Intra-Industry Effects of Bank Privatization: A Clinical Analysis of the Privatization of the Commonwealth Bank of Australia
Bibliographic record
Abstract
This paper provides a comprehensive analysis of the effects of the privatization of the Commonwealth Bank of Australia (CBA) on the Bank's performance and that of the rival banks. First, we find that the major rival banks reacted negatively to the privatization announcements although the initial (partial privatization) and the final (full) privatization announcements elicited stronger stock market reaction from the rival banks. Second, we find that the CBA's long-term stock market performance improved markedly as the proportion of government ownership decreased, with the Bank's cumulative abnormal returns being 50% more than those of its rivals three years after the Bank had been fully privatized. Also, the CBA has not only been very efficient in reducing cost and improving its profitability in the post-privatiztion period, it has outperformed its rivals on almost all the operating performance measures and has become the most profitable bank in Australia. A particularly noteworthy finding is that the improvement in the CBA's operating and stock market performance and the rival banks' reaction to the partial and full privatization announcements were strongest after the Bank had been fully privatized. The implication of the results for governments contemplating privatization of state-owned enterprises is that full privatization is necessary in order to achieve strong gains in efficiency, profitability and stock market performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".