engleski

Neural Network Applications in Stock Market Predictions

Neural networks (NNs), as artificial intelligence (AI) methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include AI, although there is no optimal methodology for a certain problem. In order to identify the main benefits and limitations of previous methods in NN applications and to find connections between methodology and problem domains, data models, and results obtained, a comparative analysis of selected applications is conducted. It can be concluded from analysis that NNs are most implemented in forecasting stock prices, returns, and stock modeling, and the most frequent methodology is the Backpropagation algorithm. However, the importance of NN integration with other artificial intelligence methods is emphasized by numerous authors. Inspite of many benefits, there are limitations that should be investigated, such as the relevance of the results, and the "best" topology for the certain problems.

neural network applications; stock market; qualitative comparative analysis; NN methodology; benefits; limitations

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