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
How to Cite
Chen, S., Huang, Y., & Ge, L. (2024). An early warning system for financial crises: a temporal convolutional network approach . Technological and Economic Development of Economy, 30(3), 688–711. https://doi.org/10.3846/tede.2024.20555
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Beutel, J., List, S., & von Schweinitz, G. (2019). Does machine learning help us predict banking crises? Journal of Financial Stability, 45, Article 100693. https://doi.org/10.1016/j.jfs.2019.100693
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Büyükkarabacak, B., & Valev, N. T. (2010). The role of household and business credit in banking crises. Journal of Banking & Finance, 34(6), 1247–1256. https://doi.org/10.1016/j.jbankfin.2009.11.022
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Davis, E. P., Karim, D., & Liadze, I. (2011). Should multivariate early warning systems for banking crises pool across regions? Review of World Economics, 147(4), 693–716. https://doi.org/10.1007/s10290-011-0102-1
Demirgüç-Kunt, A., & Detragiache, E. (1998). The determinants of banking crises in developing and developed countries. Staff Papers, 45(1), 81–109.
Domaç, I., & Martinez Peria, M. S. (2003). Banking crises and exchange rate regimes: Is there a link? Journal of International Economics, 61(1), 41–72. https://doi.org/10.1016/S0022-1996(02)00081-8
Duca, M. L., & Peltonen, T. A. (2013). Assessing systemic risks and predicting systemic events. Journal of Banking & Finance, 37(7), 2183–2195. https://doi.org/10.1016/j.jbankfin.2012.06.010
Duttagupta, R., & Cashin, P. (2011). Anatomy of banking crises in developing and emerging market countries. Journal of International Money and Finance, 30(2), 354–376. https://doi.org/10.1016/j.jimonfin.2010.08.006
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1016/0364-0213(90)90002-E
Filippopoulou, C., Galariotis, E., & Spyrou, S. (2020). An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach. Journal of Economic Behavior & Organization, 172, 344–363. https://doi.org/10.1016/j.jebo.2019.12.023
Firat, O., Aksan, E., Oztekin, I., & Yarman Vural, F. T. (2015). Learning deep temporal representations for fMRI brain decoding (arXiv:1412.7522; Version 2). https://doi.org/10.48550/arXiv.1412.7522
Fricke, D. (2017). Financial crisis prediction: A model comparison. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3059052
Giese, J., Andersen, H., Bush, O., Castro, C., Farag, M., & Kapadia, S. (2014). The credit-to-GDP gap and complementary indicators for macroprudential policy: Evidence from the UK. International Journal of Finance & Economics, 19(1), 25–47. https://doi.org/10.1002/ijfe.1489
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Beutel, J., List, S., & von Schweinitz, G. (2019). Does machine learning help us predict banking crises? Journal of Financial Stability, 45, Article 100693. https://doi.org/10.1016/j.jfs.2019.100693
Binner, J., Elger, T., Nilsson, B., & Tepper, J. (2004). Tools for non-linear time series forecasting in economics – an empirical comparison of regime switching vector autoregressive models and recurrent neural networks. In Binner, J. M., Kendall, G., & Chen, S.-H. (Eds.), Advances in Econometrics: vol. 19. Applications of Artificial Intelligence in Finance and Economics (pp. 71–91). Emerald Group Publishing Limited. https://doi.org/10.1016/S0731-9053(04)19003-8
Bluwstein, K., Buckmann, M., Joseph, A., Kapadia, S., & Şimşek, Ö. (2023). Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach. Journal of International Economics, 145, Article 103773. https://doi.org/10.1016/j.jinteco.2023.103773
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Boyacioglu, M. A., Kara, Y., & Baykan, Ö. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2, Part 2), 3355–3366. https://doi.org/10.1016/j.eswa.2008.01.003
Bussiere, M., & Fratzscher, M. (2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25(6), 953–973. https://doi.org/10.1016/j.jimonfin.2006.07.007
