Search and Evaluation of Stock Ranking Rules Using Internet Activity Time Series and Multiobjective Genetic Programming

Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

Hundreds of millions of people are daily active on the internet. They view webpages, search for different terms, post their thoughts, or write blogs. Time series can be built from the popularity of different terms on webpages, search engines, or social networks. It was already shown in multiple publications that popularity of some terms on Google, Wikipedia, Twitter, or Facebook can predict moves on the stock market. We are trying to find relations between internet popularity of company names and the rank of the company’s stock. Popularity is represented by time series of Google Trends data and Wikipedia view count data. We use multiobjective genetic programming (MOGP) to find these relations. MOGP is using evolutionary operators to find tree-like solutions to multiobjective problems and is popular in financial investing in the last years. Stock rank is used in an investment strategy to find stock portfolios in our implementation, revenue and standard deviation are used as objectives. Solutions found by the MOGP algorithm show the relation between the internet popularity and stock rank. It is also shown that such data can help to achieve higher revenue with lower risk. Evaluation is done by comparing the results with different investment strategies, not only the market index.

Keywords

Internet activity Multiobjective genetic programming Portfolio 

Notes

Acknowledgment

This research has been supported by a VUB grant no. 2015-3-02/5.

References

  1. 1.
    Bohdalová, M.: A comparison of value-at-risk methods for measurement of the financial risk. In: The Proceedings of the E-Leader, pp. 1–6. CASA, New~York, (2007)Google Scholar
  2. 2.
    Bohdalová, M., Šlahor, L.: Modeling of the risk factors in correlated markets using a multivariate t-distributions. Appl. Nat. Sci. 2007, 162–172 (2007)Google Scholar
  3. 3.
    Bohdalová, M., Šlahor, L.: Simulations of the correlated financial risk factors. J. Appl. Math. Stat. Inf. 4(1), 89–97 (2008)Google Scholar
  4. 4.
    Preis, T., Moat, S.H., Stanley, H.E.: Quantifying trading behavior in financial markets using Google Trends. Sci. Rep. 3, 1684 (2013)Google Scholar
  5. 5.
    Moat, H.S., Curme, CH., Avakian, A., Kenett, D.Y., Stanley, H.E., Preis, T.: Quantifying Wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013)Google Scholar
  6. 6.
    Ruiz, J.E., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with micro-blogging activity. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, New~York, NY, USA, pp. 513–522 (2012)Google Scholar
  7. 7.
    Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. http://www.gp-field-guide.org.uk (2008)
  8. 8.
    Ghosh, A., Dehuri, S.: Evolutionary algorithms for multicriterion optimization: a survey. Int. J. Comput. Inform. Sci. 2(1), 38–57 (2005)Google Scholar
  9. 9.
    Mullei, S., Beling, P.: Hybrid evolutionary algorithms for a multiobjective financial problem. In: Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics, 11–14 October 1998, San Diego, CA, USA, vol. 4, pp. 3925–3930 (1998)Google Scholar
  10. 10.
    Becker, Y.L., Fei, P., Lester, A.: Stock selection—an innovative application of genetic programming methodology. In: Genetic Programming Theory and Practice IV, pp. 315–334. Springer, New York (2007)Google Scholar
  11. 11.
    Huang, C.F., Chang, C.H., Chang, B.R., Cheng, D.W.: A study of a hybrid evolutionary fuzzy model for stock selection. In: Proceeding of the 2011 IEEE International Conference on Fuzzy Systems, 27–30 June 2011, pp. 210–217. Taipei (2011)Google Scholar
  12. 12.
    Chen, S.S., Huang, C.F., Hong, T.P.: A multi-objective genetic model for stock selection. Proceedings of The 27th Annual Conference of the Japanese Society for Artificial Intelligence, Toyama, Japan, 4–7 June 2013Google Scholar
  13. 13.
