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Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks

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Author Info

  • Matkovskyy, Roman

Abstract

This paper investigates neural network tools, especially the nonlinear autoregressive model with exogenous input (NARX), to forecast the future conditions of the Index of Financial Safety (IFS) of South Africa. Based on the time series that was used to construct the IFS for South Africa (Matkovskyy, 2012), the NARX model was built to forecast the future values of this index and the results are benchmarked against that of Bayesian Vector-Autoregressive Models. The results show that the NARX model applied to IFS of South Africa and trained by the Levenberg-Marquardt algorithm may ensure a forecast of adequate quality with less computation expanses, compared to BVAR models with different priors.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 42153.

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Date of creation: Aug 2012
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Handle: RePEc:pra:mprapa:42153

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Related research

Keywords: Index of Financial Safety (IFS); neural networks; nonlinear dynamic network (NDN); nonlinear autoregressive model with exogenous input (NARX); forecast;

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References

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  1. Abdul Abiad, 2003. "Early Warning Systems," IMF Working Papers 03/32, International Monetary Fund.
  2. Matkovskyy, Roman, 2012. "The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model," MPRA Paper 42173, University Library of Munich, Germany.
  3. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer, vol. 23(3), pages 187-200, September.
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Cited by:
  1. Abounoori, Abbas Ali & Mohammadali, Hanieh & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Comparative study of static and dynamic neural network models for nonlinear time series forecasting," MPRA Paper 46466, University Library of Munich, Germany.
  2. Majid Delavari & Nadiya Gandali Alikhani & Esmaeil Naderi, 2013. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," International Journal of Economics and Financial Issues, Econjournals, vol. 3(2), pages 466-475.
  3. Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45860, University Library of Munich, Germany.

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