Effect of capital market on economic growth: An analysis using the autoregressive distributed lag (ARDL) approach
Vol 7, Issue 2, 2024
VIEWS - 39 (Abstract) 26 (PDF)
Abstract
The limits testing method known as ARDL (Autoregressive Distributed Lag) examines Nigerian capital market improvement and monetary development. Relevant indicators about the capital market are taken into account during analysis while considering influencing factors as well. This study aims to explore the relationship between the capital market and financial development in Nigeria. The study’s theoretical framework, which is based on the ARDL framework, includes both short- and long-term dynamics. The data used in this analysis was collected from the Central Bank of Nigeria (CBN), the World Bank database, and the Nigerian Stock Exchange (NSE). In this study, the Nigerian All-Share Index, foreign direct investment, currency exchange rates, and inflation rates are the free factors, while the increase in GDP is the dependent factor. Through cointegration analysis using the ARDL framework, it was discovered that in Nigeria there’s a lengthy equilibrium correlation between economic expansion and financial development. The coefficients determined allow for a deeper understanding of how capital market factors affect economic growth over time. Furthermore, utilizing an error correction model derived from the ARDL analysis offers insight into both brief dynamics and modified speed toward reaching a lasting state of balance. By utilizing the ARDL approach, this study adds to the continuing discourse surrounding how capital markets impact growth in Nigeria. Its empirical evidence provides valuable knowledge for policymakers and stakeholders looking to utilize capital markets effectively toward achieving long-lasting economic development within the country.
Keywords
Full Text:
PDFReferences
1. Han J, He J, Pan Z, et al. Twenty Years of Accounting and Finance Research on the Chinese Capital Market. Abacus. 2018; 54(4): 576-599. doi: 10.1111/abac.12143
2. Teytelboym A. Natural Capital Market Design. SSRN Electronic Journal. 2018. doi: 10.2139/ssrn.3250413
3. Hubbard RG. Capital-Market Imperfections and Investment. National Bureau of Economic Research; 1997. doi: 10.3386/w5996
4. Merton RC. A Simple Model of Capital Market Equilibrium with Incomplete Information. The Journal of Finance. 1987; 42(3): 483-510. doi: 10.1111/j.1540-6261.1987.tb04565.x
5. Zhou S, Simnett R, Green W. Does Integrated Reporting Matter to the Capital Market? SSRN Electronic Journal. 2015. doi: 10.2139/ssrn.2600364
6. Afolabi A. Impact of the Nigerian capital market on the economy. European Journal of Accounting Auditing and Finance Research. 2015; 3(2): 88-96.
7. Badertscher BA, Katz SP, Rego SO, et al. Conforming Tax Avoidance and Capital Market Pressure. The Accounting Review. 2019; 94(6): 1-30. doi: 10.2308/accr-52359
8. Briggs AP. Capital Market and Economic Growth of Nigeria. Research Journal of Finance and Accounting. 2015; 6, 82-93.
9. Aobdia D, Lin CJ, Petacchi R. Capital Market Consequences of Audit Partner Quality. Financial Accounting eJournal. 2015.
10. Baker MP, Wurgler JA. Market Timing and Capital Structure. SSRN Electronic Journal. 2001. doi: 10.2139/ssrn.267327
11. Radhakrishnan S, Wang Z, Zhang Y. Customers’ Capital Market Information Quality and Suppliers’ Performance. Production and Operations Management. 2014; 23(10): 1690-1705. doi: 10.1111/poms.12211
12. Brochet F, Naranjo P, Yu G. The Capital Market Consequences of Language Barriers in the Conference Calls of Non-U.S. Firms. The Accounting Review. 2016; 91(4): 1023-1049. doi: 10.2308/accr-51387
13. Adepoju AA, Ogundunmade TP. A Longitudinal and Cross-Regional Analysis of Economic Growth and its Determinants based on Bayesian Model Averaging. Journal of the Nigerian Association of Mathematical Physics. 2018; 46: 45-154.
