The Nexus between Financial Performance and Equilibrium: Empirical Evidence on Publicly Traded Companies from the Global Financial Crisis Up to the COVID-19 Pandemic
Bibliographic record
Abstract
Financial performance and financial equilibrium are two key aspects that should be monitored by any business manager interested in passing the test of time and overcoming unpredictable events such as economic crises. The organic link between financial performance and financial equilibrium has rarely been studied in the long run for companies listed on the stock market. The present article fills this gap in the literature by examining the degree to which financial performance influenced long-term financial equilibrium using data from 34 major companies publicly traded on the New York Stock Exchange and operating around the world in a wide variety of industries and sectors. The period of analysis spread over a decade (2007Q1–2020Q3) in order to cover two major crises that have marked the dawn of the third millennium and occurred relatively close to one another: the 2008 financial meltdown and the COVID-19 pandemic crisis. By means of panel data modelling, the study showed that the short-term and long-term financial equilibria of these public companies measured by current ratio, quick ratio and debt to equity ratio were significantly impacted by different financial performance indicators. The study addresses various implications of the empirical results and lays out avenues for future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".