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Stable classes of technical trading rules

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  • Paolo Falbo
  • Cristian Pelizzari

Abstract

Technical analysis includes a huge variety of trading rules. This fact has always been a serious hindrance to the large number of market efficiency studies implemented either to demonstrate the profitability of market-beating systems or to deny their operational feasibility. For evident reasons it is practically impossible and theoretically weak to systematically analyse the entire body of all trading rules. We therefore propose a novel method to form natural classes of trading rules which are found to be robust to changing market scenarios. In particular, groups are formed adopting a similarity measure based on the investing signals of the trading rules. Our clustering methodology adopts a Markov chain bootstrapping technique to generate differentiated scenarios preserving volume and price joint distributional features. An application is developed on a sample of 674 trading rules. Results show that six groups (here identified as trading styles) are sufficient to explain the large portion of the investing signals variance. We also suggest applications of our results to fund performance measurement and the analysis of financial markets.

Suggested Citation

  • Paolo Falbo & Cristian Pelizzari, 2011. "Stable classes of technical trading rules," Applied Economics, Taylor & Francis Journals, vol. 43(14), pages 1769-1785.
    • Handle: RePEc:taf:applec:v:43:y:2011:i:14:p:1769-1785
    DOI: 10.1080/09603100802676239
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    Cited by:

    1. Luís Lobato Macedo & Pedro Godinho & Maria João Alves, 2020. "A Comparative Study of Technical Trading Strategies Using a Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 349-381, January.

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