Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

Data Mining Research Center Xiamen University Xiamen 361005 China

Department of Finance Performance and Marketing Teesside University International Business School Teesside University Middlesbrough TS1 3BX Tees Valley UK

Institute of Metal Resources Strategy Central South University Changsha 410083 China

National Institute for Data Science in Health and Medicine Xiamen University Xiamen 361005 China

School of Business Administration Hunan University Changsha 410082 China

School of Management Swansea University Bay Campus Fabian Way SA1 8EN Swansea Wales UK

School of Mathematics and Statistics Central South University Changsha 410083 Hunan China

School of Medicine Xiamen University Xiamen 361005 China

Science and Research Centre Faculty of Economics and Administration University of Pardubice Studentska 84 532 10 Pardubice Czech Republic

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Abdin AF, Fang YP, Caunhye A, Alem D, Barros A, Zio E. An optimization model for planning testing and control strategies to limit the spread of a pandemic–The case of COVID-19. European Journal of Operational Research. 2021;304(1):308–324. doi: 10.1016/j.ejor.2021.10.062. PubMed DOI PMC

Abedin MZ, Moon MH, Hassan MK, Hajek P. Deep learning-based exchange rate prediction during the COVID-19 pandemic. Annals of Operations Research. 2023 doi: 10.1007/s10479-021-04420-6. PubMed DOI PMC

Aharon DY, Siev S. COVID-19, government interventions and emerging capital markets performance. Research in International Business and Finance. 2021;58:101492. doi: 10.1016/j.ribaf.2021.101492. PubMed DOI PMC

Ahmed B, Sharif T, Abedin MZ. COVID-19 and stock returns: Evidence from the Markov switching dependence approach. Research in International Business and Finance. 2023;64:101882. doi: 10.1016/j.ribaf.2023.101882. PubMed DOI PMC

Alvarez-Melis, D., & Jaakkola, T. S. (2018). On the robustness of interpretability methods. arXiv preprint https://arxiv.org/abs/1806.08049

Ashraf BN. Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets. Journal of Behavioral and Experimental Finance. 2020;27:100371. doi: 10.1016/j.jbef.2020.100371. PubMed DOI PMC

Awad M, Khanna R. Support vector regression. Efficient learning machines. Apress; 2015. pp. 67–80.

Baele L, Bekaert G, Inghelbrecht K, Wei M. Flights to safety. The Review of Financial Studies. 2020;33(2):689–746. doi: 10.1093/rfs/hhz055. DOI

Bastos JA, Matos SM. Explainable models of credit losses. European Journal of Operational Research. 2022;301(1):386–394. doi: 10.1016/j.ejor.2021.11.009. DOI

Biau G. Analysis of a random forests model. The Journal of Machine Learning Research. 2012;13(1):1063–1095.

Birge JR, Candogan O, Feng Y. Controlling epidemic spread: Reducing economic losses with targeted closures. Management Science. 2022;68(5):3175–3973. doi: 10.1287/mnsc.2022.4318. PubMed DOI

Biswas D, Alfandari L. Designing an optimal sequence of non-pharmaceutical interventions for controlling COVID-19. European Journal of Operational Research. 2022;303(3):1372–1391. doi: 10.1016/j.ejor.2022.03.052. PubMed DOI PMC

Bouri E, Chen Q, Lien D, Lv X. Causality between oil prices and the stock market in China: The relevance of the reformed oil product pricing mechanism. International Review of Economics and Finance. 2017;48:34–48. doi: 10.1016/j.iref.2016.11.004. DOI

Brodeur A, Gray D, Islam A, Bhuiyan S. A literature review of the economics of COVID-19. Journal of Economic Surveys. 2021;35(4):1007–1044. doi: 10.1111/joes.12423. PubMed DOI PMC

Bustos O, Pomares-Quimbaya A. Stock market movement forecast: A systematic review. Expert Systems with Applications. 2020;156:113464. doi: 10.1016/j.eswa.2020.113464. DOI

