data mining on romanian stock market using neural networks for price prediction
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ID: 213697
2013
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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.
| Reference Key |
nemes2013informaticdata
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| Authors | ;Magdalena Daniela NEMES;Alexandru BUTOI |
| Journal | fuel |
| Year | 2013 |
| DOI |
10.12948/issn14531305/17.3.2013.11
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