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Non-linear long horizon returns predictability: evidence from six south-east Asian markets

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  • David McMillan
  • Alan Speight

Abstract

Whilst the existence of long-horizon returns predictability has been a recurrent theme in empirical finance research, extant work has focused almost exclusively on the US, and more recent work has cast doubt over the validity of such potential predictability due to non-stationarity and serial correlation in the data. The present paper examines long-horizon returns predictability in six South-East Asian markets in the context of unit root tests conducted under corrections for serial correlation and heteroscedasticity. The analysis conducted also extends the investigation of long-horizon predictability to the non-linear setting, and examines whether any detected non-linear predictability is consistent with behavioural approaches to asset pricing which emphasise the role of noise traders. The results obtained suggest the following conclusions. First, long-horizon predictability is present in each of the six South-East Asian markets considered. Second, whilst forecast power increases with horizon, for the majority of series it is maximised for forecast horizons of between 12 and 48 months. Third, non-linear predictability is reported for all series, suggesting that positive and negative values of the (demeaned) dividend yield impart different levels of predictability for returns at different forecast horizons. However, there is no consistency in the pattern of non-linearity reported, and whilst the observed patterns are consistent with noise traders models for some of the markets considered, this is not true of all the markets considered. Nevertheless, the non-linear dynamics detected are suggestive of potential market inefficiencies in all six cases. Copyright Springer Science+Business Media, LLC 2006

Suggested Citation

  • David McMillan & Alan Speight, 2006. "Non-linear long horizon returns predictability: evidence from six south-east Asian markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(2), pages 95-111, June.
    • Handle: RePEc:kap:apfinm:v:13:y:2006:i:2:p:95-111
    DOI: 10.1007/s10690-007-9036-y
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    References listed on IDEAS

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    1. Gabriel Perez‐Quiros & Allan Timmermann, 2000. "Firm Size and Cyclical Variations in Stock Returns," Journal of Finance, American Finance Association, vol. 55(3), pages 1229-1262, June.
    2. Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Long-Run Regressions: Theory and Application to US Asset Markets," Finance 0410018, University Library of Munich, Germany.
    3. Andrew Ang & Geert Bekaert, 2007. "Stock Return Predictability: Is it There?," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 651-707.
    4. John Y. Campbell & Robert J. Shiller, 2001. "Valuation Ratios and the Long-Run Stock Market Outlook: An Update," NBER Working Papers 8221, National Bureau of Economic Research, Inc.
    5. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    6. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    7. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272.
    8. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    9. McMillan, David G., 2005. "Non-linear dynamics in international stock market returns," Review of Financial Economics, Elsevier, vol. 14(1), pages 81-91.
    10. Mark E. Wohar & David E. Rapach, 2005. "Valuation ratios and long-horizon stock price predictability," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 327-344.
    11. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.
    12. Markku Lanne, 2002. "Testing The Predictability Of Stock Returns," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 407-415, August.
    13. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    14. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    15. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    16. Wolf, Michael, 2000. "Stock Returns and Dividend Yields Revisited: A New Way to Look at an Old Problem," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 18-30, January.
    17. Fang, Yue & Xu, Daming, 2003. "The predictability of asset returns: an approach combining technical analysis and time series forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 369-385.
    18. #Name#, 2001. "Non-Linear Predictability of Stock Market Returns: Evidence from Non-Parametric and Threshold Models," Discussion Paper Series, School of Economics and Finance 200102, School of Economics and Finance, University of St Andrews.
    19. Shively, Philip A., 2003. "The nonlinear dynamics of stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 43(3), pages 505-517.
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    Keywords

    Long-horizon predictability; Threshold;

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