Carranza A, Goic M, Lara E, Olivares M, Weintraub GY, Covarrubia J, Basso LJ. The social divide of social distancing: Shelter-in-place behavior in Santiago during the COVID-19 pandemic. Management Science. 2022;68(3):2016–2027. doi: 10.1287/mnsc.2021.4240. DOI

Chai S, Chu W, Zhang Z, Abedin MZ. Dynamic nonlinear connectedness between the green economy, clean energy, and stock price: The impact of the COVID-19 pandemic. Annals of Operations Research. 2022 doi: 10.1007/s10479-021-04452-y. PubMed DOI PMC

Chen K, Pun CS, Wong HY. Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations. European Journal of Operational Research. 2021;304(1):84–98. doi: 10.1016/j.ejor.2021.11.012. PubMed DOI PMC

Chen L, Liu H, Zhou Z, Chen M, Chen Y. IT-business alignment, big data analytics capability, and strategic decision-making: Moderating roles of event criticality and disruption of COVID-19. Decision Support Systems. 2022;161:113745. doi: 10.1016/j.dss.2022.113745. PubMed DOI PMC

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SigKDD international conference on knowledge discovery and data mining (pp. 785–794).

Csiszár O, Csiszár G, Dombi J. Interpretable neural networks based on continuous-valued logic and multicriteria decision operators. Knowledge-Based Systems. 2020;199:105972. doi: 10.1016/j.knosys.2020.105972. DOI

Dash RK, Nguyen TN, Cengiz K, Sharma A. Fine-tuned support vector regression model for stock predictions. Neural Computing and Applications. 2023 doi: 10.1007/s00521-021-05842-w. DOI

Deng S, Huang X, Zhu Y, Su Z, Fu Z, Shimada T. Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments. The North American Journal of Economics and Finance. 2023;64:101848. doi: 10.1016/j.najef.2022.101848. DOI

Dhar S, Bose I. Victim crisis communication strategy on digital media: A study of the COVID-19 pandemic. Decision Support Systems. 2022;161:113830. doi: 10.1016/j.dss.2022.113830. PubMed DOI PMC

Ding Q, Huang J, Zhang H. The time-varying effects of financial and geopolitical uncertainties on commodity market dynamics: A TVP-SVAR-SV analysis. Resources Policy. 2021;72:102079. doi: 10.1016/j.resourpol.2021.102079. DOI

Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. arXiv preprint https://arxiv.org/abs/1810.11363

du Jardin P. Designing topological data to forecast bankruptcy using convolutional neural networks. Annals of Operations Research. 2022 doi: 10.1007/s10479-022-04780-7. DOI

Efat M, Alam I, Hajek P, Abedin MZ, Azad RU, Jaber M, Aditya S, Hassan MK. Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales. Annals of Operations Research. 2022 doi: 10.1007/s10479-022-04838-6. DOI

Feyen E, Gispert TA, Kliatskova T, Mare DS. Financial sector policy response to COVID-19 in emerging markets and developing economies. Journal of Banking and Finance. 2021;133:106184. doi: 10.1016/j.jbankfin.2021.106184. PubMed DOI PMC

Freedman DA. Statistical models: Theory and practice. Cambridge University Press; 2009.

Giudici P, Raffinetti E. Shapley-Lorenz eXplainable artificial intelligence. Expert Systems with Applications. 2021;167:114104. doi: 10.1016/j.eswa.2020.114104. DOI

Gössling S, Scott D, Hall CM. Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism. 2020;29(1):1–20. doi: 10.1080/09669582.2020.1758708. DOI

Handelman GS, Kok HK, Chandra RV, Razavi AH, Huang S, Brooks M, Lee MJ, Asadi H. Peering into the black box of artificial intelligence: Evaluation metrics of machine learning methods. American Journal of Roentgenology. 2019;212(1):38–43. doi: 10.2214/AJR.18.20224. PubMed DOI

He P, Sun Y, Zhang Y, Li T. COVID–19’s impact on stock prices across different sectors—An event study based on the Chinese stock market. Emerging Markets Finance and Trade. 2020;56(10):2198–2212. doi: 10.1080/1540496X.2020.1785865. DOI

He Q, Liu J, Wang S, Yu J. The impact of COVID-19 on stock markets. Economic and Political Studies. 2020;8(3):275–288. doi: 10.1080/20954816.2020.1757570. DOI

Hoffmann F, Bertram T, Mikut R, Reischl M, Nelles O. Benchmarking in classification and regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019;9(5):e1318.

