Information advantages, excessive risk-taking, and capital regulation for BigTechs in financial intermediation
Bibliographic record
Abstract
This paper establishes theoretical models to study capital regulation of BigTech firms (BigTechs hereafter) providing financial intermediation services. In our models, BigTechs borrow debts to invest between socially efficient prudent assets and socially inefficient risky assets. Limited liabilities imply that with a higher capital adequacy ratio , BigTechs are more risk averse and favor the prudent asset more. Then we examine how better information of BigTechs will affect BigTechs' incentive for excessive risk-taking, welfare, and capital regulation. The major results produced by our models are as follows: (1) Better information of BigTechs does not eliminate and could in some circumstances exacerbate their excessive risk-taking behavior. (2) Better information of BigTechs does not necessarily improve welfare. BigTechs could employ better information to more precisely identify the socially inefficient risky asset to invest in, causing more severe resource misallocation. (3) Capital regulation is an effective tool to curb BigTechs' excessive risk-taking and to ensure that better information of BigTechs will improve welfare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 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".