The inverse hockey stick effect: an empirical investigation of the fiscal calendar’s impact on firm inventories
Bibliographic record
Abstract
We empirically investigate how manufacturers’ inventory decisions relate to the fiscal calendar. Although optimal firm inventories should depend on demand and supply, we find that the artificial accounting construct of the fiscal year frequently drives inventory dynamics. In an effort to manage earnings and cash flows (CFs) towards the fiscal year-end (FYE), firms significantly reduce their inventories in the fourth fiscal quarter only to increase their inventories in the next fiscal year. Using a sample of 4877 US manufacturing firms for the period 1990–2010, we find that inventories are 3.9–6.0% lower on average in the fourth fiscal quarter. In the analysis, we control for inventory theory-related factors that have been identified in prior literature. Because this pattern is the inverse of that observed for sales, we refer to this phenomenon as the inverse hockey stick effect. The effect holds for all three individual inventory types: raw materials, work in progress and finished goods. We find that inventory reductions in the fourth fiscal quarter are particularly substantial if firms have an incentive to beat CF targets. In contrast to our expectations, we do not find evidence that financial distress links to inventory reductions at the FYE.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".