News, intermediation eciency and expectations-driven boom-bust cycles
Bibliographic record
Abstract
The years leading up to the great recession were a time of rapid innovation in the financial industry. This period also saw a fall in interest rates, and a boom in liquidity that accompanied the boom in real activity, especially investment. In this paper we argue that these were not unrelated phenomena. The adoption of new financial products and practices led to a fall in the expected costs of intermediation which in turn engendered the flood of liquidity in the financial sector, lowered interest rate spreads and facilitated the boom in economic activity. When the events of 2007-2009 led to a re-evaluation of the effectiveness of these new products, agents revised their expectations regarding the actual efficiency gains available to the financial sector and this led to a withdrawal of liquidity from the financial system, a reversal in interest rates and a bust in real activity. We treat the efficiency of the financial sector as an exogenous process and study the impact of news shocks regarding this process. Following the expectations driven business cycle literature, we model the boom and bust cycle in terms of an expected future efficiency gain which is eventually not realized. The build up in liquidity and economic activity in expectation of these efficiency gains is then abruptly reversed when agent's hopes are dashed. The model generates counter-cyclical movements in the spread between lending rates and the risk-free rate which are driven purely by expectations, even in the absence of any exogenous movement in intermediation costs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".