Sell‐offs, internal capital markets, and long run performance: Canadian evidence
Bibliographic record
Abstract
Purpose The paper's aim is to analyze excess returns generated by Canadian sell‐offs and their links to changes in firms' internal capital allocation efficiency to test the efficiency of internal capital markets after assets divestitures. Design/methodology/approach This study investigates the relationship between the level of the excess returns subsequent to sell‐offs and changes in the capital allocated through internal capital markets. The authors measure excess returns by calculating buy‐and‐hold abnormal (BHAR) returns up to three years after divestitures and test whether changes in value are related to changes in investment efficiency. The paper uses the relative value added by allocation (RVA) as developed by Rajan et al. to measure the variation in allocational efficiency of the internal capital market. Findings The study reveals that on average assets divestitures enable Canadian firms to keep up with the performance of their peers of the same industrial sector during the long‐run post divestiture period. A closer look at the results shows that the variation of long‐run post divestitures performance among Canadian firms is significantly and positively linked to changes in the allocational efficiency of the internal capital markets. These results suggest that dismantling some parts of the internal capital market does lead to improvements in firm value in the long run. Research limitations/implications The sample is limited to a group of firms that sell off a portion of their assets. Further research could be conducted to determine whether other divestiture methods (spin‐off, sell‐off or equity carve‐out) have any impact on internal capital allocation efficiency and long run financial performance. Originality/Value The paper adds to other studies examining the source of gains from divestitures by documenting the effects of changes in internal capital allocation efficiency on the creation of long‐term shareholder wealth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".