Horvath A, Lang P. Do loan subsidies boost the real activity of small firms? Journal of Banking and Finance. 2021;122:105988. doi: 10.1016/j.jbankfin.2020.105988. DOI

Hosseini-Motlagh SM, Samani MRG, Homaei S. Design of control strategies to help prevent the spread of COVID-19 pandemic. European Journal of Operational Research. 2021;304(1):219–238. doi: 10.1016/j.ejor.2021.11.016. PubMed DOI PMC

Jabeur SB, Khalfaoui R, Arfi WB. The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning. Journal of Environmental Management. 2021;298:113511. doi: 10.1016/j.jenvman.2021.113511. PubMed DOI PMC

Jabeur SB, Mefteh-Wali S, Viviani JL. Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research. 2021 doi: 10.1007/s10479-021-04187-w. DOI

Jiang M, Jia L, Chen Z, Chen W. The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm. Annals of Operations Research. 2022;309:553–585. doi: 10.1007/s10479-020-03690-w. DOI

Jiang W. Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications. 2021;184:115537. doi: 10.1016/j.eswa.2021.115537. DOI

Kapalu N, Kodongo O. Financial markets' responses to COVID-19: A comparative analysis. Heliyon. 2022;8(9):e10469. doi: 10.1016/j.heliyon.2022.e10469. PubMed DOI PMC

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems. 2017;30:1–9.

Khalfaoui R, Ben Jabeur S, Hammoudeh S, Ben Arfi W. The role of political risk, uncertainty, and crude oil in predicting stock markets: Evidence from the UAE economy. Annals of Operations Research. 2023 doi: 10.1007/s10479-022-04824-y. DOI

Khatatbeh IN, Hani MB, Abu-Alfoul MN. The impact of COVID-19 pandemic on global stock markets: An event study. International Journal of Economics and Business Administration. 2020;8(4):505–514.

Kim JK, Sharman R, Rao HR, Upadhyaya S. Efficiency of critical incident management systems: Instrument development and validation. Decision Support Systems. 2007;44(1):235–250. doi: 10.1016/j.dss.2007.04.002. DOI

Kumar R, Mukherjee S, Choi TM, Dhamotharan L. Mining voices from self-expressed messages on social-media: Diagnostics of mental distress during COVID-19. Decision Support Systems. 2022;162:113792. doi: 10.1016/j.dss.2022.113792. PubMed DOI PMC

Liu C, Yin SQ, Zhang M, Zeng Y, Liu JY. An improved grid search algorithm for parameters optimization on SVM. Applied Mechanics and Materials. 2014;644:2216–2219. doi: 10.4028/www.scientific.net/AMM.644-650.2216. DOI

Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence. 2020;2(1):56–67. doi: 10.1038/s42256-019-0138-9. PubMed DOI PMC

Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Guyon I, Von Luxburg U, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, editors. Advances in Neural Information Processing Systems. Curran Associates Inc; 2017.

Molnar, C., Casalicchio, G., & Bischl, B. (2020). Interpretable machine learning—A brief history, state-of-the-art and challenges. In Joint European conference on machine learning and knowledge discovery in databases (pp. 417–431). Springer.

