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On biologically inspired predictions of the global financial crisis

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Abstract

Early-warning models provide means for ex ante identification of elevated risks that may lead to a financial crisis. This paper taps into the early-warning literature by introducing biologically inspired models for predicting systemic financial crises. We create three models: a conventional statistical model, a back-propagation neural network (NN) and a neuro-genetic (NG) model that uses a genetic algorithm for choosing the optimal NN configuration. The models are calibrated and evaluated in terms of usefulness for policymakers that incorporates preferences between type I and type II errors. Generally, model evaluations show that biologically inspired models outperform the statistical model. NG models are, however, shown not only to provide largest usefulness for policymakers as an early-warning model, but also in form of decreased expertise and labor needed for, and uncertainty caused by, manual calibration of an NN. For better generalization of data-driven models, we also advocate adopting to the early-warning literature a training scheme that includes validation data.

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Notes

  1. The FSI consists of five components: the spread of the 3-month interbank rate over the rate of the 3-month government bill; quarterly equity returns; realized volatility of a main equity index; realized volatility of the exchange rate; and realized volatility of the yield on the 3-month government bill.

  2. Model robustness has been tested with respect to different forecast horizons. In general, for longer horizons (e.g. 24 months) there is a slight increase in usefulness and for shorter (e.g. 6 and 12 months) a slight decrease. However, the relative performance is similar to the benchmark results with a horizon of 18 months.

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  1. Department of Information Technologies, Turku Centre for Computer Science, Åbo Akademi University, Joukahaisenkatu 3-5, 20520, Turku, Finland

    Peter Sarlin

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Correspondence to Peter Sarlin.

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Sarlin, P. On biologically inspired predictions of the global financial crisis. Neural Comput & Applic 24, 663–673 (2014). https://doi.org/10.1007/s00521-012-1281-y

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