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Predicting the Profitability of the Stock Market during a Pandemic

Year 2022, , 183 - 190, 30.06.2022

Jamilu Said Babangida , Attahir Abubakar , Suleiman Mamman , Fadwa Ben Brahim

https://doi.org/10.52791/aksarayiibd.908268

Abstract

This paper investigates the impact of the Covid-19 pandemic in predicting the profitability of the stock market of the ten most hit countries at the beginning of the pandemic. The study employed the Artificial Neural Network models for the analysis. Specifically, the Backward Propagation (BP) and Feed-Forward (FF) Neural Network models are used to predict the profitability of the stock market on a daily time frame. Taking Covid-19 into account, the estimation result shows that the Neural Network built is resilient in its ability to forecast the profitability of the stock market in Brazil and China. However, in the case of Germany, Russia, Turkey, and the United States, the Neural Network is partly resilient in its forecasting ability; predicted profitability deviated from the actual profitability in some of the periods. For the remaining countries in the sample, the Artificial Neural Network is found to have a weak prediction power.

Keywords

Covid-19 Stock market Artificial Neural Network

References

  • Baker, S. R., Bloom, N., Davis, J., Kost, K., Sammon, M., & Viratyosin, T. (2020). The unprecedented stock market reaction to Covid-19. Pandemics: Long-Run Effects, 1(DP 14543), 33–42.
  • Chatterjee, A., Ayadi, O. F., & Boone, B. E. (2000). Artificial neural network and the financial markets: A survey. Managerial Finance, 26(12), 32–45. https://doi.org/10.1108/03074350010767034
  • Corbet, S., Larkin, C. J., & Lucey, B. M. (2020). The Contagion Effects of the COVID-19 Pandemic: Evidence from Gold and Cryptocurrencies. SSRN Electronic Journal, 101554. https://doi.org/10.2139/ssrn.3564443
  • Ding, W., Levine, R. E., Lin, C., & Xie, W. (2020). Corporate Immunity to the COVID-19 Pandemic. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3578585
  • Feng, S., Li, L., Cen, L., & Huang, J. (2003). Using MLP networks to design a production scheduling system. Computers and Operations Research, 30(6), 821–832. https://doi.org/10.1016/S0305-0548(02)00044-8
  • Goodell, J. W. (2020). COVID-19 and finance: Agendas for future research. Finance Research Letters, 101512. https://doi.org/10.1016/j.frl.2020.101512
  • H.M.NishanthiHerath, P., Perera, A. A. I., & Wijekoon, H. P. (2014). Prediction of Dengue Outbreaks in Sri Lanka using Artificial Neural Networks. International Journal of Computer Applications, 101(15), 1–5. https://doi.org/10.5120/17760-8862
  • Laureano-Rosario, A., Duncan, A., Mendez-Lazaro, P., Garcia-Rejon, J., Gomez-Carro, S., Farfan-Ale, J., … Muller-Karger, F. (2018). Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease, 3(1), 5. https://doi.org/10.3390/tropicalmed3010005
  • Lim, K., & Liew, K. (2003). Testing for Non-Linearity in ASEAN Financial Markets. Finance. Retrieved from http://econwpa.wustl.edu/eps/fin/papers/0308/0308002.pdf%5Cnpapers2://publication/uuid/114CB99E-FB4E-4F25-A57E-E762A63D2B33
  • Lopez, D., Manogaran, G., & Jagan Mohan, J. (2017). Modelling the H1N1 influenza using mathematical and neural network approaches. Biomedical Research (India), 28(8), 3711–3715.
  • Marius-Constantin, P., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579–588.
  • Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Predicción del índice del mercado bursátil utilizando una red neuronal artificial. Journal of Economics, Finance and Administrative Science, 21(41), 89–93. https://doi.org/10.1016/j.jefas.2016.07.002
  • Ramelli, S., & Wagner, A. F. (2020). Feverish Stock Price Reactions to COVID-19. Swiss Finance Institute Research Paper Series, 20(12).
  • Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 Pandemic, Oil Prices, Stock Market and Policy Uncertainty Nexus in the US Economy: Fresh Evidence from the Wavelet-Based Approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3574699
  • Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39, 43–62.
  • Yu, Z., Qin, L., Chen, Y., & Parmar, M. D. (2020). Stock price forecasting based on LLE-BP neural network model. Physica A: Statistical Mechanics and Its Applications, 124197. https://doi.org/10.1016/j.physa.2020.124197
  • Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, (March), 101528. https://doi.org/10.1016/j.frl.2020.101528

