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On Biologically Inspired Predictions of the Global Financial Crisis

Peter Sarlin, On Biologically Inspired Predictions of the Global Financial Crisis. In: Klaus G. Troitzsch, Michael Möhring, Ulf Lotzmann (Eds.), Proceedings of the 26th European Conference on Modelling and Simulation, 253–259, European Council for Modelling and Simulation, 2012.

Abstract:

This paper evaluates the performance of biologically inspired early warning systems (EWS) for systemic financial crises. We create three EWSs: a logit model, a standard back-propagation neural network (NN) and a neuro-genetic (NG) model that uses a genetic algorithm for choosing the optimal NN configuration. The performance of the NN-based models are compared with the benchmark logit in terms of utility for policymakers. For creating the NN-based EWSs, we use two training schemes for parsimonious and generalized models and advocate adopting to the EWS literature the scheme using validation sets for better generalization of data-driven models. The performance evaluation shows that NN-based models, in general, outperform the logit model. The key finding is, however, that NG models not only provide largest utility for policymakers as an EWS, but also in form of decreased expertise and labor needed for, and uncertainty caused by, manual calibration of a NN.

BibTeX entry:

@INPROCEEDINGS{inpSarlin_Peter12a,
  title = {On Biologically Inspired Predictions of the Global Financial Crisis},
  booktitle = {Proceedings of the 26th European Conference on Modelling and Simulation},
  author = {Sarlin, Peter},
  editor = {Troitzsch, Klaus G. and Möhring, Michael and Lotzmann, Ulf},
  publisher = {European Council for Modelling and Simulation},
  pages = {253–259},
  year = {2012},
}

Belongs to TUCS Research Unit(s): Data Mining and Knowledge Management Laboratory

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