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Understanding Financial Market States Using Artificial Double Auction Market

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  • Kyubin Yim
  • Gabjin Oh
  • Seunghwan Kim

Abstract

The ultimate value of theories of the fundamental mechanisms comprising the asset price in financial systems will be reflected in the capacity of such theories to understand these systems. Although the models that explain the various states of financial markets offer substantial evidences from the fields of finance, mathematics, and even physics to explain states observed in the real financial markets, previous theories that attempt to fully explain the complexities of financial markets have been inadequate. In this study, we propose an artificial double auction market as an agent-based model approach to study the origin of complex states in the financial markets, characterizing important parameters with an investment strategy that can cover the dynamics of the financial market. The investment strategy of chartist traders after market information arrives should reduce market stability originating in the price fluctuations of risky assets. However, fundamentalist traders strategically submit orders with a fundamental value and, thereby stabilize the market. We construct a continuous double auction market and find that the market is controlled by a fraction of chartists, P_{c}. We show that mimicking real financial markets state, which emerges in real financial systems, is given between approximately P_{c} = 0.40 and P_{c} = 0.85, but that mimicking the efficient market hypothesis state can be generated in a range of less than P_{c} = 0.40. In particular, we observe that the mimicking market collapse state created in a value greater than P_{c} = 0.85, in which a liquidity shortage occurs, and the phase transition behavior is P_{c} = 0.85.

Suggested Citation

  • Kyubin Yim & Gabjin Oh & Seunghwan Kim, 2015. "Understanding Financial Market States Using Artificial Double Auction Market," Papers 1503.00913, arXiv.org.
  • Handle: RePEc:arx:papers:1503.00913
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    References listed on IDEAS

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    1. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
    2. Unknown, 2004. "Reviews in Brief," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 59(2), pages 1-3.
    3. Unknown, 2004. "Book Reviews," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 59(1), pages 1-17.
    4. anonymous, 2004. "Revisions to Regulation Z," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Spr, pages 199-199.
    5. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    6. Unknown, 2004. "Book Reviews," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 59(2), pages 1-15.
    7. Unknown, 2004. "Reviews in Brief," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 59(1), pages 1-2.
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