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Non-linear trading rules in the New York Stock Exchange

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  • 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)

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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Bibliographic 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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Keywords: Technical trading rules; Nearest neighbour predictors; Security markets;

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  1. 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.
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  3. Hardle, W., 1992. "Applied Nonparametric Methods," Papers 9204, Catholique de Louvain - Institut de statistique.
  4. 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.
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  7. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
  8. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
  9. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-28, September.
  10. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
  11. Andrew W. Lo & A. Craig MacKinlay, 1987. "Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test," NBER Working Papers 2168, National Bureau of Economic Research, Inc.
  12. 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.
  13. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July.
  14. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
  15. 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.
  16. 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.
  17. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-37, July.
  18. F. FernAndez-RodrIguez & S. Sosvilla-Rivero & J. Andrada-FElix, 2003. "Technical analysis in foreign exchange markets: evidence from the EMS," Applied Financial Economics, Taylor & Francis Journals, vol. 13(2), pages 113-122.
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