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Technical trading strategies and return predictability: NYSE

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  • Ki-Yeol Kwon
  • Richard Kish
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    Abstract

    This study consists of an empirical analysis on technical trading rules (the simple price moving average, the momentum, and trading volume) utilizing the NYSE value-weighted index over the period 1962-1996, as well as, three subperiods. The methodologies employed include the traditional t-test and residual bootstrap methodology utilizing random walk, GARCH-M and GARCH-M with some instrument variables. The results indicate that the technical trading rules add a value to capture profit opportunities over a buy-hold strategy. When the trading rules are applied to the different sub-samples, the results are weaker in the last sub-period, 1985-1996. This may imply that the market is getting efficient in information over the recent years because of technological improvements.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 12 (2002)
    Issue (Month): 9 ()
    Pages: 639-653

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    Handle: RePEc:taf:apfiec:v:12:y:2002:i:9:p:639-653

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    Cited by:
    1. Alexandros E. Milionis & Evangelia Papanagiotou, 2011. "Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non linear dependencies in stock returns," Working Papers 134, Bank of Greece.
    2. Michael D. McKenzie, 2007. "Technical Trading Rules in Emerging Markets and the 1997 Asian Currency Crises," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 43(4), pages 46-73, August.
    3. Yung-Ho Chang & Massoud Metghalchi & Chia-Chung Chan, 2006. "Technical trading strategies and cross-national information linkage: the case of Taiwan stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 731-743.
    4. Lubnau, Thorben, 2014. "Spread trading strategies in the crude oil futures market," Discussion Papers 353, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    5. Stephan Schulmeister, 2008. "Profitability of Technical Stock Trading: Has it Moved from Daily to Intraday Data?," WIFO Working Papers 323, WIFO.
    6. Alexandros E. Milionis & Evangelia Papanagiotou, 2008. "A Note on the Use of Moving Average Trading Rules to Test For Weak from Efficiency in Capital Markets," Working Papers 91, Bank of Greece.
    7. Georgi Nalbantov & Rob Bauer & Ida Sprinkhuizen-Kuyper, 2006. "Equity style timing using support vector regressions," Applied Financial Economics, Taylor & Francis Journals, vol. 16(15), pages 1095-1111.
    8. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
    9. Terence Tai-Leung Chong & Wing-Kam Ng & Venus Khim-Sen Liew, 2014. "Revisiting the Performance of MACD and RSI Oscillators," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 7(1), pages 1-12, February.
    10. Laura Marta Nuã‘Ez, 2004. "Do Moving Average Rules Make Profits? A Study Using The Madrid Stock Market," Working Papers Economia wp04-03, Instituto de Empresa, Area of Economic Environment.
    11. Metghalchi, Massoud & Chang, Yung-Ho & Marcucci, Juri, 2008. "Is the Swedish stock market efficient? Evidence from some simple trading rules," International Review of Financial Analysis, Elsevier, vol. 17(3), pages 475-490, June.

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