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Building an Artificial Stock Market Populated by Reinforcement-Learning Agents

Listed author(s):
  • Tomas Ramanauskas


    (Bank of Lithuania)

  • Aleksandras Vytautas Rutkauskas

    (Vilnius Gediminas Technical University)

Registered author(s):

    In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup.

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    Paper provided by Bank of Lithuania in its series Bank of Lithuania Working Paper Series with number 6.

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    Length: 35 pages
    Date of creation: 04 Sep 2009
    Handle: RePEc:lie:wpaper:6
    Contact details of provider: Postal:
    Bank of Lithuania Gedimino pr. 6, LT-01103 Vilnius, Lithuania

    Phone: 22 40 08
    Fax: 22 15 01
    Web page:

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