Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis
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 the most countries affected by crisis, declared on 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 the economic crises.
Volume (Year): (2011)
Issue (Month): ()
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- Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998.
"Leading Indicators of Currency Crises,"
IMF Staff Papers,
Palgrave Macmillan, vol. 45(1), pages 1-48, March.
- Kaminsky, Graciela & Lizondo, Saul & Reinhart, Carmen M., 1997. "Leading indicators of currency crises," Policy Research Working Paper Series 1852, The World Bank.
- Reinhart, Carmen & Kaminsky, Graciela & Lizondo, Saul, 1998. "Leading Indicators of Currency Crises," MPRA Paper 6981, University Library of Munich, Germany.
- 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.
- 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.
- Marco Fioramanti, 2006. "Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach," ISAE Working Papers 72, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
- Axel Schimmelpfennig & Nouriel Roubini & Paolo Manasse, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 03/221, .
- Allen, Franklin & Gale, Douglas, 1999. "Bubbles, Crises, and Policy," Oxford Review of Economic Policy, Oxford University Press, vol. 15(3), pages 9-18, Autumn.
- Abdul d Abiad, 2003. "Early Warning Systems; A Survey and a Regime-Switching Approach," IMF Working Papers 03/32, . Full references (including those not matched with items on IDEAS)
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