Determinants of Financial Risks Pre- and Post-COVID-19 in Companies Listed on Euronext Lisbon
Bibliographic record
Abstract
The COVID-19 pandemic had a significant impact on the economy and the stability of financial markets, creating challenges and financial risks for companies. This study analyzes the financial reports of companies listed on Euronext Lisbon with the aim of examining financial risk disclosures and calculating their determinants. For this purpose, data was collected from the Euronext Lisbon website as well as the companies’ own websites. Once the data were gathered, 16 companies were analyzed over a five-year period, from 2018 to 2022. Using panel data regression techniques (e.g., fixed effects regression models), it was observed that profitability, capital structure, and size have a positive but not statistically significant relationship with interest risk. Conversely, size and capital structure they have a positive and significant relationship with liquidity risk. Profitability has a positive and significant relationship with insolvency risk. Macroeconomic variables do not exhibit consistent signs across all models. This research provides insights into how the determinants of financial risks influence risks during a pandemic period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".