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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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  1. Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
  2. Fernando Fernández-Rodríguez & Christian González-Martel* & Simón Sosvilla-Rivero, "undated". "On the profitability of technical trading rules based on arifitial neural networks : evidence from the Madrid stock market," Working Papers 99-07, FEDEA.
  3. Christie, Andrew A., 1982. "The stochastic behavior of common stock variances : Value, leverage and interest rate effects," Journal of Financial Economics, Elsevier, vol. 10(4), pages 407-432, December.
  4. Fabozzi, Frank J & Francis, Jack Clark, 1977. "Stability Tests for Alphas and Betas over Bull and Bear Market Conditions," Journal of Finance, American Finance Association, vol. 32(4), pages 1093-1099, September.
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  6. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
  7. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-533, October.
  8. G. William Schwert, 2001. "Stock Volatility in the New Millennium: How Wacky Is Nasdaq?," NBER Working Papers 8436, National Bureau of Economic Research, Inc.
  9. Peter F. Christoffersen & Francis X.Diebold, 2003. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," PIER Working Paper Archive 04-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  10. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 13(1), pages 1-42.
  11. 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.
  12. Asger Lunde & Allan Timmermann, 2000. "Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets," Econometric Society World Congress 2000 Contributed Papers 1216, Econometric Society.
  13. Po-Hsuan Hsu & Chung-Ming Kuan, 2005. "Reexamining the Profitability of Technical Analysis with Data Snooping Checks," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 606-628.
  14. Gencay, Ramazan, 1998. "The predictability of security returns with simple technical trading rules," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 347-359, October.
  15. Chen, Son-Nan, 1982. "An Examination of Risk-Return Relationship in Bull and Bear Markets Using Time-Varying Betas," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(02), pages 265-286, June.
  16. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
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