Preventing a Repeat of the Money Market Meltdown of the Early 1930s
Bibliographic record
Abstract
This paper analyzes the meltdown of the commercial paper market during the Great Depression, and relates those findings to the recent financial crisis.Theoretical models of financial frictions and information problems imply that lenders will make fewer noncollateralized loans or investments and relatively more extensions of collateralized finance in times of high risk premiums.This study investigates the relevance of such theories to the Great Depression by analyzing whether the increased use of a collateralized form of business lending (bankers acceptances) relative to that of non-collateralized commercial paper can be econometrically attributable to measures of corporate credit/financial risk premiums.Because commercial paper and bankers acceptances are short-lived, they are more timely measures of the availability of short-term credit than are bank or business failures and the level or growth rate of the stock of bank loans, whose maturities were often longer and were renegotiable.In this way, the study adds to the literature on financial market frictions during the Great Depression, which aside from analyzing securities prices, typically investigates the behavior of credit-related variables that lag current conditions, such as bank failures, bankruptcies, the stock of money, or outstanding bank loans.In particular, the real level of bankers acceptances and their use relative to noncollateralized commercial paper were strongly and positively related to spreads between corporate and treasury bond yields.Also significant were short-run events, such as the October 1929 stock market crash and the 1933 bank holiday episode that sparked flights to quality in the bond market and a flight to collateral (BAs) in the money market and perhaps away from the loan market.Furthermore, these shifts in the composition of external finance were large, supporting the view that financial frictions and reduced credit availability may have played an important role in depressing the U.S. economy during the 1930s.The paper also relates these findings to the current financial crisis by examining how the relative use of commercial paper reacted to yield spreads during the current crisis, taking into account Federal Reserve actions to improve liquidity conditions in the money markets.Results suggest that these efforts may have, at least so far, helped prevent the commercial paper market from melting down to the extent seen during the early 1930s.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".