Hybrid system prediction for the stock market: The case of transitional markets
| Autor: | Nebojša M. Ralević, Goran B. Andjelic, Natasa Glisovic, Vladimir Djakovic |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Neural Networks
lcsh:HB71-74 Financial economics 05 social sciences Stock market bubble lcsh:Economics as a science Hybrid System 010501 environmental sciences 01 natural sciences Market Market depth Hybrid system 0502 economics and business Economics 050211 marketing Stock market Stock Investments Prediction 0105 earth and related environmental sciences |
| Zdroj: | Industrija (2017) 45(1):45-60 Industrija, Vol 45, Iss 1, Pp 45-60 (2017) |
| ISSN: | 0350-0373 |
| Popis: | The subject of this paper is the creation and testing of an enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes, including the comparison with the traditional neural network backpropagation model. The objective of the research is to gather information concerning the possibilities of using the enhanced fuzzy neural network backpropagation model for the prediction of stock market indexes focusing on transitional markets. The methodology used involves the integration of fuzzified weights into the neural network. The research results will be beneficial both for the broader investment community and the academia, in terms of the application of the enhanced model in the investment decision-making, as well as in improving the knowledge in this subject matter. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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