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Can the neuro fuzzy model predict stock indexes better than its rivals?

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Author Info

  • Chin-Shien Lin

    (Department of Finance, Providence University)

  • Haider Ali Khan

    (GSIS, University of Denver and CIRJE, Faculty of Economics, University of Tokyo)

  • Chi-Chung Huang

    (Graduate School of Business Administration, Providence University)

Abstract

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

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File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2002/2002cf165.pdf
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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-165.

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Length: 49 pages
Date of creation: Aug 2002
Date of revision:
Handle: RePEc:tky:fseres:2002cf165

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  1. John Y. Campbell, 1985. "Stock Returns and the Term Structure," NBER Working Papers 1626, National Bureau of Economic Research, Inc.
  2. Bessembinder, Hendrik & Chan, Kalok, 1995. "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 3(2-3), pages 257-284, July.
  3. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
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