循环神经网络
逻辑回归
金融危机
人工神经网络
计算机科学
短时记忆
人工智能
时间序列
计量经济学
机器学习
财务
经济
宏观经济学
标识
DOI:10.1016/j.jfs.2020.100746
摘要
We consider predicting systemic financial crises one to five years ahead using recurrent neural networks. We evaluate the prediction performance with the Jórda-Schularick-Taylor dataset, which includes the crisis dates and annual macroeconomic series of 17 countries over the period 1870−2016. Previous literature has found that simple neural net architectures are useful and outperform the traditional logistic regression model in predicting systemic financial crises. We show that such predictions can be significantly improved by making use of the Long-Short Term Memory (RNN-LSTM) and the Gated Recurrent Unit (RNN-GRU) neural nets. Behind the success is the recurrent networks’ ability to make more robust predictions from the time series data. The results remain robust after extensive sensitivity analysis.
科研通智能强力驱动
Strongly Powered by AbleSci AI