The Impact of Working Capital Components on Firm Value in US Firms
Bibliographic record
Abstract
Working capital is an important part of any businesses day-to-day operations. However, most businesses do not take into consideration that continuous investment into working capital does not maximize firm value. The specific problem addressed was firm managers that do not understand the optimal level for each component of working capital create sub-optimal value firm; leading to diminished investment returns for shareholders. For this study, 140 firms for the years 2003-2012 were selected from a stratified random sample of firms listed on the Russell 2000 index. Accounts receivable days outstanding, accounts payable days outstanding, and inventory days outstanding were regressed on economic value to determine whether a curvilinear relationship existed. All three models showed a statistically significant relationship to firm value, F(6, 2268), p<.01, R2= .40, F(6, 2268), p<.01, R2= .38, F(6, 2268), p<.01, R2= .39. Recommendations for firm managers included lowering accounts receivable, accounts payable, and inventory days during boom economic times while increasing accounts receivable, accounts payable, and inventory days during recessionary economic times. Consideration for future research into working-capital management and firm value should consider whether different curvilinear relationships exist between firm value and working-capital components during different economic cycles.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".