Büyükkarabacak, B., & Valev, N. T. (2010). The role of household and business credit in banking crises. Journal of Banking & Finance, 34(6), 1247–1256. https://doi.org/10.1016/j.jbankfin.2009.11.022
Caggiano, G., Calice, P., & Leonida, L. (2014). Early warning systems and systemic banking crises in low income countries: A multinomial logit approach. Journal of Banking & Finance, 47, 258–269. https://doi.org/10.1016/j.jbankfin.2014.07.002
Caggiano, G., Calice, P., Leonida, L., & Kapetanios, G. (2016). Comparing logit-based early warning systems: Does the duration of systemic banking crises matter? Journal of Empirical Finance, 37, 104–116. https://doi.org/10.1016/j.jempfin.2016.01.005
Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research, 166(2), 528–546. https://doi.org/10.1016/j.ejor.2004.03.023
Casabianca, E. J., Catalano, M., Forni, L., Giarda, E., & Passeri, S. (2019). An early warning system for banking crises: From regression-based analysis to machine learning techniques (“Marco Fanno” Working Papers 0235). Dipartimento di Scienze Economiche “Marco Fanno”. https://ideas.repec.org//p/pad/wpaper/0235.html
Cerutti, E., Claessens, S., & Laeven, L. (2017). The use and effectiveness of macroprudential policies: New evidence. Journal of Financial Stability, 28, 203–224. https://doi.org/10.1016/j.jfs.2015.10.004
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014, October). Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1724–1734). Doha, Qatar. Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1179
Colah’s blog. (2015). Understanding LSTM Networks. https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Davis, E. P., & Karim, D. (2008a). Comparing early warning systems for banking crises. Journal of Financial Stability, 4(2), 89–120. https://doi.org/10.1016/j.jfs.2007.12.004
Davis, E. P., & Karim, D. (2008b). Could early warning systems have helped to predict the sub-prime crisis? National Institute Economic Review, 206(1), 35–47. https://doi.org/10.1177/0027950108099841
Davis, E. P., Karim, D., & Liadze, I. (2011). Should multivariate early warning systems for banking crises pool across regions? Review of World Economics, 147(4), 693–716. https://doi.org/10.1007/s10290-011-0102-1
Demirgüç-Kunt, A., & Detragiache, E. (1998). The determinants of banking crises in developing and developed countries. Staff Papers, 45(1), 81–109.
Domaç, I., & Martinez Peria, M. S. (2003). Banking crises and exchange rate regimes: Is there a link? Journal of International Economics, 61(1), 41–72. https://doi.org/10.1016/S0022-1996(02)00081-8
Duca, M. L., & Peltonen, T. A. (2013). Assessing systemic risks and predicting systemic events. Journal of Banking & Finance, 37(7), 2183–2195. https://doi.org/10.1016/j.jbankfin.2012.06.010
Duttagupta, R., & Cashin, P. (2011). Anatomy of banking crises in developing and emerging market countries. Journal of International Money and Finance, 30(2), 354–376. https://doi.org/10.1016/j.jimonfin.2010.08.006
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1016/0364-0213(90)90002-E
Filippopoulou, C., Galariotis, E., & Spyrou, S. (2020). An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach. Journal of Economic Behavior & Organization, 172, 344–363. https://doi.org/10.1016/j.jebo.2019.12.023
Firat, O., Aksan, E., Oztekin, I., & Yarman Vural, F. T. (2015). Learning deep temporal representations for fMRI brain decoding (arXiv:1412.7522; Version 2). https://doi.org/10.48550/arXiv.1412.7522
Fricke, D. (2017). Financial crisis prediction: A model comparison. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3059052
Giese, J., Andersen, H., Bush, O., Castro, C., Farag, M., & Kapadia, S. (2014). The credit-to-GDP gap and complementary indicators for macroprudential policy: Evidence from the UK. International Journal of Finance & Economics, 19(1), 25–47. https://doi.org/10.1002/ijfe.1489
