Use it or lose it: fiscal year-end corporate investment around the world
Bibliographic record
Abstract
Purpose It is documented that companies and government agencies in the USA invest more in the fourth fiscal quarter without having higher investment opportunities. While previous studies focus on the agency conflicts and information asymmetry within organizations, this study is motivated by Scharfstein and Stein's (2000) two-tiered agency model and aims to examine how firms' external business environment affects the “fourth quarter effect.” Design/methodology/approach The authors implement this study in a sample of 41 countries and observe similar seasonality in firm investment as documented in the US market. Findings More importantly, using country characteristics, this study finds that firms from countries with better investor rights and protection, and more developed financial markets show less severe over-investment in the fourth fiscal quarter. Originality/value This paper contributes to the literature of law and finance, and the internal capital market, by investigating the quarterly investment patterns of firms from 41 countries. The authors find that similar to the results in earlier studies on the US market, firms in the global market increase their capital expenditure in the fourth fiscal quarter, indicating that the internal agency conflicts between the headquarters and divisional managers are widespread across the world. The authors also find that firms that operate in countries with higher investor rights and protection, and more developed financial markets, tend to show less severe “fourth quarter effect”.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".