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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 742182, 8 pages
How Investor Structure Influences the Yield, Information Dissemination Efficiency, and Liquidity
1College of Management and Economics, Tianjin University, Tianjin 300072, China
2China Center for Social Computing and Analytics, Tianjin University, Tianjin 300072, China
Received 8 March 2014; Accepted 12 May 2014; Published 25 May 2014
Academic Editor: Fenghua Wen
Copyright © 2014 Hongli Che et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- D. Easley, M. M. L. de Prado, and M. O'Hara, “Flow toxicity and volatility in a high frequency world,” Review of Financial Studies, vol. 25, no. 5, pp. 1457–1493, 2011.
- J. L. Holland, Making Vocational Choices: A Theory of Vocational Personalities and Work Environments, Psychological Assessment Resources, Odessa, Fla, USA, 1997.
- D. Easley, N. M. Kiefer, M. O'Hara, and J. B. Paperman, “Liquidity, information, and infrequently traded stocks,” Journal of Financeno, vol. 51, no. 4, pp. 1405–1436, 1996.
- D. Easley, N. M. Kiefer, and M. O'Hara, “The information content of the trading process,” Journal of Empirical Finance, vol. 4, no. 2-3, pp. 159–186, 1997.
- J. H. Venter and D. de Jongh, “Extending the EKOP model to estimate the probability of informed trading,” Studies in Economics and Econometrics, vol. 30, no. 2, pp. 25–39, 2006.
- Q. Lei and G. Wu, “Time-varying informed and uninformed trading activities,” Journal of Financial Markets, vol. 8, no. 2, pp. 153–181, 2005.
- D. Easley, M. M. L. de Prado, and M. O'Hara, “The volume clock: insights into the high frequency paradigm,” The Journal of Portfolio Management, vol. 39, no. 1, pp. 19–29, 2012.
- K. Nyholm, “Estimating the probability of informed trading,” Journal of Financial Research, vol. 25, no. 4, pp. 485–505, 2002.
- K. Nyholm, “Inferring the private information content of trades: a regime-switching approach,” Journal of Applied Econometrics, vol. 18, no. 4, pp. 457–470, 2003.
- D. Easley, M. M. L. de Prado, and M. O'Hara, “The microstructure of the flash crash: flow toxicity, liquidity crashes and the probability of informed trading,” The Journal of Portfolio Management, vol. 37, no. 2, pp. 118–128, 2011.
- C. Huang, X. Gong, X. Chen, and F. Wen, “Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model,” Abstract and Applied Analysis, vol. 2013, Article ID 143194, 13 pages, 2013.
- H. Levy, M. Levy, and S. Solomon, Microscopic Simulation of Financial Markets: From Investor Behavior to Market Phenomena, Academic Press, San Diego, Calif, USA, 2000.
- B. LeBaron, “Agent-based financial markets: matching stylized facts with style,” in Post Walrasian Macroeconomics: Beyond the DSGE Model, D. Colander, Ed., pp. 221–235, Cambridge University Press, 2006.
- L. Tesfatsion and K. L. Judd, “Agent-based computational finance,” in Handbook of Computational Economics, vol. 2, chapter 16, pp. 831–880, North-Holland, Amsterdam, The Netherlands, 2006.
- T. Lux and M. Marchesi, “Scaling and criticality in a stochastic multi-agent model of a financial market,” Nature, vol. 397, no. 6719, pp. 498–500, 1999.
- S. Mike and J. D. Farmar, “An empirical behavioral model of liquidity and volatility,” Journal of Economic Dynamics and Control, vol. 32, no. 1, pp. 200–234, 2008.
- G. F. Gu and W. X. Zhou, “Emergence of long memory in stock volatility from a modified Mike-Farmer model,” Europhysics Letters, vol. 86, no. 4, article 48002, 6 pages, 2009.
- Y. J. Zhang, W. Zhang, and X. Xiong, “Investment strategies and yield: based on the calculation of financial research experiments,” Journal of Management Sciences in China, vol. 13, no. 009, pp. 107–118, 2010.
- X. Xiong, M. Wen, W. Zhang, and Y. J. Zhang, “Cross-market financial risk analysis: an agent-based computational finance,” International Journal of Information Technology and Decision Making, vol. 10, no. 3, pp. 563–584, 2011.
- W. Zhang, X. Xiong, and L. J. Wei, “Analysis of market-based trading mechanisms computational experiment finance stock index futures,” China Financial Futures Exchange Research Report, 2011.
- D. Easley, R. F. Engle, and M. O'Hara, “Time-varying arrival rates of informed and uninformed traders,” Journal of Financial Econometrics, vol. 6, no. 2, pp. 171–207, 2008.
- C. Chiarella, G. Iori, and J. Perelló, “The impact of heterogeneous trading rules on the limit order book and order flows,” Journal of Economic Dynamics and Control, vol. 33, no. 3, pp. 525–537, 2009.
- J. Gil-Bazo, D. Moreno, and M. Tapia, “Price dynamics, informational efficiency, and wealth distribution in continuous double-auction markets,” Computational Intelligence, vol. 23, no. 2, pp. 176–196, 2007.