Credit Quality of Trade Receivables and Its Impact on Cash Holdings and Firm Value
Bibliographic record
Abstract
This paper aims to define a variable that indicates the credit quality of trade receivables and test its impact on corporate cash holdings and corporate value. Based on a sample of publicly listed firms from the United States, we provide evidence that as the credit quality of trade receivables deteriorates, firms tend to keep higher levels of cash. This finding is in line with the precautionary motive for holding cash. Additionally, when trade receivables credit quality gets worse, corporate value is reduced. In all regressions, we treat trade receivable policy variables as endogenous because of concerns regarding omitted variables bias. Moreover, we provide the first large-sample US evidence about the negative and non-linear impact of investment in trade receivables on corporate value. We utilize system GMM in all estimations. The major contribution of this study to the accounting and trade credit literature is the introduction of a variable that aims to denote the credit quality of trade receivables and the empirical evidence about the impact of deterioration in trade receivables credit quality on cash holdings and corporate value. This study also extends the literature by delivering the first large-sample evidence for the US regarding the nature of the relationship between investment in trade receivables and corporate value.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".