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Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model

Author

Listed:
  • Qixuan Luo

    (Beijing Normal University)

  • Yu Shi

    (Beijing Normal University)

  • Xuan Zhou

    (Beijing Normal University)

  • Handong Li

    (Beijing Normal University)

Abstract

Based on the multi-agent model, an artificial stock market with four types of traders is constructed. On this basis, this paper focuses on comparing the effects of liquidation behavior on market liquidity, volatility, price discovery efficiency and long memory of absolute returns when the institutional trader adopts equal-order strategy, Volume Weighted Average Price (VWAP) strategy and Implementation Shortfall (IS) strategy respectively. The results show the following: (1) the artificial stock market based on multi-agent model can reproduce the stylized facts of real stock market well; (2) among these three algorithmic trading strategies, IS strategy causes the longest liquidation time and the lowest liquidation cost; (3) the liquidation behavior of institutional trader will significantly reduce market liquidity, price discovery efficiency and long memory of absolute returns, and increase market volatility; (4) in comparison, IS strategy has the least impact on market liquidity, volatility and price discovery efficiency, while VWAP strategy has the least impact on long memory of absolute returns.

Suggested Citation

  • Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
  • Handle: RePEc:kap:compec:v:58:y:2021:i:4:d:10.1007_s10614-020-09987-z
    DOI: 10.1007/s10614-020-09987-z
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    1. Ponta, Linda & Pastore, Stefano & Cincotti, Silvano, 2018. "Static and dynamic factors in an information-based multi-asset artificial stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 814-823.
    2. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Glosten, Lawrence R. & Harris, Lawrence E., 1988. "Estimating the components of the bid/ask spread," Journal of Financial Economics, Elsevier, vol. 21(1), pages 123-142, May.
    4. Thomas H. Noe & Michael J. Rebello & Jun Wang, 2006. "The Evolution of Security Designs," Journal of Finance, American Finance Association, vol. 61(5), pages 2103-2135, October.
    5. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    6. Jim Gatheral, 2010. "No-dynamic-arbitrage and market impact," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 749-759.
    7. Konishi, Hizuru, 2002. "Optimal slice of a VWAP trade," Journal of Financial Markets, Elsevier, vol. 5(2), pages 197-221, April.
    8. Terrence Hendershott & Ryan Riordan, 2009. "Algorithmic Trading and Information," Working Papers 09-08, NET Institute, revised Aug 2009.
    9. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    10. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233, Elsevier.
    11. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    12. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    13. Bullard, James & Duffy, John, 1998. "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207, February.
    14. Sasha Stoikov & Rolf Waeber, 2016. "Reducing transaction costs with low-latency trading algorithms," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1445-1451, September.
    15. 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.
    16. Paul Brewer & Jaksa Cvitanic & Charles R. Plott, 2013. "Market Microstructure Design and Flash Crashes: A Simulation Approach," Journal of Applied Economics, Taylor & Francis Journals, vol. 16(2), pages 223-250, November.
    17. Yasong Jin, 2017. "Optimal execution strategy and liquidity adjusted value-at-risk," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1147-1157, August.
    18. Marco Raberto & Silvano Cincotti & Sergio Focardi & Michele Marchesi, 2003. "Traders' Long-Run Wealth in an Artificial Financial Market," Computational Economics, Springer;Society for Computational Economics, vol. 22(2), pages 255-272, October.
    19. Thomas H. Noe & Michael J. Rebello & Jun Wang, 2003. "Corporate Financing: An Artificial Agent‐based Analysis," Journal of Finance, American Finance Association, vol. 58(3), pages 943-973, June.
    20. Forsyth, P.A. & Kennedy, J.S. & Tse, S.T. & Windcliff, H., 2012. "Optimal trade execution: A mean quadratic variation approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1971-1991.
    21. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    22. Lux, Thomas, 1998. "The socio-economic dynamics of speculative markets: interacting agents, chaos, and the fat tails of return distributions," Journal of Economic Behavior & Organization, Elsevier, vol. 33(2), pages 143-165, January.
    23. Michael Goldstein & Tina Viljoen & P. Joakim Westerholm & Hui Zheng, 2014. "Algorithmic Trading, Liquidity, and Price Discovery: An Intraday Analysis of the SPI 200 Futures," The Financial Review, Eastern Finance Association, vol. 49(2), pages 245-270, May.
    24. Chiarella, Carl & He, Xue-Zhong & Pellizzari, Paolo, 2012. "A Dynamic Analysis Of The Microstructure Of Moving Average Rules In A Double Auction Market," Macroeconomic Dynamics, Cambridge University Press, vol. 16(4), pages 556-575, September.
    25. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    26. Humphery-Jenner, Mark L., 2011. "Optimal VWAP trading under noisy conditions," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2319-2329, September.
    27. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    28. Gsell, Markus, 2008. "Assessing the impact of algorithmic trading on markets: A simulation approach," CFS Working Paper Series 2008/49, Center for Financial Studies (CFS).
    29. Brogaard, Jonathan & Hendershott, Terrence & Riordan, Ryan, 2017. "High frequency trading and the 2008 short-sale ban," Journal of Financial Economics, Elsevier, vol. 124(1), pages 22-42.
    30. He, Xue-Zhong & Li, Youwei, 2007. "Power-law behaviour, heterogeneity, and trend chasing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(10), pages 3396-3426, October.
    31. Christoph Frei & Nicholas Westray, 2015. "Optimal Execution Of A Vwap Order: A Stochastic Control Approach," Mathematical Finance, Wiley Blackwell, vol. 25(3), pages 612-639, July.
    32. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    33. 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..
    34. Robert Almgren, 2003. "Optimal execution with nonlinear impact functions and trading-enhanced risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(1), pages 1-18.
    35. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    36. repec:bla:jfinan:v:43:y:1988:i:1:p:97-112 is not listed on IDEAS
    37. Hisata, Yoshifumi & Yamai, Yasuhiro, 2000. "Research toward the Practical Application of Liquidity Risk Evaluation Methods," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 83-127, December.
    38. Paul Brewer & Jaksa Cvitanic & Charles R. Plott, 2013. "Market microstructure design and flash crashes: A simulation approach," Journal of Applied Economics, Universidad del CEMA, vol. 16, pages 223-250, November.
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