A Study on the Market Impact of Short-Selling Regulation Using Artificial Markets

  • Isao Yagi
  • Takanobu Mizuta
  • Kiyoshi Izumi
Part of the Studies in Computational Intelligence book series (SCI, volume 325)

Abstract

Since the subprime mortgage crisis in the United Sates, stock markets around the world have crashed, revealing their instability. To stem the decline in stock prices, short-selling regulations have been implemented in many markets. However, their effectiveness remains unclear. In this paper, we discuss the effectiveness of short-selling regulation using artificial markets. An artificial market is an agent-based model of financial markets. We constructed an artificial market that allows short-selling and an artificial market with short-selling regulation and have observed the stock prices in both of these markets. We found that the market in which short-selling was allowed was more stable than the market with short-selling regulation, and a bubble emerged in the regulated market. We evaluated the values of assets of agents who used three trading strategies, specifically, these agents were fundamentalists, chartists, and noise traders. The fundamentalists had the best performance among the three types of agents. Finally, we observe the price variations when the market price affects the theoretical price.

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References

  1. 1.
    Arthur, W., Holland, J., LeBaron, B., Palmer, R., Tayler, P.: Asset pricing under endogenous expectations in an artificial stock market. In: The Economy as an Evolving Complex System II, pp. 15–44. Addison-Wesley, Reading (1997)Google Scholar
  2. 2.
    Arturo, B., Goetzmann, W.N., Zhu, N.: Efficiency and the bear: short sales and markets around the world, Yale ICF Working Paper No.02-45 (2004)Google Scholar
  3. 3.
    Chen, S.-H., Yeh, C.-H.: On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis. Journal of Economic Behavior & Organization 49(2), 217–239 (2002)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Darley, V., Outkin, A.V.: A NASDAQ Market Simulation: Insights on a Major Market from the Science of Complex Adaptive Systems. World Scientific Pub. Co. Inc., Singapore (2007)Google Scholar
  5. 5.
    De Long, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J.: Noise Trader Risk in Financial Markets. Journal of Political Economy 68(4), 703–738 (1990)CrossRefGoogle Scholar
  6. 6.
    Hara, A., Nagao, T.: Construction and analysis of stock markets model using ADG; automatically defined groups. International Journal of Computational Intelligence and Application 2(4), 433–446 (2002)CrossRefGoogle Scholar
  7. 7.
    Izumi, K., Toriumi, F., Matsui, H.: Evaluation of automated-trading programs using an artificial market. NeuroComputing (forthcoming)Google Scholar
  8. 8.
    Marsh, I.W., Niemer, N.: The impact of short sales restrictions, The London Investment Banking Association (2008)Google Scholar
  9. 9.
    Saffi, P.A.C., Sigurdsson, K.: Price efficiency and short-selling, AFA 2008 New Orleans Meeting Paper (2007)Google Scholar
  10. 10.
    Shiller, R.J.: Bubbles, Human Judgement, and Expert Opinion. Financial Analysts Journal 58, 18–26 (2001)CrossRefGoogle Scholar
  11. 11.
    Soros, G.: The Alchemy of Finance. John Wiley & Sons. Inc., Chichester (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Isao Yagi
    • 1
  • Takanobu Mizuta
    • 2
  • Kiyoshi Izumi
    • 3
  1. 1.Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyYokohamaJapan
  2. 2.SPARX Asset Management Co.Ltd., Gate City OhsakiTokyoJapan
  3. 3.Digital Human Research CenterNational Institute of Advanced Industrial Science and TechnologyTokyoJapan

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