Advances in Economics, Management and Political Sciences

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Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 63 , 28 December 2023


Open Access | Article

The Impact of Natural Disaster on US Stock Market Index: Using DID, ARMAX-GARCH, and Random Forest

Xuehan Zhou * 1
1 University of Illinois at Urbana-Champaign, Illinois, USA

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 63, 101-116
Published 28 December 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xuehan Zhou. The Impact of Natural Disaster on US Stock Market Index: Using DID, ARMAX-GARCH, and Random Forest. AEMPS (2023) Vol. 63: 101-116. DOI: 10.54254/2754-1169/63/20231387.

Abstract

The stock market might be impacted by natural disasters, which can cause significant market disruptions and financial uncertainty, as already shown in several existing studies. This study investigates the role of natural disasters in stock market movements in the US specifically, hypothesizing that incorporating natural disasters leads to more accurate stock market predictions. Various models, including Difference-in-Differences (DID), ARMA-GARCH/ARMAX-GARCH, and Random Forest, are employed to analyze the S&P 500 index's daily returns and the impact of natural disasters. The study reveals that the ARMA-GARCH model effectively captures market volatility but does not account for all influencing factors such as interest rate, inflation rate, and natural disasters. The DID method isolates natural disaster effects, showing significant but inconsistent impacts. The Random Forest model, incorporating a disaster severity index, yields more accurate predictions than the one without, reinforcing the hypothesis and supporting previous studies. Although the study highlights the importance of considering natural disasters in stock market predictions, it also underscores the market's complexity, indicating that more features and factors should be considered in future research.

Keywords

Difference-in-Differences, stock return, return volatility clustering, random forest

References

1. Tavor, T., & Teitler-Regev, S. (2019). The impact of disasters and terrorism on the stock market. Jamba (Potchefstroom, South Africa), 11(1), 534.

2. Pagnottoni, P., Spelta, A., Flori, A., & Pammolli, F. (2022). Climate change and financial stability: Natural disaster impacts on global stock markets. Physica A: Statistical Mechanics and Its Applications, 599, 127514. https://doi.org/10.1016/j.physa.2022.127514

3. Bourdeau-Brien, M., & Kryzanowski, L. (2017). The impact of natural disasters on the stock returns and volatilities of local firms. The Quarterly Review of Economics and Finance, 63, 259–270. https://doi.org/10.1016/j.qref.2016.05.003

4. Lanfear, M. G., Lioui, A., & Siebert, M. G. (2019). Market anomalies and disaster risk: Evidence from extreme weather events. Journal of Financial Markets, 46, 100477. https://doi.org/10.1016/j.finmar.2018.10.003

5. Panwar, V., & Sen, S. (2019). Economic Impact of Natural Disasters: An Empirical Re-examination. Margin: The Journal of Applied Economic Research, 13(1), 109–139. https://doi.org/10.1177/0973801018800087

6. Worthington *, A., & Valadkhani, A. (2004). Measuring the impact of natural disasters on Capital Markets: An empirical application using intervention analysis. Applied Economics, 36(19), 2177–2186. https://doi.org/10.1080/0003684042000282489

7. Breen, W., Glosten, L. R., & Jagannathan, R. (1989, December). Economic significance of predictable variations in stock index returns. https://www.jstor.org/stable/2328638

8. Pearce, D. K. (1984). An empirical analysis of expected stock price movements. Journal of Money, Credit and Banking, 16(3), 317. https://doi.org/10.2307/1992219

9. Caldera, H. J., & Wirasinghe, S. C. (2021). A Universal Severity Classification for Natural Disasters. https://doi.org/10.21203/rs.3.rs-333435/v1

10. Bialkowski, J. P., Gottschalk, K., & Wisniewski, T. P. (2006). Stock market volatility around national elections. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.892143

11. Difference-in-difference estimation. Columbia University Mailman School of Public Health. (2023a, March 13). https://www.publichealth.columbia.edu/research/population-health-methods/difference-difference-estimation#:~:text=some%20social%20sciences.-,Description,to%20estimate%20a%20causal%20effect.

12. Breiman, L. (2001). Random Forest. Machine Learning 45, 5-32.

13. Kho, J. (2019, March 12). Why random forest is My Favorite Machine Learning Model. Medium. https://towardsdatascience.com/why-random-forest-is-my-favorite-machine-learning-model-b97651fa3706

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-227-5
ISBN (Online)
978-1-83558-228-2
Published Date
28 December 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/63/20231387
Copyright
28 December 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated