Data Based Stock Portfolio Construction Using Computational Intelligence

  • Asimina DimaraEmail author
  • Christos-Nikolaos Anagnostopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10750)


The stock market is everywhere in our lives and stocks are sold and bought daily. Many people believe that investing in stocks is one of the most profitable and easiest ways to make money. The lure of easy profit can be proven erroneous when starting to invest in stocks, as stock portfolio construction and management processes are laborious. Constructing and managing a portfolio is multi stage and multi criteria problem and many of the models proposed are based on supporting only one stage. Moreover, available online data may be confusing as there is no clear evidence of how to use and clarify it. Therefore, in this paper, we propose a full-scale model that will exploit open data and will support portfolio management during all stages using Computational Intelligence. Available fundamental data will be used to evaluate stocks using Genetic Algorithms. Open past data of stock prices will be used for stock forecasting using a Multi Layer Perceptron. Eventually, using all the results of precedent stages a portfolio optimization will be implemented using Genetic Algorithms.


Artificial Neural Networks Genetic algorithms Open data  Stock portfolio management Stock portfolio optimization Stock selection 


  1. 1.
    Shoaf, J.S., Foster, J.A.: A genetic algorithm solution to the efficient set problem: a technique for portfolio selection based on the Markowitz model. In: Annual Meeting, vol. 2, pp. 571–573. Decision Sciences Institute, Orlando (1996)Google Scholar
  2. 2.
    Markowitz, H.: Portfolio selection. J. Finan. 7(1), 77–91 (1952)Google Scholar
  3. 3.
    Omisore, I., Yusuf, M., Christopher, N.: The modern portfolio theory as an investment decision tool. J. Account. Tax. 4(2), 19–28 (2012)Google Scholar
  4. 4.
    Oh, K.J., Kim, T.Y., Min, S.H., Lee, H.Y.: Portfolio algorithm based on portfolio beta using genetic algorithm. Expert Syst. Appl. 30(3), 527–534 (2006)CrossRefGoogle Scholar
  5. 5.
    Muhammad, A., King, G.A.: Foreign exchange market forecasting using evolutionary fuzzy networks. In: IEEE/IAFE Computational Intelligence for Financial Engineering, pp. 213–219 (1997)Google Scholar
  6. 6.
    Pandari, A.R., Azar, A., Shavazi, A.R.: Genetic algorithms for portfolio selection problems with nonlinear objectives. Afr. J. Bus. Manag. 6(20), 6209–6216 (2012)Google Scholar
  7. 7.
    Taylor, M.P., Allen, H.: The use of technical analysis in the foreign exchange market. J. Int. Money Finan. 11(3), 304–314 (1992)CrossRefGoogle Scholar
  8. 8.
    Richardson, S., Tuna, İ., Wysocki, P.: Accounting anomalies and fundamental analysis: a review of recent research advances. J. Account. Econ. 50(2–3), 410–454 (2010)CrossRefGoogle Scholar
  9. 9.
    Edwards, R.D., Bassetti, W.H.C., Magee, J.: Technical Analysis of Stock Trends, 9th edn. CRC Press, Boca Raton (2007)zbMATHGoogle Scholar
  10. 10.
    Bauman, M.P.: A review of fundamental analysis research in accounting. J. Account. Lit. 15, 1–33 (1996)Google Scholar
  11. 11.
    Chan, L.K.C., Hamao, Y., Lakonisjok, J.: Fundamentals and stock returns in Japan. J. Finan. 46(5), 1739–1764 (1991)CrossRefGoogle Scholar
  12. 12.
    Chin, J.Y.F., Prevost, A.K., Gottesman, A.A.: Contrarian investing in a small capitalization market: evidence from New Zealand. Fin. Rev. 37, 421–446 (2002)CrossRefGoogle Scholar
  13. 13.
    Kwag, S.-W.(A.), Lee, S.W.: Value investing and the business cycle. J. Finan. Plann. 7, 1–11 (2006)Google Scholar
  14. 14.
    London Stock Exchange market Homepage. Accessed 08 Aug 2017
  15. 15.
    McCall, J.: Genetic algorithms for modeling and optimization. J. Comput. Appl. Math. 184, 205–222 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Goldberg, D.E.: Genetic Algorithms in Search. Optimization and Machine Learning, 1st edn. Addison Wesley, Boston (1989)zbMATHGoogle Scholar
  17. 17.
    Kumar, A.: Encoding schemes in genetic algorithm. Int. J. Adv. Res. IT Eng. 2(3), 1–7 (2013)Google Scholar
  18. 18.
    Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)CrossRefGoogle Scholar
  19. 19.
    Evans, J.L., Archer, S.H.: Diversification and the reduction of dispersion: an empirical analysis. J. Finan. 23(5), 761–767 (1968)Google Scholar
  20. 20.
    Nicholson, S.F.: Price - earnings ratio. Finan. Anal. J. 16(16), 43–45 (1960)CrossRefGoogle Scholar
  21. 21.
    Dreman, D.N., Lufkin, E.A.: Investor overreaction: evidence that its basis is psychological. J. Psychol. Financ. Mark. 1(1), 61–75 (2000)CrossRefGoogle Scholar
  22. 22.
    Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithm. Trans. Syst. Man Cybern. 24(4), 656–667 (1994)CrossRefGoogle Scholar
  23. 23.
    Umbarkar, A.J., Sheth, P.D.: Crossover operators in genetic algorithms: a review. ICTAT J. Soft Comput. 06(01), 1083–1092 (2015)CrossRefGoogle Scholar
