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Recovery of hidden information from stock price data: A semiparametric approach

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  • George Vachadze

Abstract

This paper proposes a new methodology for measuring announcement effect on stock returns. This methodology requires no prior specification of the event day, event, and estimation windows, and therefore is a generalization of the traditional event study methodology. The dummy variable, which indicates whether the event occurred or not, is treated as missing. The unconditional probability of abnormal return is estimated by the EM algorithm. The probability that announcement is effective and the average announcement effect are estimated by the Gibbs sampler. How the method works is demonstrated on simulated data and IBM stock price returns. Copyright Academy of Economics and Finance 2001

Suggested Citation

  • George Vachadze, 2001. "Recovery of hidden information from stock price data: A semiparametric approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 25(3), pages 243-258, September.
    • Handle: RePEc:spr:jecfin:v:25:y:2001:i:3:p:243-258
    DOI: 10.1007/BF02745887
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    References listed on IDEAS

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    1. Salinger, Michael, 1992. "Standard Errors in Event Studies," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 27(1), pages 39-53, March.
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    Cited by:

    1. Ramiah, Vikash & Xu, Xiaoming & Moosa, Imad A., 2015. "Neoclassical finance, behavioral finance and noise traders: A review and assessment of the literature," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 89-100.

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