Does Long-term Investment Really Pay Off? Evidence from Listed Mining Firms in Canada
Bibliographic record
Abstract
Conventional wisdom often advises that long-term investors enjoy positively abnormal returns ( Daniel et al. 1998 , Eberhart et al. 2004 ). IPOs are important ingredients of the stock markets. On the one hand, getting listed allow the firms to access a large pool of capital for future expansion. On the other, getting listed allow entrepreneurs and venture capitals (VCs) to withdraw, at least partially, from their prior investments with positive returns. The public investors can then share the firm&s;s growth benefits with various other stakeholders. Interestingly, declining post-listing performance seems increasingly common in recent decades ( Kooli and Suret 2004 ). Some studies have documented the declining performance of certain stocks in their post-listing period as investor under- or over-reaction towards firm behaviour ( Ritter 1991 . Aggarwal et al. 1993 , Loughran and Ritter 1995 ). In other words, firm post-listing performance may also bring negative returns to stock market investors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".