  24. 24.
    Abdoun, O., Abouchabaka, J., Tajani, C.: Analyzing the performance of mutation operators to solve the travelling salesman problem. JES Int. J. Emerg. Sci. 2(1), 61–77 (2014)Google Scholar
  25. 25.
    Tsenov, A.: Simulated annealing and genetic algorithm in telecommunications network planning. Int. J. Inf. Math. Sci. 2(4), 240–245 (2006)Google Scholar
  26. 26.
    Mayilvaganan, M., Geethamani, G.S.: Performance comparison of roulette wheel selection and steady state selection in genetic nucleotide sequence. Int. J. Innov. Res. Comput. Commun. Eng. 3(4), 4271–4276 (2015)Google Scholar
  27. 27.
    Lin, W.-Y., Lee, W.-Y., Hong, T.-P.: Adapting crossover and mutation rates in genetic algorithms. J. Inf. Sci. Eng. 19, 889–903 (2003)Google Scholar
  28. 28.
    Jong, K.A.D.: Adaptive system design: a genetic approach. IEEE Trans. Syst. Man Cybern. 10, 566–574 (1980)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Schaffer, J.D. et al.: A study of control parameters affecting the online performance of genetic algorithms for function optimization. In: Proceeding of the Third International Conference on Genetic Algorithms, pp. 51–60 (1989)Google Scholar
  30. 30.
    Bäck, T.: Optimal mutation rates in genetic search. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 2–8 (1993)Google Scholar
  31. 31.
    Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Trans. Syst. Man Cybern. 16, 122–128 (1986)CrossRefGoogle Scholar
  32. 32.
    Schaffer, J.D., Morishima, A.: An adaptive crossover distribution mechanism for genetic algorithms. In: Proceeding of the Second International Conference on Genetic Algorithms, pp. 36–40 (1987)Google Scholar
  33. 33.
    Koumousis, V.K., Katsaras, C.P.: Sawtooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–28 (2006)CrossRefGoogle Scholar
  34. 34.
    Pelikan, M. Goldberg, D.E., Cantu-Paz, E.: Bayesian optimization algorithm, population sizing, and time to convergence. Technical report, Illinois Genetic Algorithms Laboratory, University of Illinois (2000)Google Scholar
  35. 35.
    Piszcz, A., Soule, T.: Genetic programming: optimal population sizes for varying complexity problems. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 953–954 (2006)Google Scholar
  36. 36.
    Lobo, F.G., Goldberg, D.E.: The parameterless genetic algorithm in practice. Inf. Sci. Inform. Comput Sci. 167(1–4), 217–232 (2004)zbMATHGoogle Scholar
  37. 37.
    Lobo, F.G., Lima, C.F.: A review of adaptive population sizing schemes in genetic algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 228–234 (2005)Google Scholar
  38. 38.
    Roeva, O., Fidanova, S., Paprzycki, M.: Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. In: Federated Conference on Computer Science and Information Systems, pp. 371–376 (2013)Google Scholar
  39. 39.
    Thawornwong, S., Enke, D.: Forecasting stock returns with artificial neural networks. In: Zhang, G.P. (ed.) Neural Networks in Business Forecasting, pp. 47–79 (2004).
  40. 40.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the National Academy of Sciences of the U.S.A., pp. 2554–2558 (1982)Google Scholar
  41. 41.
    Kohonen, T.: Self Organization and Associative Memory, 2nd edn. Springer, Heidelberg (1988). CrossRefzbMATHGoogle Scholar
  42. 42.
    Manry, M.T., Chandrasekaran, H., Hsieh, C.: Signal processing using the multilayer perceptron. In: Hu, Y.H., Hwang, J. (eds.) Handbook of Neural Network Signal Processing, pp. 1–29. CRC Press, Boca Raton (2001)Google Scholar
  43. 43.
    Johansson, E.M., Dowla, F.U., Goodman, D.M.: Backpropagation learning for multilayer feedforward neural networks using the conjugate gradient method. Int. J. Neural Syst. 2(4), 291–301 (1991)CrossRefGoogle Scholar
  44. 44.
    Karsoliya, S.: Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int. J. Eng. Trends Technol. 3(6), 714–717 (2012)Google Scholar
  45. 45.
    Hecht-Nielsen, R.: Kolmogorov’s mapping neural network existence theorem. In: IEEE First Annual International Conference on Neural Networks, vol. 3, pp. 11–13 (1987)Google Scholar
  46. 46.
    Huang, G.-B., Babri, H.: General approximation theorem on feedforward networks. In: International Conference on Information, Communications and Signal Processing, Singapore, 9–12 September, pp. 698–702 (1997)Google Scholar
  47. 47.
    LeCun, Y., Bottou, L., Orr, G.B., Müller, K.-R.: Efficient BackProp. In: Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 1524, pp. 9–50. Springer, Heidelberg (1998). CrossRefGoogle Scholar
  48. 48.
    Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2007). zbMATHGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science, School of Science and TechnologyHellenic Open UniversityPatrasGreece
  2. 2.Cultural Technology and Communication Department, Social Science SchoolUniversity of the AegeanMytileneGreece

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