How Corporate Liquidity Reacts to COVID-19 Outbreak? Evidence from Listed Corporations in Saudi Arabia
Bibliographic record
Abstract
The outbreak of the COVID-19 pandemic remarkably influenced the global economy and hence corporate decisions. This study investigates corporate liquidity policy reaction to the COVID-19 pandemic by comparing corporate cash holdings levels before and after the COVID-19 crisis. Using quarterly data of listed non-financial firms in Saudi Arabia over the years 2018-2021 and employing a paired-sample univariate analysis, this study found that the COVID-19 outbreak significantly influenced corporate liquidity decisions. In particular, the results show a significant difference in the cash holdings levels between the periods before and after the COVID-19 crisis. Firms’ cash holdings are significantly higher after COVID-19 in comparison to cash holdings before the pandemic. These findings support the trade-off theory’s precautionary and transaction cost motives for holding liquid assets. The outcomes of this study provide important implications for policymakers, corporate managers, and lenders.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".