Predicting Nigeria’s Economic Growth through the Financial Markets: A Comparative Analysis
Abstract
This study focused on predicting Nigeria’s economic growth through financial market operations for the period 1981-2016. For the purpose of the study the financial market was broken down into the money and capital markets and a comparative analysis carried out in order to determine which arm of the market contributes more to Nigeria’s economic growth. The study made use of secondary data sourced from the CBN statistical bulletin and different statistical tools were employed in analyzing the data. The Johansen co-integration test result revealed that the money and capital market investment components and gross domestic product are co-integrated, indicating the existence of long-run equilibrium relationship between the correlates in Nigeria. The money market ECM result indicated that only treasury bills outstanding has a positive and significant relationship with GDP and the capital market ECM result revealed that only outstanding value of equity conforms with a priori expectation by having a positive and significant relationship with GDP. From the summary of findings, the study concludes that of all the money market funding elements examined, only treasury bills outstanding is significant in predicting Nigeria’s economic growth and of the three capital market funding elements examined, only outstanding value of equity is significant in predicting Nigeria’s economic growth. Also, from causality analysis, treasury bills, commercial papers and outstanding value of equity drives growth in Nigeria’s GDP while bonds and government securities outstanding are driven by growth in Nigeria’s GDP. The study therefore recommends that there is a need to evolve comprehensive measures to strengthen, broaden and deepen the market to enhance its intermediary role in financing economic activities.Terms and conditions of Creative Commons Attribution 4.0 International License apply to all published manuscripts. This Journal is licensed under a Creative Commons Attribution 4.0 International License. This licence allows authors to use all articles, data sets, graphics and appendices in data mining applications, search engines, web sites, blogs and other platforms by providing appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
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