Why Are Financial Markets Regulated? An Empirical Investigation of Validity of Regulation Theories.
Bibliographic record
Abstract
According to the normative market efficiency theory of regulation (Buchanan and Tullock, 1962) popular in Finance and the mission of the Securities and Exchange Commission (SEC), the main regulatory body of financial markets in the United States, the first and foremost responsibility of regulation is to protect the interests and the rights of investors. According to Public Choice (Chicago) theory of regulation, developed by Stigler (1971) and Peltzman (1976), given that regulators in the US and other countries are appointed by and are ultimately responsible to the elected politicians, regulation is a benefit that is bought by interest groups using political contributions and votes. Following Public Choice theory, Securities and Exchange Commission's decisions should benefit suppliers of financial markets: financial services companies. Still, according to growth-smoothing theory of regulation (Aldridge, 2004), regulation benefits suppliers of the market but only in the following sense: regulation smoothes short-run growth fluctuations itself becoming a financial growth-hedging security that market suppliers (such as financial companies) buy through lobbying and votes. In this paper, I test the three theories using SEC regulatory decisions data obtained from the Federal Register database for the period of 1997-2003. I find strong empirical support for the growth-smoothing theory of regulation.
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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 | 0.042 | 0.256 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".