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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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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.

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References

  • Allen, F., & Karjalainen, R. (1999). Using genetic algorithms to find technical trading rules. Journal of Financial Economics, 51(2), 245–271.

    Google Scholar 

  • Bessembinder, H., & Chan, K. (1995). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal, 3(2–3), 257–284.

    Article  Google Scholar 

  • Cai, B. M., Cai, C. X., & Keasey, K. (2005). Market efficiency and returns to simple technical trading rules: Further evidence from U.S. U.K., Asian and Chinese stock markets. Asia-Pacific Financial Markets, 12(1), 45–60.

    Article  Google Scholar 

  • Chen, S. H., & Navet, N. (2007). Failure of genetic-programming induced trading strategies: Distinguishing between efficient markets and inefficient algorithms. Intelligence in Economics and Finance. New York: Springer.

    Google Scholar 

  • How, J., Ling, M., & Verhoeven, P. (2010). Does size matter? A genetic programming approach to technical trading. Quantitative Finance, 10(2), 131–140.

    Article  Google Scholar 

  • Hudson, R., Dempsey, M., & Keasey, K. (1996). A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices-1935 to 1994. Journal of Banking and Finance, 20(6), 1121–1132.

    Article  Google Scholar 

  • Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. Cambridge: The MIT press.

    Google Scholar 

  • Neely, C., Weller, P., & Dittmar, R. (1997). Is technical analysis in the foreign exchange market profitable? A genetic programming approach. Journal of Financial and Quantitative Analysis, 32(4), 405–426.

    Article  Google Scholar 

  • Potvin, J. Y., Soriano, P., & Vallée, M. (2004). Generating trading rules on the stock markets with genetic programming. Computers & Operations Research, 31(7), 1033–1047.

    Article  Google Scholar 

  • Qu, H. (2010). Using genetic programming to find technical trading rules for SSE 50 index. Journal of Management Science, 23(5), 103–113. (in Chinese).

    Google Scholar 

  • Ratner, M., & Leal, R. P. C. (1999). Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking and Finance, 23(12), 1887–1905.

    Google Scholar 

  • Roberts, M. C. (2005). Technical analysis and genetic programming: Constructing and testing a commodity portfolio. Journal of Futures Markets, 25(7), 643–660.

    Article  Google Scholar 

  • Rode, D., Parikh, S., Friedman, Y., & Kane, J. (1995). An evolutionary approach to technical trading and capital market efficiency. Pennsylvania, PA: University of Pennsylvania, The Wharton School.

    Google Scholar 

  • Tian, G. G., Wan, G. H., & Guo, M. (2002). Market efficiency and the returns to simple technical trading rules: New evidence from US equity market and Chinese equity markets. Asia-Pacific Financial Market, 9(3), 241–258.

    Article  Google Scholar 

  • Wang, J. (2000). Trading and hedging in S &P 500 spot and futures markets using genetic programming. Journal of Futures Markets, 20(10), 911–942.

    Article  Google Scholar 

Download references

Acknowledgments

We acknowledge support from the National Natural Science Foundation of China (70932003, 71201075), the Natural Science Foundation of Jiangsu Province (BK2011561), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20120091120003) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. We thank also one referee for accurate comments.

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Authors and Affiliations

  1. School of Management and Engineering, Nanjing University, 210093 , Nanjing, China

    Hui Qu & Xindan Li

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  1. Hui Qu
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  2. Xindan Li
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Correspondence to Hui Qu.

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Qu, H., Li, X. Building Technical Trading System with Genetic Programming: A New Method to Test the Efficiency of Chinese Stock Markets. Comput Econ 43, 301–311 (2014). https://doi.org/10.1007/s10614-013-9369-8

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  • DOI: https://doi.org/10.1007/s10614-013-9369-8

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