class of agents utilize recent advances in deep learning to make trading decisions and employ different trading objectives to ensure diversity in outcomes. The model supports various what-if scenarios such as sudden price crash, bearish or bullish market sentiment and shock contagion. We conduct evaluations on multiple asset classes including portfolio of assets and illustrate that the proposed agent decision mechanism outperforms other techniques. Our simulation model also successfully replicates the empirical stylized facts of financial markets.

N. Raman—Work conducted when author was working at Thomson Reuters.

Refinitiv—The Financial and Risk business of Thomson Reuters is now Refinitiv.

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Author information

Authors and Affiliations

  1. S&P Global, 20 Canada Square, London, UK

    Natraj Raman

  2. Refinitiv, 30 South Colonnade, London, UK

    Jochen L. Leidner

Authors
  1. Natraj Raman
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  2. Jochen L. Leidner
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Corresponding author

Correspondence to Natraj Raman .

Editor information

Editors and Affiliations

  1. Grenoble Computer Science Laboratory, Grenoble, France

    Yves Demazeau

  2. Purdue University, West Lafayette, IN, USA

    Eric Matson

  3. University of Salamanca, Salamanca, Spain

    Juan Manuel Corchado

  4. University of Salamanca, Salamanca, Spain

    Fernando De la Prieta

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Raman, N., Leidner, J.L. (2019). Financial Market Data Simulation Using Deep Intelligence Agents. In: Demazeau, Y., Matson, E., Corchado, J., De la Prieta, F. (eds) Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection. PAAMS 2019. Lecture Notes in Computer Science(), vol 11523. Springer, Cham. https://doi.org/10.1007/978-3-030-24209-1_17

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  • DOI: https://doi.org/10.1007/978-3-030-24209-1_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24208-4

  • Online ISBN: 978-3-030-24209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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