An Assessment of the Benefits of Optimizing Working Capital and Profitability: Perspectives from DJIA30 and NASDAQ100
Bibliographic record
Abstract
The objective of this paper goes beyond the boundaries of an exploratory analysis to operationalize the association between corporate working capital and return on assets. This paper optimizes the impact of the Cash Conversion Cycle (CCC) on Return on Assets (ROA). The paper develops a mathematical formulation that connects the components of CCC to ROA. The sample includes the non-financial firms listed in DJIA30 and NASDAQ100. The data covers the quarterly periods from June 1992 to March 2018. The paper uses standard statistical tests including linearity (RESET), the Hausman test for fixed and random effects, and the Breusch–Pagan/Cook–Weisberg test for heteroskedasticity. The estimation is carried out using the GLS estimator. This study finds: (a) the optimal, rather than observed, components of CCC are robust and coherent, (b) if firms were to optimize the components of CCC, the ROA improves significantly, (c) the positive estimates of size show that the components of CCC help firms grow, (d) the effects of either observed or optimal CCC on ROA are reached in the short term (four quarters), (e) the results show that observed as well as optimal CCC are able to detect the structural break in the 2008 financial crisis, and (f) the results of a logit analysis show that the optimization algorithm results in significant increases in ROA that are associated with increases in degree of financial leverage and decreases in short-term debt ratio. This paper contributes to the related literature in two ways. First, the paper develops a mathematical structure that associates corporate CCC and ROA in a way that offers a guide to corporate financial managers regarding structural management of corporate CCC. Second, the paper examines the impacts of optimized CCC on ROA.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".