Bibliographic record
Abstract
As the banking industry makes its way through the economic downturn, it's useful to put the situation in perspective by comparing industry performance in the current recession to that of the banking problems in the late 1980s to early 1990s. At present, the situation looks to be relatively better. Further examination reveals a strong foundation compared to the previous period. [ILLUSTRATION OMITTED] Higher provisioning, higher reserves Despite Federal Reserve Chairman Ben Bernanke's recent statement that the recession has likely ended, many banks are expecting more delinquencies. In second quarter of 2009, 4.4% of industry loans were 90 days past due or in nonaccrual status. Although on par with the peak levels reached in 1987 and 1990, the trend line today has yet to show signs of peaking. It follows, then, that banks have had to provision more against loan losses. During the current cycle, the ratio of two-year average provisions to net charge-off reached 169% as of June 2009, compared to 150% as of December 1990. Loss provisions to net operating revenue also show the banking industry provisioning a higher percentage of dollars in this down-cycle than in the past. In the last two years, the industry provisioned on average 28% of net operating revenue compared to the average two-year provisioning in 1990 of 20%. Due to the aggressive buffering, the industry is now better reserved than it has been in the last 30 years. As the industry grew during the strong economic period of the middle 2000s, reserve ratios generally fell. However, this was due to lending portfolios expanding to keep up with demand. Now, banks are reserving more than in the past--up to 2.8% of their portfolios. The unloading of bad loans and tightening of lending standards have also contributed to the rise in reserve ratios. Stronger capital One of the other most noticeable differences between this period and the last is the higher levels of capital held by banks. As the table below demonstrates, the industry increased its holdings in all categories of capital. Moreover, there is broad evidence that banks of all asset sizes generally hold more capital now as compared to the earlier year. This stronger capital base, coupled with greater reserves, has allowed banks to better absorb the shock of the current financial crisis. This goes a long way toward explaining why bank failures, while continuing, are way down in number as compared to the earlier period. Industry profitability The strong provisioning for losses has taken its toll on bank income. Over a three-year period ending in the second quarter 2009, the percentage of banks that had negative net income rose from 7% to over 25%. In comparison to the late 1980s to early 1990s, the level of non-profitable banks is higher and ramped up more quickly than in the previous cycle. As a result, industry return-on-assets has fallen to lows last seen in the 1980s--after nearly two decades of holding over one percent. …
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".