    Chen, S.S., Huang, C.F., Hong, T.P.: An improved multi-objective genetic model for stock selection with domain knowledge. In: Technologies and Applications of Artificial Intelligence, Lecture Notes in Computer Science, vol. 8916, pp. 66–73. Springer (2014)Google Scholar
  14. 14.
    Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. J. Financ. Econ. 51, 245–271 (1999)CrossRefGoogle Scholar
  15. 15.
    Neely, C.H.: Risk-adjusted, ex-ante, optimal technical trading rules in equity markets. Int. Rev. Econ. Financ. 12, 69–87 (1999)CrossRefGoogle Scholar
  16. 16.
    Potvin, J.Y., Soriano, P., Vallée, M.: Generating trading rules on the stock markets with genetic programming. Comput. Oper. Res. 31(7), 1033–1047 (2004)CrossRefMATHGoogle Scholar
  17. 17.
    Becker, L.A., Seshadri, M.: GP-evolved technical rules can outperform buy and hold. In: Proceedings of the 6th International Conference on Computational Intelligence and Natural Computing, pp. 26–30 (2003)Google Scholar
  18. 18.
    Lohpetch, D., Corne, D.: Discovering effective technical trading rules with genetic programming: towards robustly outperforming buy-and-hold. World Congress on Nature and Biologically Inspired Computing, pp. 431–467 (2009)Google Scholar
  19. 19.
    Lohpetch, D., Corne, D.: Outperforming buy-and-hold with evolved technical trading rules: daily, weekly and monthly trading. In: Proceedings of the 2010 International Conference on Applications of Evolutionary Computation, vol. 6025, pp. 171–181. Valencia, Spain (2010)Google Scholar
  20. 20.
    Lohpetch, D., Corne, D.: Multiobjective algorithms for financial trading: Multiobjective out-trades single-objective. IEEE Congress on Evolutionary Computation, 5–8 June 2011, New~Orleans, LA, USA, pp. 192–199 (2011)Google Scholar
  21. 21.
    Briza, A.C., Naval, P.C.: Design of stock trading system for historical market data using multiobjective particle swarm optimization of technical indicators. In: Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Computation, pp. 1871–1878. Atlanta, Georgia, USA (2008)Google Scholar
  22. 22.
    Hassan, G.N.A.: Multiobjective genetic programming for financial portfolio management in dynamic environments. Doctoral Dissertation, University College London (2010)Google Scholar
  23. 23.
    Tapia, G.C., Coello, C.A.: Applications of multi-objective evolutionary algorithms in economics and finance: a survey. IEEE Congress on Evolutionary Computation, pp. 532–539 (2007)Google Scholar
  24. 24.
    Chen, S.H., Navet, N.: Failure of genetic-programming induced trading strategies: distinguishing between efficient markets and inefficient algorithms. Comput. Intell. Econ. Finan. 2, 169–182 (2007)CrossRefGoogle Scholar
  25. 25.
    Jakubéci, M.: Výber portfólia akcií s využitím genetického programovania a údajov o popularite na internete. VII. mezinárodní vědecká konference doktorandů a mladých vědeckých pracovníků, pp. 47–56. Opava, Czech Republic (2014)Google Scholar
  26. 26.
    Jakubéci, M.: Evaluation of investment strategies created by multiobjective genetic programming. In: Proceedings of the 7th International Scientific Conference Finance and Performance of Firms in Science, Education and Practice, 23–24 April 2015, pp. 498–509. Zlin, Czech Republic (2015)Google Scholar
  27. 27.
    Domiana, D.L., Loutonb, D.A., Mossmanc, C.E.: The rise and fall of the Dogs of the Dow. Financ. Serv. Rev. 7(3), 145–159 (1998)CrossRefGoogle Scholar
  28. 28.
    Kirkpatrick, C., Dahlquist, J.: Technical analysis. FT Press, Upper Saddle River, NJ (2010)Google Scholar
  29. 29.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. In: Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100. International Center for Numerical Methods in Engineering, BarcelonaGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Information Systems, Faculty of ManagementComenius University in BratislavaBratislavaSlovakia

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