Abstract
Generally, a huge earthquake may be a big shock to the stock market, but it is difficult to measure it. We seek to quantify it in this paper. The normal price is predicted in the event window of the 1994 Northridge Earthquake, and then the abnormal return is obtained from the observed price. By testing the significant of abnormal returns, the shock can be quantified. Although numerous statistical and economic models have been developed to predict the movement of stock price, it is still a huge challenge. Rather than fitting the price to specific models, deep learning models of artificial neural network, namely a Long Short-Term Memory (LSTM) and a nonlinear autoregressive (NAR) neural network, are employed to predict the daily movements of US stock market in the event window. And, T-test and sign test are used to test the significance of the abnormal return in the event window. From the result of both two models, the impact of the Northridge Earthquake on the whole market is not significant at 95% confidence level; 20 stocks react positively, among which 5 stocks are from housing-related industry; 23 stocks react negatively, among which 8 stocks are from service industry.
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Acknowledgement
This work was funded by Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2013B14) and National Nature Science Foundation of China (51678540, 51478443 and 51178435).
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Tao, Z., Han, L., Bai, K. (2020). The Economic Impact Analysis of the 1994 Northridge Earthquake by Deep Learning Tools. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_13
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DOI: https://doi.org/10.1007/978-3-030-31967-0_13
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