Evidence of improvement in neural-network based predictability of stock market indexes through co-movement entries

  • Authors:

  • Vasile Georgescu;Elena Claudia Dinucă

  • Affiliations:

  • Department of Mathematical Economics, University of Craiova, Craiova, Romania;Department of Mathematical Economics, University of Craiova, Craiova, Romania

  • Venue:
  • AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
  • Year:
  • 2011

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Abstract

Computational Intelligence is particularly suitable for modeling and forecasting complex nonlinear and time-varying financial processes, where many difficult problems are attempted to achieve tractability and robustness. The approach in this paper serves to find some pieces of evidence that emerging markets are deeply affected from global influences such as external shocks or signals and at least with neural network models the inclusion of exogenous variables from well established global markets significantly improves the forecasting performance of the emerging market model. NNARX-type neural architectures are used to capture such co-movements and the results are contrasted with those obtained using only NNAR-type architectures.