Fuzzy prediction based on regression models: Evidence from the Belgrade Stock Exchange - prime market and graphic industry

Ralević, Nebojša; Djakovic, Vladimir; Andjelic, Goran B.; Kovačević, Ilija; Kiurski, Jelena S.; Čomić, Lidija

doi:10.1109/sisy.2011.6034312

2011 IEEE 9th International Symposium on Intelligent Systems and Informatics 2011

DOI: 10.1109/sisy.2011.6034312

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Fuzzy prediction based on regression models: Evidence from the Belgrade Stock Exchange - prime market and graphic industry

Nebojša Ralević

Vladimir Djakovic

Goran B. Andjelic

et al.

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2012

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Cited by 2 publications

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“…Using fuzzy techniques tends to reduce the magnitude of errors and as a result yields better forecasts and fitting for decision making in the prediction of stock market prices [5].…”

mentioning

confidence: 99%

Fuzzy uncertainty analysis in the investment processes

Ralević

Djakovic

Kiurski

et al. 2012

2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics

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The analyses of the investment processes, especially the possibility of monitoring and forecasting the intense return fluctuations, demands usage of an appropriate framework, which adequately evaluates the dynamic nature of those processes. In the recessive business conditions, volatility and extreme stock market movements presents a challenge while evaluation of the investment processes. Thus, the stock market prices volatility and the possibility of stock market forecasting in an uncertain environment are in the focus of the research. Fuzzy sets allow usage of approximated values of the ambiguous data, that is, the intraday stock market prices data. The sample comprises stocks of Alfa plam a.d. Vranje (ALFA), Komercijalna banka a.d. Beograd (KMBN) and Metalac a.d. Gornji Milanovac (MTLC), that are traded at the regulated market of the Belgrade Stock Exchange. The main goal of the research is to present an overview and performance test of the fuzzy sets as a part of soft computing (SC) technology in the investment processes. The research results indicate the necessity of the fuzzy sets application in the forecasting of stock market prices, i.e. its approximate values.

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“…Using fuzzy techniques tends to reduce the magnitude of errors and as a result yields better forecasts and fitting for decision making in the prediction of stock market prices [5].…”

mentioning

confidence: 99%

Fuzzy uncertainty analysis in the investment processes

Ralević

Djakovic

Kiurski

et al. 2012

2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics

Self Cite

View full text Add to dashboard Cite

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Robust stock trading using fuzzy decision trees

Ochotorena

Yap

Dadios

et al. 2012

2012 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Fuzzy prediction based on regression models: Evidence from the Belgrade Stock Exchange - prime market and graphic industry

Cited by 2 publications

References 14 publications

Fuzzy uncertainty analysis in the investment processes

Fuzzy uncertainty analysis in the investment processes

Robust stock trading using fuzzy decision trees

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