ecms_neu_mini.png

Digital Library

of the European Council for Modelling and Simulation

 

Title:

On Biologically Inspired Predictions Of The Global Financial Crisis

Authors:

Peter Sarlin

Published in:

 

(2012).ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann. European Council for Modeling and Simulation. doi:10.7148/2012 

 

ISBN: 978-0-9564944-4-3

 

26th European Conference on Modelling and Simulation,

Shaping reality through simulation

Koblenz, Germany, May 29 – June 1 2012

 

Citation format:

Sarlin, P. (2012). On Biologically Inspired Predictions Of The Global Financial Crisis. ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann (pp. 253-259). European Council for Modeling and Simulation. doi:10.7148/2012-0253-0259

DOI:

http://dx.doi.org/10.7148/2012-0253-0259

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.

Full text: