ICT Adoption and Stock Market Development in Africa: An Application of the Panel ARDL Bounds Testing Procedure
Bibliographic record
Abstract
The nexus between Information Communication Technology (ICT) and stock market development has been predominantly based on studies of the developed markets and high-income economies of the world. The objective of this study was to examine the causal relationship between ICT adoption and stock market development in Africa. The study examined a panel of 11 African stock exchanges for the period 2008–2017 and employed the panel ARDL bounds testing procedure to test for cointegration and examine the causal relationship between ICT adoption and stock market development. The dependent variable employed was the stock market development index (FINDEX), while the independent variable was the ICT adoption index (ICTDEX), and the financial freedom index (FFI) was employed as a control variable. Firstly, the results of the study documented that the variables are cointegrated in the long term. Secondly, the results of the study documented a bi-directional causal relationship (complementarity) between ICT adoption and stock market development. In essence, ICT adoption and stock market development reinforce each other. Thirdly, the study established a causal relationship running from financial freedom to stock market development. This lends credence to the notion that financial market deregulation promotes stock market development. Lastly, a positive causal relationship that ran from financial freedom to stock market development was documented. This study contributes to the body of knowledge in the sense that it is the first study to examine the phenomenon of the ICT–stock market development nexus by employing a panel study. Hitherto, studies were mainly country-specific in nature. The findings of the research imply that policymakers should be more resolute when formulating ICT policies, as ICT adoption can drive stock market development and vice versa for better economic growth. Policymakers should embrace policies that support the deregulation of stock markets as this will lead to the development of the latter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".