IDEAS home Printed from https://ideas.repec.org/a/ddj/fseeai/y2012i2p61-66.html
   My bibliography  Save this article

Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis

Author

Listed:
  • Mioara CHIRITA

    (Faculty of Economics and Business Administration, Dunarea de Jos University of Galati, Romania)

Abstract

The objective of the present study is to explore the issue of the forecasting of economic crisis using the neural network. The subject is of great importance in the economy, more so as that most countries affected by crisis, declared at the end of 2010, the economic growth but the crisis paralyzed the financial world over the past three years. Neural network techniques have been frequently applied in order to predict problems like economic forecasting. The results show that creating a model using the neural networks might be a powerful tool and could be applied to prevent economic crises

Suggested Citation

  • Mioara CHIRITA, 2012. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-66.
    • Handle: RePEc:ddj:fseeai:y:2012:i:2:p:61-66
    as

    Download full text from publisher

    File URL: http://www.ann.ugal.ro/eco/Doc2012.2/Mioara_Chirita.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    2. Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
    3. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Seyma Caliskan Cavdar & Alev Dilek Aydin, 2015. "An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures," JRFM, MDPI, vol. 8(3), pages 1-18, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
    2. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    3. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
    4. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
    6. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    7. Jorge Perez-Rodriguez & Salvador Torra & Julian Andrada-Felix, 2005. "Are Spanish Ibex35 stock future index returns forecasted with non-linear models?," Applied Financial Economics, Taylor & Francis Journals, vol. 15(14), pages 963-975.
    8. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    9. Wu, Yih-Jiuan, 1998. "Exchange rate forecasting: an application of radial basis function neural networks," ISU General Staff Papers 1998010108000013540, Iowa State University, Department of Economics.
    10. Peter Sarlin & Dorina Marghescu, 2011. "Neuro‐Genetic Predictions Of Currency Crises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(4), pages 145-160, October.
    11. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    12. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean-Marie, 2005. "A global forecasting support system adapted to textile distribution," International Journal of Production Economics, Elsevier, vol. 96(1), pages 81-95, April.
    13. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers 14/2019, Bank of Finland.
    14. Prybutok, Victor R. & Yi, Junsub & Mitchell, David, 2000. "Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations," European Journal of Operational Research, Elsevier, vol. 122(1), pages 31-40, April.
    15. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    16. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    17. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    18. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    19. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    20. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.

    More about this item

    Keywords

    Economic and financial crisis; Forecasting models; Neural networks;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G01 - Financial Economics - - General - - - Financial Crises
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ddj:fseeai:y:2012:i:2:p:61-66. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gianina Mihai (email available below). General contact details of provider: https://edirc.repec.org/data/fegalro.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.