IDEAS home Printed from https://ideas.repec.org/p/fiu/wpaper/1704.html
   My bibliography  Save this paper

Global Investigation of Return Autocorrelation and its Determinants

Author

Listed:
  • Pawan Jain

    () (University of Wyoming)

  • Wen-Jun Xue

    () (Department of Economics, Florida International University)

Abstract

We estimate global return autocorrelation by using the quantile autocorrelation model and investigate its determinants across 43 stock markets from 1980 to 2013. Although our results document a decline in autocorrelation across the entire sample period for all countries, return autocorrelation is significantly larger in emerging markets than in developed markets. The results further document that larger and liquid stock markets have lower return autocorrelation. We also find that price limits in most emerging markets result in higher return autocorrelation. We show that the disclosure requirement, public enforcement, investor psychology, and market characteristics significantly affect return autocorrelation. Our results document that investors from different cultural backgrounds and regulation regimes react differently to corporate disclosers, which affects return autocorrelation.

Suggested Citation

  • Pawan Jain & Wen-Jun Xue, 2017. "Global Investigation of Return Autocorrelation and its Determinants," Working Papers 1704, Florida International University, Department of Economics.
  • Handle: RePEc:fiu:wpaper:1704
    as

    Download full text from publisher

    File URL: http://economics.fiu.edu/research/working-papers/2017/1704/1704.pdf
    File Function: First version, 2017
    Download Restriction: no

    References listed on IDEAS

    as
    1. Acharya, Viral V. & Pedersen, Lasse Heje, 2005. "Asset pricing with liquidity risk," Journal of Financial Economics, Elsevier, vol. 77(2), pages 375-410, August.
    2. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1993. "Trading Volume and Serial Correlation in Stock Returns," The Quarterly Journal of Economics, Oxford University Press, vol. 108(4), pages 905-939.
    3. Rafael Porta & Florencio Lopez-De-Silanes & Andrei Shleifer, 2006. "What Works in Securities Laws?," Journal of Finance, American Finance Association, vol. 61(1), pages 1-32, February.
    4. Koenker, Roger & Xiao, Zhijie, 2006. "Quantile Autoregression," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 980-990, September.
    5. Conrad, Jennifer S & Hameed, Allaudeen & Niden, Cathy, 1994. " Volume and Autocovariances in Short-Horizon Individual Security Returns," Journal of Finance, American Finance Association, vol. 49(4), pages 1305-1329, September.
    6. Foster, F Douglas & Viswanathan, S, 1993. " Variations in Trading Volume, Return Volatility, and Trading Costs: Evidence on Recent Price Formation Models," Journal of Finance, American Finance Association, vol. 48(1), pages 187-211, March.
    7. Guillermo Llorente & Roni Michaely & Gideon Saar & Jiang Wang, 2002. "Dynamic Volume-Return Relation of Individual Stocks," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1005-1047.
    8. Paul Brockman & Dennis Y. Chung & Christophe Pérignon, 2009. "Commonality in Liquidity: A Global Perspective," Post-Print hal-00461036, HAL.
    9. Brockman, Paul & Chung, Dennis Y. & Pérignon, Christophe, 2009. "Commonality in Liquidity: A Global Perspective," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(04), pages 851-882, August.
    10. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    11. Kent Daniel & David Hirshleifer & Avanidhar Subrahmanyam, 1998. "Investor Psychology and Security Market Under- and Overreactions," Journal of Finance, American Finance Association, vol. 53(6), pages 1839-1885, December.
    12. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    13. Verrecchia, Robert E., 2001. "Essays on disclosure," Journal of Accounting and Economics, Elsevier, vol. 32(1-3), pages 97-180, December.
    14. Mech, Timothy S., 1993. "Portfolio return autocorrelation," Journal of Financial Economics, Elsevier, vol. 34(3), pages 307-344, December.
    15. Doran, James S. & Peterson, David R. & Wright, Colby, 2010. "Confidence, opinions of market efficiency, and investment behavior of finance professors," Journal of Financial Markets, Elsevier, vol. 13(1), pages 174-195, February.
    16. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    17. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    18. Tarun Chordia & Bhaskaran Swaminathan, 2000. "Trading Volume and Cross-Autocorrelations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(2), pages 913-935, April.
    19. repec:hrv:faseco:30747159 is not listed on IDEAS
    20. De Bondt, Werner F M & Thaler, Richard, 1985. " Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    21. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2000. "Commonality in liquidity," Journal of Financial Economics, Elsevier, vol. 56(1), pages 3-28, April.
    22. Gu, Anthony Yanxiang & Finnerty, Joseph, 2002. "The Evolution of Market Efficiency: 103 Years Daily Data of the Dow," Review of Quantitative Finance and Accounting, Springer, vol. 18(3), pages 219-237, May.
    23. Ito, Mikio & Sugiyama, Shunsuke, 2009. "Measuring the degree of time varying market inefficiency," Economics Letters, Elsevier, vol. 103(1), pages 62-64, April.
    24. Choi, In, 1999. "Testing the Random Walk Hypothesis for Real Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(3), pages 293-308, May-June.
    25. Pawan Jain & Quentin Chu, 2014. "Dividend clienteles: a global investigation," Review of Quantitative Finance and Accounting, Springer, vol. 42(3), pages 509-534, April.
    26. Shen, Chung-Hua & Wang, Lee-Rong, 1998. "Daily serial correlation, trading volume and price limits: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 6(3-4), pages 251-273, August.
    27. Agrawal, Anup & Nasser, Tareque, 2012. "Insider trading in takeover targets," Journal of Corporate Finance, Elsevier, vol. 18(3), pages 598-625.
    28. Wang, Jiang, 1994. "A Model of Competitive Stock Trading Volume," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 127-168, February.
    29. Hyun-Jung Ryoo & Graham Smith, 2002. "Korean stock prices under price limits: variance ratio tests of random walks," Applied Financial Economics, Taylor & Francis Journals, vol. 12(8), pages 545-553.
    30. Stephen B Salter & Frederick Niswander, 1995. "Cultural Influence on the Development of Accounting Systems Internationally: A Test of Gray's [1988] Theory," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 26(2), pages 379-397, June.
    31. Lee, Jie-Haun & Chou, Robin K., 2004. "The intraday stock return characteristics surrounding price limit hits," Journal of Multinational Financial Management, Elsevier, vol. 14(4-5), pages 485-501.
    32. David Easley & Maureen O'hara, 2004. "Information and the Cost of Capital," Journal of Finance, American Finance Association, vol. 59(4), pages 1553-1583, August.
    33. Chan, Kalok & Hameed, Allaudeen, 2006. "Stock price synchronicity and analyst coverage in emerging markets," Journal of Financial Economics, Elsevier, vol. 80(1), pages 115-147, April.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Return autocorrelation; Global stock markets; Quantile autoregression model; Legal environment; Investor psychology; Hofstede’s cultural dimensions;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fiu:wpaper:1704. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sheng Guo). General contact details of provider: http://edirc.repec.org/data/defiuus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.