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Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

Author

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  • Magdalena Daniela NEMES
  • Alexandru BUTOI

Abstract

Predicting future prices by using time series forecasting models has become a relevant trading strategy for most stock market players. Intuition and speculation are no longer reliable as many new trading strategies based on artificial intelligence emerge. Data mining represents a good source of information, as it ensures data processing in a convenient manner. Neural networks are considered useful prediction models when designing forecasting strategies. In this paper we present a series of neural networks designed for stock exchange rates forecasting applied on three Romanian stocks traded on the Bucharest Stock Exchange (BSE). A multistep ahead strategy was used in order to predict short-time price fluctuations. Later, the findings of our study can be integrated with an intelligent multi-agent system model which uses data mining and data stream processing techniques for helping users in the decision making process of buying or selling stocks.

Suggested Citation

  • Magdalena Daniela NEMES & Alexandru BUTOI, 2013. "Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(3), pages 125-136.
    • Handle: RePEc:aes:infoec:v:17:y:2013:i:3:p:125-136
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    Cited by:

    1. S M Raju & Ali Mohammad Tarif, 2020. "Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis," Papers 2006.14473, arXiv.org.
    2. Süleyman Bilgin Kılıç & Semin Paksoy & Tolga Genç, 2014. "Forecasting the Direction of BIST 100 Returns with Artificial Neural Network Models," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 4(3), pages 759-759.

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