Journal
NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE
Volume 56, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.najef.2021.101383
Keywords
Long-short term memory (LSTM) neural network; Attention mechanism; Network public opinion index; Systemic risk; Early warning
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Funding
- National Social Science Foundation of China [17ATJ005]
- Hunan Key Laboratory of Macroeconomic Big Data Mining and Application
- Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery Grant [RGPIN-2014-03574]
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The study found that network public opinion has a non-linear Granger causality with systemic risk, the Attention-LSTM neural network model has strong generalization ability, and it has a higher accuracy rate compared to other models.
We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.
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