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Evolving Technical Trading Strategies Using Genetic Algorithms: A Case About Pakistan Stock Exchange

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

Abstract

Finding optimum trading strategies that maximize profit has been a human desire since the inception of the first stock market. Many techniques have been employed ever since to accomplish this goal without sacrificing much computational power and time. In this paper, Genetic Algorithms (GAs) are used to achieve the aforementioned objectives. The performances of trading strategies devised by the GA are compared with the performance of the infamous Buy and Hold (B&H) Strategy. The stocks on which the performances are compared belong to Pakistan Stock Exchange (PSX). The strategies generated by GA outperform the B&H strategies on these stocks.

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References

  1. Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finan. 25, 383–417 (1970)

    Article  Google Scholar 

  2. Fama, E.F.: Efficient capital markets: Ii. J. Finan. 46, 1575–1617 (1991)

    Article  Google Scholar 

  3. Fama, E.F.: Random walks in stock-market prices. Finan. Anal. J. 21, 55–59 (1965)

    Article  Google Scholar 

  4. Kim, N., Mangi, F.: What’s next for Asia’s best-performing stock market? (2016)

    Google Scholar 

  5. Pimenta, A., Guimarães, F.G., Carrano, E.G., Nametala, C.A.L., Takahashi, R.H.: Goldminer: a genetic programming based algorithm applied to Brazilian stock market. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 397–402. IEEE (2014)

    Google Scholar 

  6. Murphy, J.J.: Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. Penguin, New York (1999)

    Google Scholar 

  7. Neely, C.J., Weller, P.A.: Intraday technical trading in the foreign exchange market. J. Int. Money Finan. 22, 223–237 (2003)

    Article  Google Scholar 

  8. Cohen, J.B., Zinbarg, E.D., Zeikel, A.: Investment analysis and portfolio management. homewood, ill.: Richard d. irwin (1967)

    Google Scholar 

  9. Leigh, W., Frohlich, C.J., Hornik, S., Purvis, R.L., Roberts, T.L.: Trading with a stock chart heuristic. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38, 93–104 (2008)

    Article  Google Scholar 

  10. Kraus, O.Z., Grys, B.T., Ba, J., Chong, Y., Frey, B.J., Boone, C., Andrews, B.J.: Automated analysis of high-content microscopy data with deep learning. Mol. Syst. Biol. 13, 924 (2017)

    Article  Google Scholar 

  11. Fernández-Blanco, P., Bodas-Sagi, D.J., Soltero, F.J., Hidalgo, J.I.: Technical market indicators optimization using evolutionary algorithms. In: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1851–1858. ACM (2008)

    Google Scholar 

  12. Esfahanipour, A., Mousavi, S.: A genetic programming model to generate risk-adjusted technical trading rules in stock markets. Expert Syst. Appl. 38, 8438–8445 (2011)

    Article  Google Scholar 

  13. Gorgulho, A., Neves, R., Horta, N.: Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert Syst. Appl. 38, 14072–14085 (2011)

    Google Scholar 

  14. Gorgulho, A., Neves, R., Horta, N.: Using gas to balance technical indicators on stock picking for financial portfolio composition. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2041–2046. ACM (2009)

    Google Scholar 

  15. Wagman, L.: Stock portfolio evaluation: an application of genetic-programming-based technical analysis. Genet. Algorithms Genet. Program. Stanford 2003, 213–220 (2003)

    Google Scholar 

  16. Yan, W., Clack, C.D.: Evolving robust GP solutions for hedge fund stock selection in emerging markets. Soft Comput. 15, 37–50 (2011)

    Article  Google Scholar 

  17. Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M.: Stock market prediction system with modular neural networks. In: 1990 IJCNN International Joint Conference on Neural Networks, pp. 1–6. IEEE (1990)

    Google Scholar 

  18. Iba, H., Nikolaev, N.: Genetic programming polynomial models of financial data series. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 1459–1466. IEEE (2000)

    Google Scholar 

  19. Kaboudan, M.A.: Genetic programming prediction of stock prices. Comput. Econ. 16, 207–236 (2000)

    Article  MATH  Google Scholar 

  20. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co. Inc., Boston (1989)

    MATH  Google Scholar 

  21. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20, 606–626 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Namal College, Mianwali, Pakistan

    Basit Tanvir Khan, Noman Javed, Ambreen Hanif & Muhammad Adil Raja

Authors
  1. Basit Tanvir Khan
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  2. Noman Javed
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  3. Ambreen Hanif
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  4. Muhammad Adil Raja
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Corresponding authors

Correspondence to Noman Javed , Ambreen Hanif or Muhammad Adil Raja .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Khan, B.T., Javed, N., Hanif, A., Raja, M.A. (2017). Evolving Technical Trading Strategies Using Genetic Algorithms: A Case About Pakistan Stock Exchange. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_37

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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