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Non-linear trading rules in the New York Stock Exchange

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Author Info
Julián Andrada Félix () (Universidad de Las Palmas de Gran Canaria; Facultad de Ciencias Económicas y Empresariales; Departamento de Métodos Cuantitativos en Economía y Gestión; c/Saulo Torón 4, 35017 Las Palmas de G.C. España; Tfno (0034) 928458959)
Fernando Fernández Rodríguez () (Universidad de Las Palmas de Gran Canaria; Facultad de Ciencias Económicas y Empresariales; Departamento de Métodos Cuantitativos en Economía y Gestión; c/Saulo Torón 4, 35017 Las Palmas de G.C. España; Tfno (0034) 928451802)
María Dolores García Artiles () (Universidad de Las Palmas de Gran Canaria; Facultad de Ciencias Económicas y Empresariales; Departamento de Métodos Cuantitativos en Economía y Gestión; c/Saulo Torón 4, 35017 Las Palmas de G.C. España; Tfno (0034) 928451807, Universidad Complutense de Madrid; Facultad de Ciencias Económicas; Departamento Economía Cuantitativa Fundamentos del Análisis Económico II; Campus de Somosaguas; 28223 Madrid; España; Tfno (0034)913942384)

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Abstract

In this paper we investigate the profitability of non-linear trading rules based on nearest neighbour (NN)predictors. Applying this investment strategy to the New York Stock Exchange, our results suggest that, taking into account transaction costs, the NN-based trading rule is superior to both a risk-adjusted buy-and-hold strategy and a linear ARIMA-based strategy in terms of returns for all of the years studied (1997-2002). Regarding other profitability measures, the NN-based trading rule yields higher Sharpe ratios than the ARIMA-based strategy for all of the years in the sample except for 2001. As for 2001, in 36 out of the 101 cases considered, the ARIMA-based strategy gives higher Sharpe ratios than those from the NN-trading rule, in 18 cases the opposite is true, and in the remaining 36 cases both strategies yield the same ratios.

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Publisher Info
Paper provided by Facultad de Ciencias Económicas de la ULPGC in its series Documentos de trabajo conjunto ULL-ULPGC with number 2004-05.

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Length: 39 pages
Date of creation: May 2004
Date of revision:
Handle: RePEc:can:series:2004-05

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Related research
Keywords: Technical trading rules; Nearest neighbour predictors; Security markets;

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References listed on IDEAS
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  1. 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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  2. 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)
    Other versions:
  3. Fernando Fernandez-Rodriguez & Simon Sosvilla-Rivero & Maria Dolores Garcia-Artiles, 1997. "Using nearest neighbour predictors to forecast the Spanish stock market," Investigaciones Economicas, Fundación SEPI, vol. 21(1), pages 75-91, January. [Downloadable!]
  4. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July. [Downloadable!] (restricted)
  5. repec:att:wimass:199520 is not listed on IDEAS
  6. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules1," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February. [Downloadable!] (restricted)
  7. Sweeney, Richard J., 1988. "Some New Filter Rule Tests: Methods and Results," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 23(03), pages 285-300, September. [Downloadable!]
  8. Wolfgang Hardle & Oliver Linton, 1994. "Applied Nonparametric Methods," Cowles Foundation Discussion Papers 1069, Cowles Foundation, Yale University. [Downloadable!]
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  9. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July. [Downloadable!] (restricted)
  10. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June. [Downloadable!] (restricted)
  11. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 1(1), pages 41-66. [Downloadable!] (restricted)
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  12. F. FernÁndez-RodrÍguez & S. Sosvilla-Rivero & J. Andrada-FÉlix, 2003. "Technical analysis in foreign exchange markets: evidence from the EMS," Applied Financial Economics, Taylor and Francis Journals, vol. 13(2), pages 113-122, January. [Downloadable!] (restricted)
  13. Hsieh, David A, 1991. " Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-77, December. [Downloadable!] (restricted)
  14. Fernando Fernández-Rodríguez & Simón Sosvilla-Rivero & Julián Andrada-Félix, . "Nearest-Neighbour Predictions in Foreign Exchange Markets," Working Papers 2002-05, FEDEA. [Downloadable!]
  15. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-91, January. [Downloadable!] (restricted)
    Other versions:
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