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Building Technical Trading System with Genetic Programming: A New Method to Test the Efficiency of Chinese Stock Markets

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  • Hui Qu
  • Xindan Li

Abstract

Testing whether technical trading rules can beat buy-and-hold strategy is a common approach to study the efficiency of stock markets. Noticing that the common approach of evaluating popular technical trading rules’ profitability would result in the biases of data snooping and incomplete test, we build a technical trading system with genetic programming to test the efficiency of Chinese stock markets. This system takes historical prices and volumes as inputs, randomly generates treelike structured technical trading rules composed of basic functions, and optimizes the rules using genetic programming according to the inputs. Using daily prices and volumes of Shenzhen Stock Exchange 100 index from January 2, 2004 to March 12, 2010, we find out that the optimal technical trading rules generated by our technical trading system have statistically significant out-of-sample excess returns compared with buy-and-hold strategy considering realistic transaction costs. Therefore, we conclude that Chinese stock markets have not achieved weak-form efficiency. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Hui Qu & Xindan Li, 2014. "Building Technical Trading System with Genetic Programming: A New Method to Test the Efficiency of Chinese Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 43(3), pages 301-311, March.
    • Handle: RePEc:kap:compec:v:43:y:2014:i:3:p:301-311
    DOI: 10.1007/s10614-013-9369-8
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    References listed on IDEAS

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    1. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 32(4), pages 405-426, December.
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    Cited by:

    1. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.
    2. Hakan Er & Adnan Hushmat, 2017. "The application of technical trading rules developed from spot market prices on futures market prices using CAPM," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 7(3), pages 313-353, December.

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