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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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  • Neely, Christopher J., 2003. "Risk-adjusted, ex ante, optimal technical trading rules in equity markets," International Review of Economics & Finance, Elsevier, vol. 12(1), pages 69-87.
    • Handle: RePEc:eee:reveco:v:12:y:2003:i:1:p:69-87
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    2. 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).
    3. GIOT, Pierre & PETITJEAN, Mikael, 2006. "International stock return predictability: statistical evidence and economic significance," LIDAM Discussion Papers CORE 2006088, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    6. Janice How & Martin Ling & Peter Verhoeven, 2010. "Does size matter? A genetic programming approach to technical trading," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 131-140.
    7. Marco Corazza & Claudio Pizzi & Andrea Marchioni, 2024. "A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization," Computational Management Science, Springer, vol. 21(1), pages 1-29, June.
    8. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.
    9. Balvers, Ronald & Wu, Yangru, 2010. "Optimal transaction filters under transitory trading opportunities: Theory and empirical illustration," Journal of Financial Markets, Elsevier, vol. 13(1), pages 129-156, February.
    10. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    11. Ślepaczuk Robert & Sakowski Paweł & Zakrzewski Grzegorz, 2018. "Investment Strategies that Beat the Market. What Can We Squeeze from the Market?," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(4), pages 36-55, December.
    12. Wang, Zi-Mei & Chiao, Chaoshin & Chang, Ya-Ting, 2012. "Technical analyses and order submission behaviors: Evidence from an emerging market," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 109-128.
    13. 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.
    14. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    15. Pierre Giot & Mikael Petitjean, 2011. "On the statistical and economic performance of stock return predictive regression models: an international perspective," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 175-193.
    16. 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, September.
    17. Yu, Hao & Nartea, Gilbert V. & Gan, Christopher & Yao, Lee J., 2013. "Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 356-371.
    18. 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.

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