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Understanding Financial Market States Using an Artificial Double Auction Market

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  • Kyubin Yim
  • Gabjin Oh
  • Seunghwan Kim

Abstract

The ultimate value of theories describing the fundamental mechanisms behind asset prices in financial systems is reflected in the capacity of such theories to understand these systems. Although the models that explain the various states of financial markets offer substantial evidence from the fields of finance, mathematics, and even physics, previous theories that attempt to address the complexities of financial markets in full have been inadequate. We propose an artificial double auction market as an agent-based model to study the origin of complex states in financial markets by characterizing important parameters with an investment strategy that can cover the dynamics of the financial market. The investment strategies of chartist traders in response to new market information should reduce market stability based on the price fluctuations of risky assets. However, fundamentalist traders strategically submit orders based on fundamental value and, thereby stabilize the market. We construct a continuous double auction market and find that the market is controlled by the proportion of chartists, Pc. We show that mimicking the real state of financial markets, which emerges in real financial systems, is given within the range Pc = 0.40 to Pc = 0.85; however, we show that mimicking the efficient market hypothesis state can be generated with values less than Pc = 0.40. In particular, we observe that mimicking a market collapse state is created with values greater than Pc = 0.85, at which point a liquidity shortage occurs, and the phase transition behavior is described at Pc = 0.85.

Suggested Citation

  • Kyubin Yim & Gabjin Oh & Seunghwan Kim, 2016. "Understanding Financial Market States Using an Artificial Double Auction Market," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
  • Handle: RePEc:plo:pone00:0152608
    DOI: 10.1371/journal.pone.0152608
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    References listed on IDEAS

