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

Terence Tai Leung Chong and Tau-Hing Lam

Quantitative Finance, 2010, vol. 10, issue 9, 1067-1076

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.

Keywords: Forecasting applications; Forecasting ability; Financial time series; Financial markets (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1080/14697688.2010.481630

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