Impact Analysis of Pandemic on Nigeria’s Stock Market Performance
Bibliographic record
Abstract
We empirically conducted a distinct analysis of the efficiency of the stock market before and during the Covid-19 pandemic in Nigeria as well as the impact of the Covid-19 pandemic on several indicators of stock market performance. Data were collected on stock variables on monthly basis for the pre-Covid-19 era (2018M02 to 2020M01) and Covid-19 pandemic era (2020M02 to 2022M01) from the Central Bank of Nigeria Statistical Bulletin, while data on the numbers of daily new confirmed cases (New cases and deaths) as well as the government response stringency index on COVID-19 pandemic were obtained from Our World in Data (OWID). We leveraged numerous advantages of the Data Envelopment Analysis (DEA) to estimate stock market efficiency before and during the Covid-19 pandemic for the purpose of comparison. Also, we employed the Autoregressive Distributed Lag Mixed Data Sampling (ADL-MIDAS) approach to conduct the impact analysis of the Covid-19 pandemic on stock market performance. We found that, in terms of efficiency, the stock market was more efficient during the Covid-19 pandemic than in the pre-Covid-19 era, being the only active market among other financial markets especially when several restrictions and total lockdown were imposed. In terms of returns and volatility, the study concluded that the Covid-19 pandemic did not significantly influence Nigeria’s stock market performance negatively. However, the government stringency measures had a significant positive impact on the stock market return in Nigeria. Our findings are instructive to policymaking and financial regulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".