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Technical analysis and genetic programming: Constructing and testing a commodity portfolio

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  • Matthew C. Roberts

Abstract

Although academic research on the usefulness of technical analysis is mixed at best, its use remains widespread in commodity markets. Much prior research into technical analysis suffers from data‐snooping biases. Using genetic programming, ex ante optimal technical trading strategies are identified. Because they are mechanically generated from simple arithmetic operators, they are free of the data‐snooping bias common in technical analysis research. Futures prices from 24 markets are used in rule generation. Rules for only two of the markets are capable of generating profits at the 5% level of significance using out‐of‐sample data, lending little support for technically based systems. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:643–660, 2005

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  • Matthew C. Roberts, 2005. "Technical analysis and genetic programming: Constructing and testing a commodity portfolio," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(7), pages 643-660, July.
    • Handle: RePEc:wly:jfutmk:v:25:y:2005:i:7:p:643-660
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    Cited by:

    1. Montgomery, William & Raza, Ahmad & Ülkü, Numan, 2019. "Tests of technical trading rules and the 52-week high strategy in the corporate bond market," Global Finance Journal, Elsevier, vol. 40(C), pages 85-103.
    2. Jasdeep S. Banga & B. Wade Brorsen, 2019. "Profitability of alternative methods of combining the signals from technical trading systems," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 26(1), pages 32-45, January.
    3. Fong, Tom Pak Wing & Wu, Shui Tang, 2020. "Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    4. Ioana-Andreea Boboc & Mihai-Cristian Dinică, 2013. "An Algorithm for Testing the Efficient Market Hypothesis," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    5. Andrei Shynkevich, 2021. "Impact of bitcoin futures on the informational efficiency of bitcoin spot market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 115-134, January.
    6. Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.

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