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Risk-adjusted, ex ante, optimal technical trading rules in equity markets

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  • Neely, Christopher J.

Abstract

Allen and Karjalainen (1999) used genetic programming to develop optimal ex ante trading rules for the S&P 500 index. They found no evidence that the returns to these rules were higher than buy-and-hold returns but some evidence that the rules had predictive ability. This comment investigates the risk-adjusted usefulness of such rules and more fully characterizes their predictive content. These results extend Allen and Karjalainen's (1999) conclusion by showing that although the rules' relative performance improves, there is no evidence that the rules significantly outperform the buy-and-hold strategy on a risk-adjusted basis. Therefore, the results are consistent with market efficiency. Nevertheless, risk-adjustment techniques should be seriously considered when evaluating trading strategies.

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Bibliographic Info

Article provided by Elsevier in its journal International Review of Economics & Finance.

Volume (Year): 12 (2003)
Issue (Month): 1 ()
Pages: 69-87

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Handle: RePEc:eee:reveco:v:12:y:2003:i:1:p:69-87

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Web page: http://www.elsevier.com/locate/inca/620165

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Cited by:
  1. Robert Ślepaczuk & Grzegorz Zakrzewski & Paweł Sakowski, 2012. "Investment strategies beating the market. What can we squeeze from the market?," Working Papers 2012-04, Faculty of Economic Sciences, University of Warsaw.
  2. Cheol-Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, 09.
  3. Ronald J. Balvers & Yangru Wu, 2005. "Optimal Transaction Filters Under Transitory Trading Opportunities: Theory and Empirical Illustration," Working Papers 022005, Hong Kong Institute for Monetary Research.
  4. Scholz, Peter & Walther, Ursula, 2011. "The trend is not your friend! Why empirical timing success is determined by the underlying's price characteristics and market efficiency is irrelevant," CPQF Working Paper Series 29, Frankfurt School of Finance and Management, Centre for Practical Quantitative Finance (CPQF).
  5. Christopher J. Neely & Paul A. Weller, 2001. "Predicting exchange rate volatility: genetic programming vs. GARCH and RiskMetrics," Working Papers 2001-009, Federal Reserve Bank of St. Louis.
  6. 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.
  7. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2010. "Out-of-sample equity premium prediction: economic fundamentals vs. moving-average rules," Working Papers 2010-008, Federal Reserve Bank of St. Louis.
  8. GIOT, Pierre & PETITJEAN, Mikael, 2006. "International stock return predictability: statistical evidence and economic significance," CORE Discussion Papers 2006088, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Ülkü, Numan & Prodan, Eugeniu, 2013. "Drivers of technical trend-following rules' profitability in world stock markets," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 214-229.

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