Were the manufacturing companies resilient in the face of COVID-19 or did they take advantage?
Abstract
The research paper aims to build a composite index of the financial performance of companies, to find if the impact of the COVID-19 crisis was significantly positive for most manufacturing companies listed on Bucharest Stock Exchange, and to look if the manufacturing companies were resilient being prepared with savings that could have mitigate the effects of this pandemic crisis. The results of the FE model selected show that 31.67% of the company’s equity variation is justified by the two independent variables, the stronger correlation of equity being with reserves. Based on the composite index of financial performance built, the manufacturing companies were grouped in three clusters: a cluster with low financial performance companies (z < 4), a cluster with good financial performance companies (4 ≤ z ≤ 8) and a cluster with high financial performance companies (z > 8). The third cluster groups the most analysed companies, on which the pandemic crisis had a positive impact, which achieved the highest financial performance; they are those companies that “take advantage” from the COVID-19 crisis, adapting their business strategy to the market conditions imposed. The article adds value to the specialty literature by building the financial performance’s composite indicator, clustering the manufacturing companies by financial performance’ Z-score.
Keyword : composite index, financial performance, principal component analysis, manufacturing companies, COVID-19, cluster
How to Cite
Sabău-Popa, C. D., Rus, L., Florea, A., Ban, O.-I., Dzitac, S., & Marcu, O. A. (2024). Were the manufacturing companies resilient in the face of COVID-19 or did they take advantage?. Technological and Economic Development of Economy, 30(5), 1372–1391. https://doi.org/10.3846/tede.2024.20806
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Baltagi, B. H. (2001). Econometric analysis of panel data (2nd ed.). Wiley.
Barbu, A., Militaru, G., Deselnicu, D., & Catana, S. A. (2021). Key success factors that enable IT service providers to achieve organizational performance: Evidence from Romania. Sustainability, 13(19), Article 10996. https://doi.org/10.3390/su131910996
Batrancea, L. (2021). The nexus between financial performance and equilibrium: Empirical evidence on publicly traded companies from the global financial crisis up to the COVID-19 pandemic. Journal of Risk and Financial Management, 14(5), Article 218. https://doi.org/10.3390/jrfm14050218
Beleneși, M., Bogdan, V., & Popa, D. N. (2021). Disclosure dynamics and non-financial reporting analysis. The case of Romanian listed companies. Sustainability, 13(9), Article 4732. https://doi.org/10.3390/su13094732
Boloș, M. I., Bradea I., & Delcea, C. (2019a). Modeling the performance indicators of financial assets with neutrosophic fuzzy numbers. Symmetry-Basel, 11(8), Article 1021. https://doi.org/10.3390/sym11081021
Boloș, M. I., Bradea I., & Delcea, C. (2019b). A fuzzy logic algorithm for optimizing the investment decisions within companies. Symmetry-Basel, 11(2), Article 186. https://doi.org/10.3390/sym11020186
Breusch, T. S., & Pagan, A. R. (1980). The Lagrange multiplier test and its applications to model specification in econometrics. Review of Economic Studies, 47(1), 239–253. https://doi.org/10.2307/2297111
Ding, W., Levine, R., Lin, C., & Xie, W. (2021). Corporate immunity to the COVID-19 pandemic. Journal of Financial Economics, 141(2), 802–830. https://doi.org/10.1016/j.jfineco.2021.03.005
Dospinescu, O., & Dospinescu, N. (2018). A profitability regression model of Romanian stock exchange’s energy companies. In Proceedings of the IE 2018 International Conference (pp. 169–174). www.conferenceie.ase.ro
