Managerial Self-Attribution Bias and Banks’ Future Performance: Evidence from Emerging Economies
Bibliographic record
Abstract
The objective of the study was to predict the future performance of banks based on the contextual information provided in annual reports. The European Central Bank has observed that performance prediction models in earlier studies mainly rely on quantitative financial data, which are insufficient for the comprehensive assessment of banks’ performance. There is a need to incorporate the qualitative information along with numerical data for better prediction. In this context, this study employed the attribution theory for understanding the contextual information of behavioral biases of management towards the expected outcomes. The sample consisted of 58 banks of 16 emerging economies, and the period covered from 2007–2015. Unsupervised hierarchical clustering was performed to identify the latent groups of banks within the data. For performance prediction, system GMM was employed, because it helped to deal with the endogeneity and heterogeneity problems. The results of the study were consistent with the attribution theory that management took credit for favorable expected outcomes and distanced from bad outcomes. An important policy implication of the study is that the prevalence of self-attribution bias of management in annual reports provides an additional source of information for the regulators to identify the banks at risks and take preventive measures to avoid the expected cost of failure. It can also help investors, and gives analysts a better tool for a comprehensive analysis of the profitability of prospective investments.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".