Greenwood, R., Hanson, S. G., Shleifer, A., & Sørensen, J. A. (2022). Predictable financial crises. The Journal of Finance, 77(2), 863–921. https://doi.org/10.1111/jofi.13105
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hoggarth, G., Reis, R., & Saporta, V. (2002). Costs of banking system instability: Some empirical evidence. Journal of Banking & Finance, 26(5), 825–855. https://doi.org/10.1016/S0378-4266(01)00268-0
Holopainen, M., & Sarlin, P. (2017). Toward robust early-warning models: A horse race, ensembles and model uncertainty. Quantitative Finance, 17(12), 1933–1963. https://doi.org/10.1080/14697688.2017.1357972
Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4), 543–558. https://doi.org/10.1016/S0167-9236(03)00086-1
Ionescu, R.-V., Zlati, M. L., Antohi, V.-M., Cornea, V., & Socoliuc, M.-I. (2023). The implementation of the regional development index in the new geo-political context. Technological and Economic Development of Economy, 29(5), 1405–1431. https://doi.org/10.3846/tede.2023.19261
Jordà, Ò., Schularick, M., & Taylor, A. M. (2016). Macrofinancial history and the new business cycle facts (Working Paper 22743). National Bureau of Economic Research. https://doi.org/10.3386/w22743
Joy, M., Rusnák, M., Šmídková, K., & Vašíček, B. (2017). Banking and currency crises: Differential diagnostics for developed countries. International Journal of Finance & Economics, 22(1), 44–67. https://doi.org/10.1002/ijfe.1570
Kaminsky, G. L., & Reinhart, C. M. (1999). The twin crises: The causes of banking and balance-of-payments problems. American Economic Review, 89(3), 473–500. https://doi.org/10.1257/aer.89.3.473
Krishnamurthy, A., & Muir, T. (2017). How credit cycles across a financial crisis (Working Paper 23850). National Bureau of Economic Research. https://doi.org/10.3386/w23850
Kuan, C.-M., & White, H. (1994). Artificial neural networks: An econometric perspective. Econometric Reviews, 13(1), 1–91. https://doi.org/10.1080/07474939408800273
Liu, L., Chen, C., & Wang, B. (2022). Predicting financial crises with machine learning methods. Journal of Forecasting, 41(5), 871–910. https://doi.org/10.1002/for.2840
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://dl.acm.org/doi/10.5555/3295222.3295230
Manasse, P., Savona, R., & Vezzoli, M. (2013). Rules of thumb for banking crises in emerging markets (Quaderni DSE Working Paper No. 872). SSRN. https://doi.org/10.2139/ssrn.2236733
Ollivaud, P., & Turner, D. (2014). The effect of the global financial crisis on OECD potential output (OECD Economics Department Working Papers, No. 1166). OECD Publishing. https://doi.org/10.1787/5jxwtl8h75bw-en
Olmeda, I., & Fernández, E. (1997). Hybrid classifiers for financial multicriteria decision making: The case of bankruptcy prediction. Computational Economics, 10(4), 317–335. https://doi.org/10.1023/A:1008668718837
Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta Numerica, 8, 143–195. https://doi.org/10.1017/S0962492900002919
Qi, Y., Qin, H., Liu, P., Liu, J., Raslanas, S., & Banaitienė, N. (2022). Macroprudential policy, house price fluctuation and household consumption. Technological and Economic Development of Economy, 28(3), 804–830. https://doi.org/10.3846/tede.2022.16787
Ravi, V., & Pramodh, C. (2008). Threshold accepting trained principal component neural network and feature subset selection: Application to bankruptcy prediction in banks. Applied Soft Computing, 8(4), 1539–1548. https://doi.org/10.1016/j.asoc.2007.12.003
Ristolainen, K. (2018). Predicting banking crises with artificial neural networks: The role of nonlinearity and heterogeneity. The Scandinavian Journal of Economics, 120(1), 31–62. https://doi.org/10.1111/sjoe.12216
Schularick, M., & Taylor, A. M. (2009). Credit booms gone bust: Monetary policy, leverage cycles and financial crises, 1870–2008 (Working Paper 15512). National Bureau of Economic Research. https://doi.org/10.3386/w15512
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