FTSE Classification of Equity Markets. (2023). FTSE equity country classification interim announcement, March 2023. Retrieved September 12, 2023, from https://research.ftserussell.com/products/downloads/FTSE_Interim_Country_Classification_Review_2023.pdf
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. https://doi.org/10.2307/1913827
Hornuf, L., & Schwienbacher, A. (2017). Should securities regulation promote equity crowdfunding? Small Business Economics, 49, 579–593. https://doi.org/10.1007/s11187-017-9839-9
Hornuf, L., & Schwienbacher, A. (2018). Market mechanisms and funding dynamics in equity crowdfunding. Journal of Corporate Finance, 50(C), 556–574. https://doi.org/10.1016/j.jcorpfin.2017.08.009
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(7), 498–520. https://doi.org/10.1037/h0070888
Ilinova, A., Dmitrieva, D., & Kraslawski, A. (2021). Influence of COVID-19 pandemic on fertilizer companies: The role of competitive advantages. Resources Policy, 71, Article 102019. https://doi.org/10.1016/j.resourpol.2021.102019
Imbrescu, C. M., & Hațegan, C. D. (2017). Ex-ante study about disclosure of non-financial information by Romanian companies from agriculture and manufacture of food products. Journal of Economics and Business Research, 23(2), 45–58. https://www.uav.ro/jour/index.php/jebr/article/view/1146
Ionita, C., & Dinu, E. (2021). The effect of intangible assets on sustainable growth and firm value – Evidence on intellectual capital investment in companies listed on Bucharest Stock Exchange. Kybernetes, 50(10), 2823–2849. https://doi.org/10.1108/K-05-2020-0325
Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (6th ed.). Prentice Hall.
Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer-Verlag Press.
Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116
Kalaycı, B., Özmen, A., & Weber, G.-W. (2020). Mutual relevance of investor sentiment and finance by modeling coupled stochastic systems with MARS. Annals of Operations Research, 295, 183–206. https://doi.org/10.1007/s10479-020-03757-8
Kennedy, P. (2008). A guide to econometrics (6th ed.). Blackwell Publications.
Khalilpourazari, S., Teimoori, S., Mirzazadeh, A., Pasandideh, S. M. R., & Tehrani, N. G. (2019). Robust fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty. Journal of Industrial and Production Engineering, 36(5), 276–285. https://doi.org/10.1080/21681015.2019.1646328
Kraus, S., Clauss, T., Breier, M., Gast, J., Zardini, A., & Tiberius, V. (2020). The economics of COVID-19: Initial empirical evidence on how family firms in five European countries cope with the corona crisis. International Journal of Entrepreneurial Behavior & Research, 26(5), 1067–1092. https://doi.org/10.1108/IJEBR-04-2020-0214
Kropat, E., Türkay, M., &Weber, G.-W. (2020). Introduction to the special issue on fuzzy analytics and stochastic methods in neurosciences. IEEE Transactions on Fuzzy Systems, 28(1), 1–4. https://doi.org/10.1109/TFUZZ.2019.2959462
Kropat, E., Özmen, A., Weber, G.-W., Meyer-Nieberg, S., & Defterli, O. (2016). Fuzzy prediction strategies for gene-environment networks – Fuzzy regression analysis for two-modal regulatory systems. RAIRO – Operations Research, 50(2), 413–435. https://doi.org/10.1051/ro/2015044
Kumar, S., & Zbib, L. (2022). Firm performance during the COVID-19 crisis: Does managerial ability matter? Finance Research Letters, 47(B), Article 102720. https://doi.org/10.1016/j.frl.2022.102720
Lin, A., Chang, H. Y., & Hung, B. (2022). Identifying key financial, environmental, social, governance (ESG), bond, and COVID-19 factors affecting global shipping companies – A hybrid multiple-criteria decision-making method. Sustainability, 14(9), Article 5148. https://doi.org/10.3390/su14095148
