The Future of African Financial Markets: Modelling in Traditional and Machine Learning Techniques

A.A. Agyemang-Badu¹ , I.N. McSeplus²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.1 , pp.13-26, Feb-2021

Online published on Feb 28, 2021

Copyright © A.A. Agyemang-Badu, I.N. McSeplus . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The opportunities, prospects and challenges of Africa is frequently been trumpeted by several scholars and experts. This study examines African Financial Markets using both traditional modelling techniques and machine learning tools. We selected proxy countries based on the macroeconomic factors and stock markets analyzed. The study revealed that even though traditional modelling can be used for modelling time series, it can significantly be improve by machine learning tools. The results show that Africa stock markets are profitably but have some underlying risk that is linked to the economy of the African economies. We highlight the weakness in the macroeconomic factors such as weakness in managing inflation, high borrowing rate, increased interests rate as well as slow growth in FDI and GDP. We recommend swift policy measures to deal with these weaknesses.

Key-Words / Index Term :
Africa, Macroeconomic factors, Fast Fourier Transformation, Principal Component Analysis, COVID-19

References :
[1] G. Boako and P. Alagidede, “Co-movement of Africa’s equity markets: Regional and global analysis in the frequency-time domains” Physica A., Vol. 468, pp. 359-380, 2017.
[2] African Development Bank, “African Economic Outlook 2020: Developing Africa’s Workforce for the Future”. 2020.
[3] International Finance Corporation, “Shaping the Future of Africa Markets and Opportunities for Private Investors”, Washington: IFC. 2018.
[4] A. Allen, I. Otchere and L. Sebbet, “African Financial Systems: A Review” Review of Development Finance 1, pp.79-113, 2011.
[5] African Development Bank, “African Economic Outlook 2020: Supplement Amid COVID-19”, 2020.
[6] African Development Bank, “African Economic Outlook 2020: Developing Africa’s Workforce for the Future” 2020.
[7] L. Senbet and I. Otchere, “African Stock Markets: Ingredients for Development and Capacity Building” Edited Q. Marc and V. Genevieve, Palgrave (Eds.) Macmillan Publishing, in African Finance in the 21th Century 2010.
[8] G. Gondwe “Assessing the Impact of COVID-19 on Africa’s Economic Development” UNCTAD/ALDC/MISC/2020/3 2020.
[9] G. E. P. Box and G. M. Jenkins, “Time Series Analysis: Forecasting and Control”, Second ed. Holden-Day, San Francisco 1976.
[10] H. Tong, “Non-linear Time Series”, Oxford University Press, Oxford 1990.
[11] L. Xiao and A. Aydemir, “Volatility modelling and forecasting in finance”, John Knight, Stephen Satchell, In Quantitative Finance, Forecasting volatility in the financial markets (Third Edition), Butterworth-Heinemann, pp. 1-45 2007.
[12] R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of variance of UK inflation”, Econometrica, Vol. 50, pp. 987-1008. 1982.
[13] T. Bollerslev, “Generalized autoregressive conditional heteroscedasticity”, Journal of Econometrics, Vol. 31, pp. 307-327, 1986.
[14] S. J. Taylor, “Modelling Financial Time Series”, Chichester: John Wiley, 1986.
[15] C. W. J. Granger, “Some Properties of Time Series Data and their Use in Econometric Model Specification”, Journal of Econometrics, Vol. 16, pp. 121-130, 1981.
[16] C. W. J. Granger and A. A. Weiss, “Time series analysis of error-correcting models”, Edited: S. Karlln, eds., Studies in econometrics, time series, and multivariate analysis, Academic Press, New York pp. 255-278, 1983.
[17] R. F. Engle and C. W. J. Granger, “Cointegration and Error-Correction: Representation, Estimation and Testing”, Econometrica, Vol. 55, pp. 251-279, 1987.
[18] S. Johansen, “Statistical analysis of cointegration vectors”. Journal of economic dynamics and control, Vol. 12 No. 2-3, pp. 231-254, 1988.