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Nonlinearity, data-snooping, and stock index ETF return predictability

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  • Yang, Jian
  • Cabrera, Juan
  • Wang, Tao

Abstract

This paper examines daily return predictability for eighteen international stock index ETFs. The out-of-sample tests are conducted, based on linear and various popular nonlinear models and both statistical and economic criteria for model comparison. The main results show evidence of predictability for six of eighteen ETFs. A simple linear autoregression model, and a nonlinear-in-variance GARCH model, but not several popular nonlinear-in-mean models help outperform the martingale model. The allowance of data-snooping bias using White's Reality Check also substantially weakens otherwise apparently strong predictability.

Suggested Citation

  • Yang, Jian & Cabrera, Juan & Wang, Tao, 2010. "Nonlinearity, data-snooping, and stock index ETF return predictability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 498-507, January.
  • Handle: RePEc:eee:ejores:v:200:y:2010:i:2:p:498-507
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    References listed on IDEAS

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    Cited by:

    1. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    2. Doyle, John R. & Chen, Catherine H., 2013. "Patterns in stock market movements tested as random number generators," European Journal of Operational Research, Elsevier, vol. 227(1), pages 122-132.
    3. Patrick Kuok-Kun Chu, 2016. "Analysis and Forecast of Tracking Performance of Hong Kong Exchange-Traded Funds: Evidence from Tracker Fund and X iShares A50," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-26, December.
    4. Lingras, P. & Butz, C.J., 2010. "Rough support vector regression," European Journal of Operational Research, Elsevier, vol. 206(2), pages 445-455, October.

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