Man, M., & Bogeanu-Popa, M. M. (2020). Impact of non-financial information on sustainable reporting of organisations’ performance: Case study on the companies listed on the Bucharest stock exchange. Sustainability, 12(6), Article 2179. https://doi.org/10.3390/su12062179
Marom, S., & Lussier, R. N. (2021). Business strategy at times of crisis: Leveraging core competencies to sustain competitive advantage. International Journal of Business Environment, 12(4), 321–337. https://doi.org/10.1504/IJBE.2021.118540
Mattera, M., Ruiz-Morales, C. A., Gava, L., & Soto, F. (2022). Sustainable business models to create sustainable competitive advantages: Strategic approach to overcoming COVID-19 crisis and improve financial performance. Competitiveness Review, 32(3), 455–474. https://doi.org/10.1108/CR-03-2021-0035
Milošević, D., Popović, J., Avakumović, J., & Kvrgić, G. (2020). The impact of the equity capital and trade credit financial sources on the company’s performances sustainability. Economics of Agriculture, 67(3), 735–746. https://doi.org/10.5937/ekoPolj2003735M
Nguyen, H. T. X. (2022). The effect of COVID-19 pandemic on financial performance of firms: Empirical evidence from Vietnamese logistics enterprises. Journal of Asian Finance Economics and Business, 9(2), 177–183. https://doi.org/10.13106/jafeb.2022.vol9.no2.0177
Nguyen T. N. L., & Nguyen, V. C. (2020). The determinants of profitability in listed enterprises: A study from Vietnamese stock exchange. Journal of Asian Finance, Economics and Business, 7(1), 47–58. https://doi.org/10.13106/jafeb.2020.vol7.no1.47
Nyikos, G., Soha, B., & Beres, A. (2021). Entrepreneurial resilience and firm performance during the COVID-19 crisis – Evidence from Hungary. Regional Statistics, 11(3), 29–59. https://doi.org/10.15196/RS110307
Olczyk, M., & Kuc-Czarnecka, M. E. (2021). Determinants of COVID-19 impact on the private sector: A multi-country analysis based on survey data. Energies, 14(14), Article 4155. https://doi.org/10.3390/en14144155
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11), 559–572. https://pca.narod.ru/pearson1901.pdf
Popa, D. N., Bogdan, V., Sabău-Popa, C. D., Beleneși, M., & Bădulescu, A. (2022). Performance mapping in two-step cluster analysis through ESEG disclosures and EPS. Kybernetes, 51(13), 98–118. https://doi.org/10.1108/K-08-2021-0672
Porter, M. E. (1997). Competitive strategy. Measuring Business Excellence, 1(2), 12–17. https://doi.org/10.1108/eb025476
Rababah, A., Al-Haddad, L., Sial, M. S., Chunmei, Z., & Cherian, J. (2020). Analyzing the effects of COVID-19 pandemic on the financial performance of Chinese listed companies. Journal of Public Affairs, 20(4), Article e2440. https://doi.org/10.1002/pa.2440
Ren, Z., Zhang, X., Zhang, Z. (2021). New evidence on COVID-19 and firm performance. Economic Analysis and Policy, 72, 213–225. https://doi.org/10.1016/j.eap.2021.08.002
Sabău-Popa, C. D., Popa, D. N., Bogdan. V., & Simut, R. (2021). Composite financial performance index prediction – A neural networks approach. Journal of Business Economics and Management, 22(2), 277–296. https://doi.org/10.3846/jbem.2021.14000
Sabău-Popa, C. D., Simut, R., Droj, L., & Bențe, C. (2020). Analyzing financial health of the SMES listed in the AERO market of Bucharest stock exchange using principal component analysis. Sustainability, 12(9), Article 3726. https://doi.org/10.3390/su12093726
Savku, E., & Weber, G.-W. (2018). A stochastic maximum principle for a Markov regime-switching jump-diffusion model with delay and an application to finance. Journal of Optimization Theory and Applications, 179, 696–721. https://doi.org/10.1007/s10957-017-1159-3
Shen, H., Fu, M., Pan, H., Yu, Z., & Chen, Y. (2020). The impact of the COVID-19 pandemic on firm performance. Emerging Markets Finance and Trade, 56(10), 2213–2230. https://doi.org/10.1080/1540496X.2020.1785863
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