Bibliographic record
Abstract
This paper examines if incorporating changes in financial market risk perception improves the predictive power of an early-warning system for systemic banking crises. In explaining systemic banking crises, the existing literature identifies inflating stock and real estate bubbles, credit booms, and surges in net capital inflows as the common drivers. Employing panel logit models to predict the postwar systemic banking crises in advanced economies to occur within three–four years, the paper’s key finding is that, even after controlling for the effects of surges in asset and credit markets and net capital inflows that are above the long-run trends for an extended period, market participants’ increasing underestimation of downside risks is a significant predictor of these crises. Incorporating changes in risk perception improves the prediction accuracy of the model significantly. This finding is robust across alternative prediction horizons, systemic crisis definitions, and risk-perception measures. Consistent with the recent theoretical developments in the form of the diagnostic expectations hypothesis for financial markets, the interpretation is that recent recurring good news about financial markets and the broader economic trends for sufficiently long periods lead to growing neglect of tail risks and riskier financial transactions, raising systemic risk and the likelihood of a financial crisis. The finding suggests monitoring financial market risk perception, in addition to the conventional indicators, to predict and avert systemic banking crises.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".