Bank Transparency and Risk Taking: Empirical Evidence from Tunisia
Bibliographic record
Abstract
An important unresolved issue in finance is the extent to which bank transparency promotes or undermines banking risk-taking. Financial accounting information is an essential component of transparency and a necessary condition for market discipline. This latter can be conceptualized as a market-based incentive scheme with which investors in banking securities penalize banks for greater risk-taking by asking for higher returns on their investments. However, in developing countries, where financial markets are insufficiently developed, the role of market discipline in limiting banks’ risk-taking may be restricted. This paper examines the impact of transparency, as measured by voluntary disclosure of financial information, on the fragility of Tunisian banks. This study is motivated by the decision of the Central Bank of Tunisia to implement the directives of the second Basel Accord to improve the soundness and the safety of the Tunisian banking system. We examine a sample of ten Tunisian banks listed on the Stock Exchange of Tunis over the period 2000-2011. The results show that transparency has no effect on Tunisian banks’ risk-taking. Similarly, the results indicate that the capital adequacy ratio has no effect on the non-performing loans rate. These results may undermine the effectiveness of the guidelines of the Basel Committee agreements to reduce risk-taking by Tunisian banks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".