Efficient working capital management, bond quality rating, and debt refinancing risk
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of efficient working capital management (WCM) on a firm’s bond quality ratings (BQR) and debt refinancing risk (RFR). Design/methodology/approach To fulfill its purpose, this study adopted a co-relational research design. Additionally, the COMPUSTAT of Wharton Research Data Services was used to collect data from American production firms for a period of five years (from 2013 to 2017). Findings The results of this study suggest that efficient WCM does, in fact, play a role in improving BQR of American production firms. Furthermore, the findings go on to suggest that efficient WCM plays a very little role in reducing RFR for American production firms. Research limitations/implications This is a correlational study that investigated the presence of an association between efficient WCM and firms’ BQR and between efficient WCM and RFR. However, the two do not necessarily share a causal relationship. Moreover, the findings of this study may only be generalized to firms that are similar to those that were included in this research. Originality/value This study contributes to the literature on financial factors that improve a firm’s BQR. Firms should consider maintaining an optimal net working capital as it improves BQR. Moreover, the findings of this study may prove useful for financial managers, investors, financial management consultants and other stakeholders.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".