Pandemi Döneminde Borsa Karlılığının Tahmini

Year 2022, , 183 - 190, 30.06.2022

Jamilu Said Babangida , Attahir Abubakar , Suleiman Mamman , Fadwa Ben Brahim

https://doi.org/10.52791/aksarayiibd.908268

Abstract

Bu makale, pandeminin başlangıcında en çok etkilenen on ülkenin borsalarının karlılığını tahmin etmede Covid-19 pandemisinin etkisini araştırmaktadır. Çalışma, analiz için Yapay Sinir Ağı modellerini kullandı. Spesifik olarak, Geriye Yayılım (BP) ve İleri Beslemeli (FF) Sinir Ağı modelleri, borsanın günlük bir zaman diliminde karlılığını tahmin etmek için kullanılır. Covid-19 dikkate alındığında, tahmin sonucu, oluşturulan Sinir Ağının Brezilya ve Çin'deki borsanın karlılığını tahmin etme yeteneğinde esnek olduğunu gösteriyor. Ancak Almanya, Rusya, Türkiye ve Amerika Birleşik Devletleri örneğinde, Sinir Ağı tahmin yeteneğinde kısmen esnektir; Bazı dönemlerde tahmin edilen kârlılık fiili kârlılıktan sapmıştır. Örneklemde kalan ülkeler için Yapay Sinir Ağının zayıf bir tahmin gücüne sahip olduğu bulunmuştur.

Keywords

Covid-19 Borsa piyasa Artificial Neural Network

References

  • Baker, S. R., Bloom, N., Davis, J., Kost, K., Sammon, M., & Viratyosin, T. (2020). The unprecedented stock market reaction to Covid-19. Pandemics: Long-Run Effects, 1(DP 14543), 33–42.
  • Chatterjee, A., Ayadi, O. F., & Boone, B. E. (2000). Artificial neural network and the financial markets: A survey. Managerial Finance, 26(12), 32–45. https://doi.org/10.1108/03074350010767034
  • Corbet, S., Larkin, C. J., & Lucey, B. M. (2020). The Contagion Effects of the COVID-19 Pandemic: Evidence from Gold and Cryptocurrencies. SSRN Electronic Journal, 101554. https://doi.org/10.2139/ssrn.3564443
  • Ding, W., Levine, R. E., Lin, C., & Xie, W. (2020). Corporate Immunity to the COVID-19 Pandemic. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3578585
  • Feng, S., Li, L., Cen, L., & Huang, J. (2003). Using MLP networks to design a production scheduling system. Computers and Operations Research, 30(6), 821–832. https://doi.org/10.1016/S0305-0548(02)00044-8
  • Goodell, J. W. (2020). COVID-19 and finance: Agendas for future research. Finance Research Letters, 101512. https://doi.org/10.1016/j.frl.2020.101512
  • H.M.NishanthiHerath, P., Perera, A. A. I., & Wijekoon, H. P. (2014). Prediction of Dengue Outbreaks in Sri Lanka using Artificial Neural Networks. International Journal of Computer Applications, 101(15), 1–5. https://doi.org/10.5120/17760-8862
  • Laureano-Rosario, A., Duncan, A., Mendez-Lazaro, P., Garcia-Rejon, J., Gomez-Carro, S., Farfan-Ale, J., … Muller-Karger, F. (2018). Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico. Tropical Medicine and Infectious Disease, 3(1), 5. https://doi.org/10.3390/tropicalmed3010005
  • Lim, K., & Liew, K. (2003). Testing for Non-Linearity in ASEAN Financial Markets. Finance. Retrieved from http://econwpa.wustl.edu/eps/fin/papers/0308/0308002.pdf%5Cnpapers2://publication/uuid/114CB99E-FB4E-4F25-A57E-E762A63D2B33
  • Lopez, D., Manogaran, G., & Jagan Mohan, J. (2017). Modelling the H1N1 influenza using mathematical and neural network approaches. Biomedical Research (India), 28(8), 3711–3715.
  • Marius-Constantin, P., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579–588.
  • Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Predicción del índice del mercado bursátil utilizando una red neuronal artificial. Journal of Economics, Finance and Administrative Science, 21(41), 89–93. https://doi.org/10.1016/j.jefas.2016.07.002
  • Ramelli, S., & Wagner, A. F. (2020). Feverish Stock Price Reactions to COVID-19. Swiss Finance Institute Research Paper Series, 20(12).
  • Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 Pandemic, Oil Prices, Stock Market and Policy Uncertainty Nexus in the US Economy: Fresh Evidence from the Wavelet-Based Approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3574699
  • Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39, 43–62.
  • Yu, Z., Qin, L., Chen, Y., & Parmar, M. D. (2020). Stock price forecasting based on LLE-BP neural network model. Physica A: Statistical Mechanics and Its Applications, 124197. https://doi.org/10.1016/j.physa.2020.124197
  • Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, (March), 101528. https://doi.org/10.1016/j.frl.2020.101528
There are 17 citations in total.

Details

Primary Language English
Subjects Business Administration
Journal Section Research Article
Authors

Jamilu Said Babangida 0000-0002-7178-0978

Attahir Abubakar 0000-0002-5238-3694

Suleiman Mamman 0000-0003-3204-0595

Fadwa Ben Brahim 0000-0001-5334-8324

Publication Date June 30, 2022
Published in Issue Year 2022

Cite

APA Babangida, J. S., Abubakar, A., Mamman, S., Ben Brahim, F. (2022). Predicting the Profitability of the Stock Market during a Pandemic. Aksaray Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 14(2), 183-190. https://doi.org/10.52791/aksarayiibd.908268