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Post-Earnings Announcement Drift in Multimarket Setting

Published:25 March 2021Publication History

ABSTRACT

This paper investigates the post-earnings announcement drift (PEAD) in the multiple markets and finds its determinants in an international setting. Using the Shenzhen-Hong Kong Connect, we investigate whether and how an exogenous event in the segmented markets affect the post earnings announcement drift in the multimarket setting. We find that the PEAD declines after the launch of Shenzhen-Hong Kong Connect in both A-share market and Hong Kong market. Then, we further investigate the multifactorial causes of the PEAD and our findings suggest that the PEAD is directly driven by investors’ limited attention and limits to arbitrage. These findings support the mispricing explanation of the PEAD in multimarket setting. We also find that the improvement of liquidity and volatility influences the determinants of the PEAD. Finally, our findings provide the evidence that the information asymmetry is the potential cause of the PEAD.

References

  1. Angrist, J., and A. Krueger. 2001. Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives 15, 69–85.Google ScholarGoogle ScholarCross RefCross Ref
  2. Ball R., Brown P. An Empirical Evaluation of Accounting Income Numbers. Journal of Accounting Research 6, 159-178.Google ScholarGoogle Scholar
  3. Griffin, J, P. Kelly, and F. Nardari. 2010. Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review ofFinancial Studies 23, 3225–77.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bernard, V. L., Thomas, J. K., & Ball, R., 1990. Evidence That Stock Prices do not fully reflect the Imolications of current earnings for future earnings. Journal of Acconting and Economics 13, 305–340.Google ScholarGoogle ScholarCross RefCross Ref
  5. Barberis, N., A. Shleifer, and R. Vishny. 1998. Amodel of investor sentiment. Journal of Financial Economics 49, 307–43.Google ScholarGoogle ScholarCross RefCross Ref
  6. Mendenhall, R. R., 2017. Arbitrage Risk and Post-Earnings-Announcement Drift. The Journal of Business 77, 875–894.Google ScholarGoogle ScholarCross RefCross Ref
  7. Hung, M., Li, X., Wang, S.,. 2015. Post-earnings-announcement drift in global markets: Evidence from an information shock. Review of Financial Studies 28, 1242–1283.Google ScholarGoogle ScholarCross RefCross Ref
  8. Sadka, R., 2006. Momentum and post-earnings-announcement drift anomalies: The role of liquidity risk. Journal of Financial Economics 80, 309–349.Google ScholarGoogle ScholarCross RefCross Ref
  9. Bhushan.R,,1994, An informational efficiency perspective on the post-earnings announcement drift, Journal of Accounting and Economics, 18(1):45-67.Google ScholarGoogle ScholarCross RefCross Ref
  10. Hirshleifer, D., S. S. Lim, and S. H. Teoh. 2009. Driven to distraction: Extraneous events and underreaction to earnings news. Journal ofFinance 64, 2289–325.Google ScholarGoogle ScholarCross RefCross Ref
  11. Fama E. F., MacBeth J. D. 1973. Risk, return, and equilibrium: Empirical tests. Journal of political economy 81, 607-636.Google ScholarGoogle ScholarCross RefCross Ref
  12. Sadka, G., Sadka, R. The Post-Earnings-Announcement Drift and Liquidity: Level, Risk, and profitability of Trading. 2004. Working paper.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

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    ICSEB '20: Proceedings of the 2020 4th International Conference on Software and e-Business
    December 2020
    119 pages
    ISBN:9781450388849
    DOI:10.1145/3446569

    Copyright © 2020 ACM

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    New York, NY, United States

    Publication History

    • Published: 25 March 2021

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