Necessary and Sufficient Conditions for Liquidity Management
Bibliographic record
Abstract
Liquidity as a measure of payment capacity must incorporate the attributes of efficiency, sustainability and synergy. Traditionally, liquidity is measured by financial indicators, centered in the current ratio (CR) as an indicator of nominal payment capacity. However, this indicator generates a gap in the liquidity assessment because it does not measure financial efficiency nor liquidity sustainability. This research paper proposes an indicator that combines nominal capacity with effective payment capacity that indicates the liquidity sustainability and financial efficient status, addressing a gap in the literature concerning liquidity management, and revealing the existence of financial synergy. In order to test this proposition, data from financial statements of 37 manufacturing firms from 2000 to 2015 were used, via parametric and nonparametric methods. In the analysis showed here, financial efficiency ratio (FER) and the liquidity sustainability ratio (LSR) were used to assess financial efficiency and sustainable liquidity. Robust empirical evidence was found showing that the main status of the firms’ liquidity is weakly sustainable and therefore does not produce financial synergy. The results suggest that the combination of financial efficiency and nominal liquidity is a robust technique to indicate the firm’s liquidity status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".