Abstract
In this paper, a C5.0 decision tree and neural network models are proposed to classify recessions in the US with 12 common financial indices and new financial stress indices inferred from the neural network models are created. A detailed experiment is presented and demonstrates that the neural network models with proper regularization and dropout achieve 98% accuracy in the training set, 97% accuracy in validation set and 100% accuracy in test accuracy. The financial stress indices outperform other existing financial stress indices in many scenes and can accurately locate crisis events even the most recent 2018 US Bear Market. With these models and new indices, contraction can be detected before NBER’s announcement and action could be taken as early as the situation get worse.
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Shen, L., Du, T., Ji, S. (2019). Neural Network Model for Classifying the Economic Recession and Construction of Financial Stress Index. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_44
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DOI: https://doi.org/10.1007/978-981-15-0121-0_44
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