An early warning system for financial crises: a temporal convolutional network approach
Abstract
The widespread and substantial effect of the global financial crisis in history underlines the importance of forecasting financial crisis effectively. In this paper, we propose temporal convolutional network (TCN), which based on a convolutional neural network, to construct an early warning system for financial crises. The proposed TCN is compared with logit model and other deep learning models. The Shapley value decomposition is calculated for the interpretability of the early warning system. Experimental results show that the proposed TCN outperforms other models, and the stock price and the real GDP growth have the largest contributions in the crises prediction.
First published online 15 March 2024
Keyword : financial crisis, deep learning, TCN, the Shapley value
This work is licensed under a Creative Commons Attribution 4.0 International License.
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