ANALYSIS OF THE SELF-ORGANIZING MAP-BASED INVESTMENT STRATEGY
DOI:
https://doi.org/10.47839/ijc.16.1.866Keywords:
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
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
