ANALYSIS OF THE SELF-ORGANIZING MAP-BASED INVESTMENT STRATEGY

Authors

  • Piotr Kossakowski
  • Piotr Bilski

DOI:

https://doi.org/10.47839/ijc.16.1.866

Keywords:

stock market, investment strategy, Self-Organizing Maps, prediction, unsupervised learning.

Abstract

The following paper presents the application of Self-Organizing Maps (SOM) to construct and apply investment strategy on the stock market. Characteristics of this type of neural network and their influence on the investment strategy performance are verified. Considered parameters include the SOM size, here connected to the size of the training set (number of examples). The average number of patterns per neuron was selected as the appropriate measure. Other aspects of the SOM analysis included conscience mechanism, which allows more neurons to be stimulated during the learning process, method of weights updating, determining the number of stimulated neurons. Additionally, the impact of the correlation between features was verified to eliminate redundant ones. Performance of each designed network was verified against the simple investment strategy, generating “buy”, ”sell” and “hold” signals based on the average Rate of Return (RoR). Results show that SOMs with the conscience mechanism outperform their simpler configurations. Elimination of correlated data also improves performance of the SOM-based investment strategy.

References

P. Kossakowski, P. Bilski, “Application of self-organizing maps to the stock exchange data analysis,” in Proceedings of the 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2015), Warsaw, Poland, September 24-26, 2015, pp. 208-2013.

E. Gurusen, G. Kayakulu and T. U. Daim, “Using artificial neural network model in stock market index prediction,” Expert Systems with Applications, Vol. 38, Issue 8, pp. 10389-10397, 2011.

V. Turchenko, P. Beraldi, F. De Simone, L. Grandinetti, “Short-term stock price prediction using MLP in moving simulation mode,” in Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS’2011), Prague, Czech republic, 15-17 September 2011, pp. 666-671.

X. Lin, Z. Yang and Y. Song, “Short-term stock price prediction based on echo state networks,” Expert Systems with Applications, Vol. 36, pp. 7313-7317, 2009.

X. Zhu, H. Wang, L. Xu and H. Li, “Predicting stock index increments by neural networks: The role of trading volume under different horizons,” Expert Systems with Applications, Vol. 34, pp. 3043-3054, 2008.

Y. Kara, M. A. Boyacioglu and O. K. Baykan, “Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Instabul Stock Exchange,” Expert Systems with Applications, Vol. 38, pp. 5311-5319, 2011.

M. Qiu, Y. Song, “Predicting the direction of stock market index movement using an optimized artificial neural network model,” PLoS ONE, Vol. 11, Issue 5, e0155133, 2016. doi:10.1371/journal.pone.0155133.

Z. Guo, H. Wang, J. Yang, D.J. Miller, “A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network,” PLoS ONE, Vol. 10, Issue 4, e0122385, 2015. doi:10.1371/journal.pone.0122385.

P. Bilski, “Analysis of the stock exchange waveforms similarity using the clustering method,” Polish Journal of Environmental Studies, Vol. 18, No. 5B, pp. 13-20, 2009.

J. Vesanto, E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on Neural Network, Vol. 11, pp. 586-600, 2000.

E. W. Saad, D. V. Prokhoro, “Comparative study of stock trend prediction using time delay,” IEEE Transactions on Neural Networks, Vol. 9, pp. 1456-1470, 1998.

S. Subramanian, U. S. Rao, “Enhancing stock selection in Indian Stock Market using value investment criteria: an application of artificial neural networks,” IUP Journal of Accounting Research & Audit Practices, Vol. 9, pp. 54-67, 2010.

P. Bilski, “Ambiguity groups detection in analog systems diagnostics using Self-Organizing Maps,” in Proceedings of the IMEKO TC10 Workshop, Milan, Italy, June 27-28 2016, pp. 294-299.

C. Estrebou, L. Lanzarini, W. Hasperué, “Voice recognition based on probabilistic SOM,” in Proceedings of the Conference: XXXVI Conferencia Latinoamericana en Informática, At Asunción, Paraguay, 2010.

M. G. Kibria and Al-Imtiaz, "Bengali optical character recognition using self organizing map,” in Proceedings of the International Conference on Informatics, Electronics & Vision (ICIEV), 18-19 May 2012, 10.1109/ICIEV.2012.6317479.

J. J. Murphy, Technical Analysis of the Financial Markets, Prentice Hall Press, 1999.

H. Kwaśnicka, M. Ciosmak, “Itelligent techniques in stock analysis,” Advances in Intelligent and Soft Computing, Vol. 10, pp. 195-208, 2001.

R. Tadeusiewicz, Neural Networks, Akademicka Oficyna Wydawnicza, 1993. (in Polish).

A. P. Engelbrecht, Computational Inteligence: An Introduction, John Wiley & Sons, 2007.

S. Osowski, Neural Networks in Algorithmic Approach, WNT, 1996. (in Polish).

Downloads

Published

2017-03-31

How to Cite

Kossakowski, P., & Bilski, P. (2017). ANALYSIS OF THE SELF-ORGANIZING MAP-BASED INVESTMENT STRATEGY. International Journal of Computing, 16(1), 10-17. https://doi.org/10.47839/ijc.16.1.866

Issue

Section

Articles