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The asymmetric information and price manipulation in stock market

Author

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  • Jer-Shiou Chiou
  • Pei-Shan Wu
  • Antony Chang
  • Bor-Yi Huang

Abstract

The interaction between asymmetrically informed traders has been mostly investigated in theoretical frameworks. Not only there are relatively few empirical studies but, if any, the mostly focus on cross-sectional analysis and use very short samples. In this study, we blend theoretic with empirical, and propose a new signalling system of turning points in the economy to examine the extent of volatility of these markets relative to their tranquil periods. The signalling system proposed here is based on the Markov-switching model. Differing from the existing literatures, the study employs three phases and time-varying transition probability, and captures the states of volatility. After examining the causality between high volatility and foreign portfolio investment (FPI) by using moving average and generalized autoregressive conditional heteroskedasticity, the portfolio's profitability of FPI and individual investors in different periods are compared. Finally, the investigation of FPI's leading effect is studied.

Suggested Citation

  • Jer-Shiou Chiou & Pei-Shan Wu & Antony Chang & Bor-Yi Huang, 2007. "The asymmetric information and price manipulation in stock market," Applied Economics, Taylor & Francis Journals, vol. 39(7), pages 883-891.
    • Handle: RePEc:taf:applec:v:39:y:2007:i:7:p:883-891
    DOI: 10.1080/00036840500462038
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    References listed on IDEAS

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    1. Masahiro Watanabe, 2002. "Rational Trend Followers and Contrarians in Excessively Volatile, Correlated Markets," Yale School of Management Working Papers ysm267, Yale School of Management.
    2. Lawrence H. Summers, 1986. "Debt problems and macroeconomic policies," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 165-208.
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    Cited by:

    1. Michael Aitken & Frederick Harris & Shan Ji, 2015. "A Worldwide Examination of Exchange Market Quality: Greater Integrity Increases Market Efficiency," Journal of Business Ethics, Springer, vol. 132(1), pages 147-170, November.
    2. Alex YiHou Huang, 2012. "Volatility forecasting by quantile regression," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 423-433, February.
    3. Edward Curran & Jack Hunt & Vito Mollica, 2021. "Single stock futures and their impact on market quality: Be careful what you wish for," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(11), pages 1677-1692, November.
    4. Kulabutr Komenkul & Mohamed Sherif & Bing Xu, 2017. "IPOs’ signalling effects for speculative stock detection: evidence from the Stock Exchange of Thailand," Applied Economics, Taylor & Francis Journals, vol. 49(31), pages 3067-3085, July.
    5. Liu, Qingbai & Wang, Chuanjie & Zhang, Ping & Zheng, Kaixin, 2021. "Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases," International Review of Financial Analysis, Elsevier, vol. 78(C).
    6. Ke Liu & Kin Lai & Jerome Yen & Qing Zhu, 2015. "A Model of Stock Manipulation Ramping Tricks," Computational Economics, Springer;Society for Computational Economics, vol. 45(1), pages 135-150, January.
    7. Huang, Bor-Yi & Chiou, Jer-Shiou & Wu, Pei-Shan, 2007. "Abnormal profitability and foreign investment based on the investigation of covered interest parity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 475-484.
    8. Zhu, Xingting & Ma, Xiang & Rehman, Faheem Ur & Liu, Bin, 2024. "Does pension fund ownership reduce market manipulation? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).

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