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

Listed author(s):
  • Magdalena Daniela NEMES


  • Alexandru BUTOI


Registered author(s):

    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.

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    Article provided by Academy of Economic Studies - Bucharest, Romania in its journal Informatica Economica.

    Volume (Year): 17 (2013)
    Issue (Month): 3 ()
    Pages: 125-136

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    Handle: RePEc:aes:infoec:v:17:y:2013:i:3:p:125-136
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