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Direction-of-change forecasting using a volatility-based recurrent neural network

Listed author(s):
  • S. D. Bekiros

    (CeNDEF, Department of Quantitative Economics, University of Amsterdam, Amsterdam, The Netherlands)

  • D. A. Georgoutsos

    (Department of Accounting and Finance, Athens University of Economics and Business, Athens, Greece)

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub-period 8 April 1998 to 5 February 2002 has been reserved for out-of-sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability, after the inclusion of transaction costs, during bear market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy-and-hold strategy. We suggest that our findings can be justified by invoking either the 'volatility feedback' theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1063
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 27 (2008)
Issue (Month): 5 ()
Pages: 407-417

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Handle: RePEc:jof:jforec:v:27:y:2008:i:5:p:407-417
DOI: 10.1002/for.1063
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  13. Abhyankar, A & Copeland, L S & Wong, W, 1997. "Uncovering Nonlinear Structure in Real-Time Stock-Market Indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 1-14, January.
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