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The Determinants of Conditional Autocorrelation in Stock Returns

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  • Michael D. McKenzie
  • Robert W. Faff

Abstract

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day-of-the-week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time-varying patterns of return autocorrelation. 2003 The Southern Finance Association and the Southwestern Finance Association.

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  • Michael D. McKenzie & Robert W. Faff, 2003. "The Determinants of Conditional Autocorrelation in Stock Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 26(2), pages 259-274.
  • Handle: RePEc:bla:jfnres:v:26:y:2003:i:2:p:259-274
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    Citations

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    Cited by:

    1. Henryk GURGUL & Tomasz WÓJTOWICZ, 2006. "Long Memory on the German Stock Exchange," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 56(09-10), pages 447-468, September.
    2. Dietmar Maringer & Tikesh Ramtohul, 2012. "Regime-switching recurrent reinforcement learning for investment decision making," Computational Management Science, Springer, vol. 9(1), pages 89-107, February.
    3. Chen, Carl R. & Su, Yuli & Huang, Ying, 2008. "Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 789-798, September.
    4. Chau, Frankie & Deesomsak, Rataporn & Lau, Marco C.K., 2011. "Investor sentiment and feedback trading: Evidence from the exchange-traded fund markets," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 292-305.
    5. Chia-Hao Lee & Shuh-Chyi Doong & Pei-I Chou, 2011. "Dynamic correlation between stock prices and exchange rates," Applied Financial Economics, Taylor & Francis Journals, vol. 21(11), pages 789-800.
    6. Warren Dean & Robert Faff, 2008. "Evidence of feedback trading with Markov switching regimes," Review of Quantitative Finance and Accounting, Springer, vol. 30(2), pages 133-151, February.
    7. Henryk Gurgul & Roland Mestel & Tomasz Wojtowicz, 2007. "Distribution of volume on the American stock market," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 1, pages 143-163.
    8. Hou, Yang & Li, Steven, 2014. "The impact of the CSI 300 stock index futures: Positive feedback trading and autocorrelation of stock returns," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 319-337.
    9. Tomasz Wojtowicz & Henryk Gurgul, 2009. "Long memory of volatility measures in time series," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 1, pages 37-54.
    10. Henryk Gurgul & Tomasz Wojtowicz, 2006. "Long-run properties of trading volume and volatility of equities listed in DJIA index," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 3, pages 29-56.
    11. Chau, Frankie & Deesomsak, Rataporn, 2015. "Business cycle variation in positive feedback trading: Evidence from the G-7 economies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 35(C), pages 147-159.
    12. Gębka, Bartosz & Wohar, Mark E., 2013. "The determinants of quantile autocorrelations: Evidence from the UK," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 51-61.
    13. McKenzie, Michael D. & Kim, Suk-Joong, 2007. "Evidence of an asymmetry in the relationship between volatility and autocorrelation," International Review of Financial Analysis, Elsevier, vol. 16(1), pages 22-40.
    14. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    15. Henryk Gurgul & Pawel Majdosz, 2006. "The impact of institutional investors on risk and stock return autocorrelation in the context of the polish pension reform," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, vol. 2, pages 5-30.

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