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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.

Suggested Citation

  • 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, June.
    • Handle: RePEc:bla:jfnres:v:26:y:2003:i:2:p:259-274
    DOI: 10.1111/1475-6803.00058
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    1. Fischer Black, 1989. "Mean Reversion and Consumption Smoothing," NBER Working Papers 2946, National Bureau of Economic Research, Inc.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Lili Li & Shan Leng & Jun Yang & Mei Yu, 2016. "Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, September.
    8. 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.
    9. 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.
    10. 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.
    11. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    12. Henryk Gurgul & Paweł Majdosz, 2006. "The impact of institutional investors on risk and stock return autocorrelations in the context of the Polish pension reform," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(2), pages 5-30.
    13. Hua Wang & Liao Xu, 2019. "Do exchange‐traded fund flows increase the volatility of the underlying index? Evidence from the emerging market in China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1525-1548, March.
    14. Yu-Hong Liu & I-Ming Jiang & Shih-Cheng Lee & Yu-Ting Chen, 2011. "The Valuation Of Reset Options When Underlying Assets Are Autocorrelated," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 5(2), pages 95-114.
    15. Jilong Chen & Liao Xu & Yang Zhao, 2020. "Do ETF flows increase market efficiency? Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(5), pages 4795-4819, December.
    16. 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.
    17. Xu, Liao & Yin, Xiangkang & Zhao, Jing, 2019. "The sidedness and informativeness of ETF trading and the market efficiency of their underlying indexes," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    18. Tomasz Wójtowicz & Henryk Gurgul, 2009. "Long memory of volatility measures in time series," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 19(1), pages 37-54.
    19. 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.
    20. IRSHAD Hira, 2017. "Relationship Among Political Instability, Stock Market Returns And Stock Market Volatility," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 12(2), pages 70-99, August.
    21. Henryk Gurgul & Tomasz Wójtowicz, 2006. "Long-run properties of trading volume and volatility of equities listed in DJIA index," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 16(3-4), pages 29-56.

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