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Predictability of nonlinear trading rules in the U.S. stock market

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  • Terence Tai-Leung Chong
  • Tau-Hing Lam

Abstract

Most of the existing technical trading rules are linear in nature. This paper investigates the predictability of nonlinear time series model based trading strategies in the U.S. stock market. The performance of the nonlinear trading rule is compared with that of the linear model based rules. It is found that the self-exciting threshold autoregressive (SETAR) model based trading rules perform slightly better than the AR rules for the Dow Jones and Standard and Poor 500, while the AR rules perform slightly better in the NASDAQ market. Both the SETAR and the AR rules outperform the VMA rules. The results are confirmed by bootstrap simulations.

Suggested Citation

  • Terence Tai-Leung Chong & Tau-Hing Lam, 2010. "Predictability of nonlinear trading rules in the U.S. stock market," Quantitative Finance, Taylor & Francis Journals, vol. 10(9), pages 1067-1076.
    • Handle: RePEc:taf:quantf:v:10:y:2010:i:9:p:1067-1076
    DOI: 10.1080/14697688.2010.481630
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    Cited by:

    1. Terence t. l. Chong & Xiaolei Wang, 2013. "Can analyst predict stock market crashes?," Economics Bulletin, AccessEcon, vol. 33(1), pages 158-166.
    2. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    3. Taicir Mezghani & Mouna Boujelbène Abbes, 2023. "Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 505-530, September.
    4. Tan, Siow-Hooi & Lai, Ming-Ming & Tey, Eng-Xin & Chong, Lee-Lee, 2020. "Testing the performance of technical analysis and sentiment-TAR trading rules in the Malaysian stock market," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

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