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Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets

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Abstract

This paper explores a possible link between an asymmetric dynamic process of stock returns and profitable technical trading rules. Using the G-7 stock market indexes, we show that the dynamic process of daily index returns is better characterized by nonlinearity arising from an asymmetric reverting property. The asymmetric reverting property of stock returns is exploitable in generating profitable buy and sells signals for technical trading strategies. The bootstrap analysis shows that not all nonlinearities generate profitable buy and sell signals, but rather only the nonlinearities generating a consistent asymmetrical pattern of return dynamics can be exploitable for the profitability of the trading rules. The significant positive (negative) returns from buy (sell) signals are a consequence of trading rules that exploit the asymmetric nonlinear dynamics of the stock returns that revolve around positive (negative) unconditional mean returns under prior positive (negative) return patterns. Our results corroborate the arguments for the usefulness of technical trading strategies in stock market investments.

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Notes

  1. Many studies have examined the profitability of technical trading rules for the currency markets. See Olson (2004), Neely and Weller (2003, 2001), Neely et al. (1997), LeBaron (1999), Gençay (1999), Kho (1996), and Taylor and Allen (1992).

  2. Many studies have documented that predictable components of stock returns are stochastically nonlinear and are better described by an asymmetric dynamic process. For example, Nam et al. (2006, 2002, 2003), Sarantis (2001), Koutmos (1998), Sentana and Wadhwani (1992), and LeBaron (1992) explore various types of asymmetric nonlinear autoregressive processes of high frequency return series. Significant nonlinearities have been also found in exchange rates as well. See Panos et al. (1997) and further reconsiderations in Sarantis (1999) and Taylor and Peel (2000), among others. Also, the particular form of non-linearity adopted, varies across studies. For example, Chelley-Steeley (2005) employs nonlinear autoregressive models for several Eastern European stock markets. Maasoumi and Racine (2002) develop a metric entropy capable of detecting nonlinear dependence within the returns series, while Kanas (2005) employs nonlinear nonparametric techniques to the stock price-dividend relation of major stock markets. Chen et al. (2004) discuss the various methodologies used to detect nonlinearity in corporate finance. Also, the episodic nature of nonlinearity suggested by Hinich and Patterson (1995, 2005) has been successfully applied to a broad range of national equity markets. See Ammermann and Patterson (2003), Lim and Hinich (2005a, b), Bonilla et al. (2006), Romero-Meza et al. (2007), Lim et al. (2007a, b).

  3. Except for the models of consecutive prior return patterns, the nonlinear Autoregressive (NAR) models considered in this study are similar to the Threshold Autoregressive (TAR) and the smooth transition autoregressive (STAR) models. While the NAR and TAR models are designed to capture a sudden jump in the autoregressive process, the STAR model captures a smooth transition between different states of the autoregressive process. With the threshold value set to zero, the TAR model degenerates to the NAR model. With a zero threshold value and an infinite value for the adjustment parameter, the STAR model becomes the NAR model.

  4. We have also performed simulations of the 20 null models, with and without the consideration of different GARCH models. For each nonlinear autoregressive model, the simulation results are robust, regardless of the consideration of the GARCH models.

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Correspondence to Kiseok Nam.

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Choe, Ki., Krausz, J. & Nam, K. Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets. Rev Quant Finan Acc 36, 323–353 (2011). https://doi.org/10.1007/s11156-010-0180-5

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