• Piotr Kossakowski
  • Piotr Bilski



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


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.


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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.