IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Neuro-Adaptive Model for Financial Forecasting

Listed author(s):
  • Nastac, Iulian


    (Senior Assistant Professor (Lecturer), Polytechnic University of Bucharest)

  • Dobrescu, Emilian


    (Professor, Senior Researcher, National Institute of Economic Research, Romanian Academy)

  • Pelinescu, Elena


    (Senior Researcher, Institute of Economic Forecasting, Romanian Academy)

The paper advances an original artificial intelligence-based mechanism for specific economic predictions. The aim is to forecast the exchange rate of euro versus the Romanian currency using a large set of financial data. The possible influence of specific forecasting indicators (such as Sibiu Futures Stock Exchange market) on the evolution of the exchange rate in Romania is also analyzed. The time series under discussion are inherently non-stationary. This aspect implies that the distribution of the time series changes over time. The recent data points could provide more important information than the far distant data points. Therefore, we propose a new adaptive retraining mechanism to take this characteristic into account. The algorithm establishes how a viable structure of an artificial neural network (ANN) at a previous moment of time could be retrained in an efficient manner, in order to support modifications in a complex input-output function of a financial forecasting system. In this system, all the inputs and outputs vary dynamically, and different time delays might occur. A “remembering process” for the former knowledge achieved in the previous learning phase is used to enhance the accuracy of the predictions. The results show that the first training (which includes the searching phase for the optimal architecture) always takes a relatively long time, but then the system can be very easily retrained, since there are no changes in the structure. The advantage of the retraining procedure is that some relevant aspects are preserved (“remembered”) not only from the immediate previous training phase, but also from the previous but one phase, and so on. A kind of “slow forgetting process” also occurs; thus for the ANN it is much easier to remember specific aspects of the previous training instead of the first training. The experiments reveal the high importance of the retraining phase as an upgrading/updating process and the effect of ignoring it, as well. There has been a decrease in the test error when successive retraining phases were performed and the neural system accumulated experience.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Article provided by Institute for Economic Forecasting in its journal Romanian Journal of Economic Forecasting.

Volume (Year): 4 (2007)
Issue (Month): 3 (September)
Pages: 19-41

in new window

Handle: RePEc:rjr:romjef:v:4:y:2007:i:3:p:19-41
Contact details of provider: Postal:
Casa Academiei, Calea 13, Septembrie nr.13, sector 5, Bucureşti 761172

Phone: 004 021 3188148
Fax: 004 021 3188148
Web page:

More information through EDIRC

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:rjr:romjef:v:4:y:2007:i:3:p:19-41. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Corina Saman)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.