The rise of shadow banking and the hidden benefits of diversification
Bibliographic record
Abstract
The diversification benefits associated with banks off-balance-sheet activities (OBS), and particularly non- traditional activities, is a question much debated in the literature. These activities, related to the emergence of shadow banking, greatly contribute to the volatility of bank operating revenues, but their impact on accounting returns is less clear (Stiroh and Rumble 2006). In this paper, we use a Canadian dataset to revisit the risk-return trade-off associated with banks OBS activities and study the evolution of the endo-geneity of banks decision to expand their market-oriented business lines. Consistent with the changing mix of noninterest income OBS activities generate, we identify a structural break in 1997 which coincides with an increased impact of endogeneity on banks returns, and which also leads to an increased return on assets (ROA) and a surge in banking risk. We trace the sources of the greater volatility of noninterest income to a tighter cointegrating relationship between noninterest income and stock market indices after 1997. Intro-ducing a new, robust estimation method based on a modification of the Hausman procedure, we find that neglecting endogeneity greatly underestimates the positive impact of shadow banking on bank accounting returns, even when the subprime crisis is considered. Our main results suggest that the influence of market-based activities on the risk-return trade-off might be larger than what was previously thought.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".