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Trading and hedging in S&P 500 spot and futures markets using genetic programming

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  • Jun Wang

Abstract

In this study, genetic programming, an optimization technique based on the principles of natural evolution, was used to generate trading and hedging rules in Standard & Poor’s 500 spot and futures markets. I adopted a realistic trading process that included reasonable transaction costs, obtainable execution prices, and all the unique features of futures trading. The results suggested that the spot market was quite efficient with most genetically generated trading rules duplicating the buy‐and‐hold strategy. Most of the trading activities of these trading programs were in the futures market, where transaction costs were substantially lower. The out‐of‐sample performance of these trading rules varied from year to year, indicating that genetic programming could not consistently find outperforming technical trading rules. Some evidence was found for the superior market‐timing abilities of these rules. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:911–942, 2000

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  • Jun Wang, 2000. "Trading and hedging in S&P 500 spot and futures markets using genetic programming," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(10), pages 911-942, November.
    • Handle: RePEc:wly:jfutmk:v:20:y:2000:i:10:p:911-942
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    Cited by:

    1. Donald Lien & Y. K. Tse & Xibin Zhang, 2003. "Structural change and lead-lag relationship between the Nikkei spot index and futures price: a genetic programming approach," Quantitative Finance, Taylor & Francis Journals, vol. 3(2), pages 136-144.
    2. Liu, Xiaojia & An, Haizhong & Wang, Lijun & Jia, Xiaoliang, 2017. "An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms," Applied Energy, Elsevier, vol. 185(P2), pages 1778-1787.
    3. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.
    4. 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.
    5. 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.
    6. Ni, Yensen & Cheng, Yirung & Huang, Paoyu & Day, Min-Yuh, 2018. "Trading strategies in terms of continuous rising (falling) prices or continuous bullish (bearish) candlesticks emitted," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 188-204.
    7. 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.

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