A Statistical Analysis of Companies’ Financing Strategies in Portugal during the COVID-19 Pandemic
Bibliographic record
Abstract
This study aims to establish which sources of financing were used and the relevance of different banking products for Portuguese companies during the pandemic. We also intend to understand the determinants of companies’ financing options and what lies behind their decisions concerning the appropriate level of debt. A quantitative methodology was used, based on a questionnaire given to Portuguese companies to analyse different financing issues. The sample was composed of 1957 companies with a business volume of more than EUR 500,000 per year. The results show that Portuguese companies focused on managing liquidity and corporate risk. We found evidence that companies kept financing themselves by banking products such as in the pre-pandemic period, although 29.6% resorted to the LAE-COVID economy support line. Companies decide on the appropriate amount of debt based on the nature of the business, the phase of the life cycle in which the company is, the cash flows’ volatility, accounting results, credit rating, and fiscal benefits. Academicians and companies should master the concept of company financing and adopt strategies to consider the level of debt and refine the banking products to be used. Although the literature on business financial management usually claims that all crises are the same, the COVID-19 pandemic not only caused a recession but also forced people and companies to adapt to a new environment. Portuguese companies have shown resilience and focus on their adoption of good financing practices.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".