Capital Structure and Firm Performance: Evidence from FTSE All-Share Firms During COVID-19
Bibliographic record
Abstract
We examine how capital structure related to firm performance for UK companies in the FTSE All-Share over 2018–2023, explicitly segmenting pre-pandemic (2018–2019), pandemic (2020–2021), and post-pandemic (2022–2023) periods. Using Bloomberg data for 516 firms and panel fixed-effects models (Hausman-tested), we assess the impact of short- and long-term leverage on ROA, ROCE, Tobin’s Q, and EPS, and compare financial versus non-financial firms. Leverage is, on average, negatively associated with ROA and EPS, consistent with pecking-order and agency-cost arguments: market-based outcomes (Tobin’s Q) show weaker, nuanced links. The adverse effects of debt are stronger for non-financial firms, particularly during and after COVID-19, while financial firms display a post-COVID positive association between short-term debt and ROA, suggesting sector-specific debt utilization under stress. Firm size relates negatively to Tobin’s Q for non-financials. Results highlight how crisis conditions and industry characteristics shape the leverage–performance nexus, offering practical guidance for managers and policymakers on capital structure decisions in turbulent environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".