    as
    1. P. Weber & B. Rosenow, 2005. "Order book approach to price impact," Quantitative Finance, Taylor & Francis Journals, vol. 5(4), pages 357-364.
    2. C. H. Hommes, 2001. "Financial markets as nonlinear adaptive evolutionary systems," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 149-167.
    3. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    4. Joshua D. Coval & Tyler Shumway, 2005. "Do Behavioral Biases Affect Prices?," Journal of Finance, American Finance Association, vol. 60(1), pages 1-34, February.
    5. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01011701, HAL.
    6. Yanhui Liu & Parameswaran Gopikrishnan & Pierre Cizeau & Martin Meyer & Chung-Kang Peng & H. Eugene Stanley, 1999. "The statistical properties of the volatility of price fluctuations," Papers cond-mat/9903369, arXiv.org, revised Mar 1999.
    7. Iori, Giulia, 2002. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 269-285, October.
    8. 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.
    9. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    10. Copeland, Thomas E & Galai, Dan, 1983. "Information Effects on the Bid-Ask Spread," Journal of Finance, American Finance Association, vol. 38(5), pages 1457-1469, December.
    11. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Post-Print halshs-01215947, HAL.
    12. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    13. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00983051, HAL.
    14. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    15. Wei Li & Fengzhong Wang & Shlomo Havlin & H. Eugene Stanley, 2011. "Financial factor influence on scaling and memory of trading volume in stock market," Papers 1106.1415, arXiv.org.
    16. Parameswaran Gopikrishnan & Vasiliki Plerou & Luis A. Nunes Amaral & Martin Meyer & H. Eugene Stanley, 1999. "Scaling of the distribution of fluctuations of financial market indices," Papers cond-mat/9905305, arXiv.org.
    17. Boswijk, H. Peter & Hommes, Cars H. & Manzan, Sebastiano, 2007. "Behavioral heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1938-1970, June.
    18. Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan & van de Velden, Henk, 2008. "Expectations and bubbles in asset pricing experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 116-133, July.
    19. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    20. Jean-Philippe Bouchaud & Marc Mezard & Marc Potters, 2002. "Statistical properties of stock order books: empirical results and models," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 251-256.
    21. Ilija Zovko & J Doyne Farmer, 2002. "The power of patience: a behavioural regularity in limit-order placement," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 387-392.
    22. Yamamoto, Ryuichi, 2011. "Order aggressiveness, pre-trade transparency, and long memory in an order-driven market," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1938-1963.
    23. Nicholas Barberis & Ming Huang, 2001. "Mental Accounting, Loss Aversion, and Individual Stock Returns," Journal of Finance, American Finance Association, vol. 56(4), pages 1247-1292, August.
    24. Cohen, Kalman J & Maier, Steven F & Schwartz, Robert A & Whitcomb, David K, 1981. "Transaction Costs, Order Placement Strategy, and Existence of the Bid-Ask Spread," Journal of Political Economy, University of Chicago Press, vol. 89(2), pages 287-305, April.
    25. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    26. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    27. Shlomo Benartzi & Richard H. Thaler, 1995. "Myopic Loss Aversion and the Equity Premium Puzzle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 73-92.
    28. Potters, Marc & Bouchaud, Jean-Philippe, 2003. "More statistical properties of order books and price impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 133-140.
    29. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November.
    30. 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.
    31. J. Doyne Farmer & Laszlo Gillemot & Fabrizio Lillo & Szabolcs Mike & Anindya Sen, 2004. "What really causes large price changes?," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 383-397.
    32. Maslov, Sergei & Mills, Mark, 2001. "Price fluctuations from the order book perspective—empirical facts and a simple model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 234-246.
    33. Becker, Torbjorn & Sy, Amadou, 2006. "Were bid-ask spreads in the FX market excessive during the Asian crisis?," International Review of Financial Analysis, Elsevier, vol. 15(4-5), pages 434-449.
    34. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01215947, HAL.
    35. Jean-Philippe Bouchaud & Marc Mezard & Marc Potters, 2002. "Statistical properties of stock order books: empirical results and models," Science & Finance (CFM) working paper archive 0203511, Science & Finance, Capital Fund Management.
    36. Nicholas Barberis & Ming Huang, 2001. "Mental Accounting, Loss Aversion, and Individual Stock Returns," NBER Working Papers 8190, National Bureau of Economic Research, Inc.
    37. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    38. Antypas, Antonios & Koundouri, Phoebe & Kourogenis, Nikolaos, 2013. "Aggregational Gaussianity and barely infinite variance in financial returns," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 102-108.
    39. Jean-Philippe Bouchaud & Andrew Matacz & Marc Potters, 2001. "The leverage effect in financial markets: retarded volatility and market panic," Science & Finance (CFM) working paper archive 0101120, Science & Finance, Capital Fund Management.
    40. Challet, D. & Zhang, Y.-C., 1997. "Emergence of cooperation and organization in an evolutionary game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 407-418.
    41. Liu, Yi-Fang & Zhang, Wei & Xu, Chao & Vitting Andersen, Jørgen & Xu, Hai-Chuan, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 204-215.
    42. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
    43. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Post-Print halshs-00983051, HAL.
    44. R. Yamamoto & B. LeBaron, 2010. "Order-splitting and long-memory in an order-driven market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(1), pages 51-57, January.
    45. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    46. Ho, Thomas S Y & Stoll, Hans R, 1983. "The Dynamics of Dealer Markets under Competition," Journal of Finance, American Finance Association, vol. 38(4), pages 1053-1074, September.
    47. Yi-Fang Liu & Wei Zhang & Chao Xu & J{o}rgen Vitting Andersen & Hai-Chuan Xu, 2013. "Impact of information cost and switching of trading strategies in an artificial stock market," Papers 1311.4274, arXiv.org, revised Jul 2014.
    48. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    49. Nicholas Barberis & Ming Huang & Tano Santos, 2001. "Prospect Theory and Asset Prices," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 1-53.
    50. Marc Potters & Jean-Philippe Bouchaud, 2002. "More statistical properties of order books and price impact," Science & Finance (CFM) working paper archive 0210710, Science & Finance, Capital Fund Management.
    51. Yi-Fang Liu & Wei Zhang & Chao Xu & Jørgen Vitting Andersen & Hai-Chuan Xu, 2014. "Impact of information cost and switching of trading strategies in an artificial stock market," Documents de travail du Centre d'Economie de la Sorbonne 14031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
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