14. Ayansola OA, Ogundunmade TP, Adedamola AO. Modelling Willingness to Pay of Electricity Supply Using Machine Learning Approach. Modern Economy and Management. 2022; 1: 9. doi: 10.53964/mem.2022009
15. Cho YJ. Segment Disclosure Transparency and Internal Capital Market Efficiency: Evidence from SFAS No. 131. Journal of Accounting Research. 2015; 53(4): 669-723. doi: 10.1111/1475-679x.12089
16. Edame GE, Okoro U. The Impact of Capital Market on Economic Growth in Nigeria. Journal of Poverty, Investment and Development. 2013; 1: 45-56.
17. Chen M, Cheng S, Hwang Y. An empirical investigation of the relationship between intellectual capital and firms’ market value and financial performance. Journal of Intellectual Capital. 2005; 6(2): 159-176. doi: 10.1108/14691930510592771
18. Guo L, Li FW, Wei KC. Security Analysts and Capital Market Anomalies. SSRN Electronic Journal. 2018. doi: 10.2139/ssrn.3101672
19. Hu GX, Pan J, Wang J. Chinese Capital Market: An Empirical Overview. SSRN Electronic Journal. 2017. doi: 10.2139/ssrn.3095056
20. Kim H, Song J. Filling institutional voids in emerging economies: The impact of capital market development and business groups on M&A deal abandonment. Journal of International Business Studies. 2016; 48(3): 308-323. doi: 10.1057/s41267-016-0025-0
21. Lee LF, Hutton AP, Shu S. The Role of Social Media in the Capital Market: Evidence from Consumer Product Recalls. SSRN Electronic Journal. 2015. doi: 10.2139/ssrn.2557212
22. Lin L. Engineering a Venture Capital Market: Lessons from China. Columbia Journal of Asian Law. 2017; 30(2): 160-220. doi: 10.52214/cjal.v30i2.9251
23. Park CY. Asian Capital Market Integration: Theory and Evidence. SSRN Electronic Journal. 2013. doi: 10.2139/ssrn.2282305
24. Sharpe WF. Capital asset prices: a theory of market equilibrium under conditions of risk. The Journal of Finance. 1964; 19(3): 425-442. doi: 10.1111/j.1540-6261.1964.tb02865.x
25. Civcir I, Akkoc U. Non-linear ARDL approach to the oil-stock nexus: Detailed sectoral analysis of the Turkish stock market. Resources Policy. 2021; 74: 102424. doi: 10.1016/j.resourpol.2021.102424
26. Ogundunmade TP, Abidoye M, Olunfunbi OM. Modelling Residential Housing Rent Price Using Machine Learning Models. Modern Economy and Management. 2023; 2: 14. doi: 10.53964/mem.2023014
27. Ogundunmade TP, Daniel AO, M. Awwal A. Modelling Infant Mortality Rate using Time Series Models. International Journal of Data Science. 2023; 4(2): 107-115. doi: 10.18517/ijods.4.2.107-115.2023
28. Ogundunmade TP, Adepoju AA. Predicting the Nature of Terrorist Attacks in Nigeria Using Bayesian Neural Network Model. In: Awe OO, Vance EA. (editors). Sustainable Statistical and Data Science Methods and Practices. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham; 2023.
29. Ogundunmade TP, Adepoju AA. Modelling Liquefied Petroleum Gas Prices in Nigeria Using Time Series Machine Learning Models. Modern Economy and Management. 2022; 1: 5. doi: 10.53964/mem.2022005
30. Adepoju AA, Ogundunmade TP, Adebayo KB. Regression Methods in the presence of heteroscdesticity and outliers, Academia Journal of Scientific Research. 2017; 5(2): 776-783.
31. Ogundunmade TP, Adepoju AA. Prior Specification in Bayesian Modelling: An application to Economic Growth, Anale. Seria Informatică. 2018.
DOI: https://doi.org/10.24294/fsj7495
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Tayo P. Ogundunmade
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.