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Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network

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Author Info
Bekiros, S. () (Universiteit van Amsterdam)
Georgoutsos, D.

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Abstract

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 2/8/1971 \u2013 4/7/1998, while the sub-period 4/8/1998 - 2/5/2002 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 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 & 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.

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Paper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 06-16.

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Date of creation: 2006
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Handle: RePEc:ams:ndfwpp:06-16

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  1. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October. [Downloadable!] (restricted)
  2. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 13(1), pages 1-42.
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  3. M. Hashem Pesaran & Allan Timmermann, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," University of California at San Diego, Economics Working Paper Series 95-19, Department of Economics, UC San Diego.
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  4. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October. [Downloadable!] (restricted)
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  5. Peter F. Christoffersen & Francis X. Diebold, 2003. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," NBER Working Papers 10009, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  6. Paul R. Krugman, 1987. "Trigger Strategies and Price Dynamics in Equity and Foreign Exchange Markets," NBER Working Papers 2459, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  7. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December. [Downloadable!] (restricted)
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  8. 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.
  9. William Schwert, G., 2002. "Stock volatility in the new millennium: how wacky is Nasdaq?," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 3-26, January. [Downloadable!] (restricted)
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  10. 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. [Downloadable!] (restricted)
  11. Pedro de Lima, 1996. "Nuisance parameter free properties of correlation integral based statistics," Econometric Reviews, Taylor and Francis Journals, vol. 15(3), pages 237-259. [Downloadable!] (restricted)
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