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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Public Policy Institute of California, Northridge Earthquake. https://www.ppic.org/content/pubs/jtf/JTF_NorthridgeJTF.pdf. Accessed 22 Jan 2019
USGS, USGS Response to an Urban Earthquake-Northridge ’94. https://pubs.usgs.gov/of/1996/ofr-96-0263/. Accessed 21 Jan 2019
Risk Management Solutions: The Northridge, California Earthquake, RMS 10-year Retrospective (2004)
USGS. https://earthquake.usgs.gov/earthquakes/eventpage/ci3144585/executive. Accessed 22 Jan 2019
California Earthquake Authority, Northridge earthquake remembered as one of costliest natural disasters in U.S. history. https://www.earthquakeauthority.com/Press-Room/Press-Releases/2019/Northridge-earthquake-remembered. Accessed 22 Jan 2019
Insurance Information Institute, Inc., Background on: Earthquake insurance and risk. https://www.iii.org/article/background-on-earthquake-insurance-and-risk. Accessed 18 Jan 2019
Goltz, J.: The Northridge, California Earthquake of January 17, 1994: General Reconnaissance Report, Technical Report NCEER-94-0005. National Center for Earthquake Engineering Research, Buffalo, New York (1994)
Tierney, K.: Business impacts of the Northridge Earthquake, Preliminary paper, University of Delaware, Disaster Research Center (1996)
Eguchi, R., Goltz, J., Taylor, C., Chang, S., Flores, P., Jphnson, L., Seligson, H., Blais, N.: Direct economic losses in the Northridge Earthquake: a three-year post-event perspective. Earthquake Spectra 14(2), 245–264 (1998)
Aiuppa, T., Krueger, T.: Insurance stock prices following the 1994 Los Angeles earthquake. J. Insur. 18, 23–36 (1995)
Lamb, R., Kennedy, W.: Insurer stock prices and market efficiency around the Los Angeles earthquake. J. Insur. Issues 20(Spring), 10–24 (1997)
Marlett, D., Corbett, R., Pacini, C.: Insurer stock price responses to the disclosure of revised insured loss estimates after the 1994 Northridge Earthquake. J. Insur. Issues 23(2), 103–123 (2000)
Marlon, B.: Business losses, transportation damage and the Northridge Earthquake. J. Transp. Stat. 1(2), 49–64 (1998)
Gordon, P., Richardson, H., Davis, B.: Transport-related impacts of the Northridge Earthquake. J. Transp. Stat. 1(2), 21–36 (1998)
Adam, R., Benavides, J., Chang, S., Szczesniak, P., Lim, D.: The regional economic impact of an earthquake: direct and indirect effects of electricity lifeline disruptions. J. Reg. Sci. 37(3), 437–458 (1997)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Liu, J., Zhang, T., Han, G., Gou, Y.: TD-LSTM: temporal dependence-based LSTM networks for marine temperature prediction. Sensors 18(11), 3797 (2018)
Yuan, X., Chen, C., Lei, X., Yuan, Y., Adnan, Y.: Monthly runoff forecasting based on LSTM-ALO model. Stoch. Env. Res. Risk Assess. 32, 2199–2212 (2018)
Zhang, R., Huang, C., Zhang, W., Chen, S.: Multi factor stock selection model based on LSTM. Int. J. Econ. Finance 10(8), 36–42 (2018)
Zhang, T., Song, S., Li, S., Ma, L., Pan, S., Han, L.: Research on gas concentration prediction models based on LSTM multidimensional time series. Energies 12(1), 161 (2019)
Campbell, Y., Lo, W., MacKinlay, C.: The Econometrics of Financial Markets. Princeton University Press, New Jersey (1998)
Javid, Y.: Stock market reaction to catastrophic shock-evidence from listed Pakistani firm. Working Papers, Pakistan Institute of Development Economics (2007)
Scholtens, B., Voorhorst, Y.: The impact of earthquakes on the domestic stock market. Earthquake Spectra 29(1), 325–337 (2013)
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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