Nikolopoulos K, Punia S, Schäfers A, Tsinopoulos C, Vasilakis C. Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research. 2021;290(1):99–115. doi: 10.1016/j.ejor.2020.08.001. PubMed DOI PMC

Nti IK, Adekoya AF, Weyori BA. A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data. 2020;7(1):1–40. doi: 10.1186/s40537-020-00299-5. DOI

Phan DHB, Narayan PK. Country responses and the reaction of the stock market to COVID-19—A preliminary exposition. Emerging Markets Finance and Trade. 2020;56(10):2138–2150. doi: 10.1080/1540496X.2020.1784719. DOI

Repetto M. Multicriteria interpretability driven deep learning. Annals of Operations Research. 2022 doi: 10.1007/s10479-022-04692-6. DOI

Ribeiro GT, Santos AAP, Mariani VC, dos Santos Coelho L. Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility. Expert Systems with Applications. 2021;184:115490. doi: 10.1016/j.eswa.2021.115490. DOI

Sermpinis G, Karathanasopoulos A, Rosillo R, de la Fuente D. Neural networks in financial trading. Annals of Operations Research. 2021;297(1):293–308. doi: 10.1007/s10479-019-03144-y. DOI

Shapley LS. A value for n-person games. In: Kuhn HW, Tucker AW, editors. Contributions to the Theory of Games. Princeton University Press; 1997. pp. 69–79.

Sharma M, Mindermann S, Rogers-Smith C, Leech G, Snodin B, Ahuja J, Sandbrink JB, Monrad JT, Altman G, Dhaliwal G, Finnveden L. Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe. Nature Communications. 2021;12(1):1–13. doi: 10.1038/s41467-021-26013-4. PubMed DOI PMC

Sharmin A, Rahman M, Ahmed S, Ali SM. Addressing critical success factors for improving concurrent emergency management: Lessons learned from the COVID-19 pandemic. Annals of Operations Research. 2021 doi: 10.1007/s10479-021-04447-9. PubMed DOI PMC

Tan B, Igan D, Peria MSM, Pierri N, Presbitero AF. Government intervention and bank markups: Lessons from the global financial crisis for the COVID-19 crisis. Journal of Banking and Finance. 2021;133:106320. doi: 10.1016/j.jbankfin.2021.106320. PubMed DOI PMC

Topcu M, Gulal OS. The impact of COVID-19 on emerging stock markets. Finance Research Letters. 2020;36:101691. doi: 10.1016/j.frl.2020.101691. PubMed DOI PMC

Tuffour P, Opoku-Mensah E, Asiedu-Ayeh LO, Darko D. Assessing governments response to exogenous shocks: Considering the COVID-19 pandemic in the Ghanaian context. Journal of Public Affairs. 2021;22:e2755. doi: 10.1002/pa.2755. PubMed DOI PMC

Wang Y, Zhang H, Gao W, Yang C. COVID-19-related government interventions and travel and leisure stock. Journal of Hospitality and Tourism Management. 2021;49:189–194. doi: 10.1016/j.jhtm.2021.09.010. DOI

Weng, F., Hou, M., Zhang, T., Yang, Y., Wang, Z., Sun, H., Zhu, H., & Luo, J. (2018). Application of regularized extreme learning machine based on BIC criterion and genetic algorithm in iron ore price forecasting. In 3rd International conference on modelling, simulation and applied mathematics (MSAM 2018) (pp. 212–217). Atlantis Press.

Zaremba A, Kizys R, Aharon DY, Demir E. Infected markets: Novel coronavirus, government interventions, and stock return volatility around the globe. Finance Research Letters. 2020;35:101597. doi: 10.1016/j.frl.2020.101597. PubMed DOI PMC

Zhang D, Hu M, Ji Q. Financial markets under the global pandemic of COVID-19. Finance Research Letters. 2020;36:101528. doi: 10.1016/j.frl.2020.101528. PubMed DOI PMC

Zhang W, Gong X, Wang C, Ye X. Predicting stock market volatility based on textual sentiment: A nonlinear analysis. Journal of Forecasting. 2021;40(8):1479–1500. doi: 10.1002/for.2777. DOI