Why We Should Stop Being Surprised that Lightly Regulated Markets Fall Short of the <scp>SEC</scp>'s Goals for Market Quality: A Discussion of “Private Intermediary Innovation and Market Liquidity”
Bibliographic record
Abstract
Abstract The stated goals of the SEC are to protect investors, maintain orderly markets and facilitate capital formation. These goals can be achieved with very light regulation if, as assumed by traditional economic theory, investors process information costlessly and protect themselves from informational disadvantages, and firms optimally balance the costs and benefits of committing to make their reports reliable. A growing body of research demonstrates that light regulation fails to achieve the SEC's goals, because investors find information processing costly and fail to protect themselves. After reviewing theory and prior evidence, I discuss new lessons learned from Jiang, Petroni, and Wang ( ), who show that Pink Sheets® reduced the liquidity of firms with low reporting quality and increased the liquidity of firms with high reporting quality, merely by highlighting the quality of their listed firms’ disclosure. While the Pink Sheets® innovation might have occurred through many causal channels, all of them entail a violation of costless processing and self‐protection, and lead to the conclusion that this lightly regulated market did not initially meet the stated goals of the SEC. I conclude by arguing that markets can achieve the SEC's goals only if they exhibit a particularly strong version of “dynamic” market efficiency, which requires that each individual trade on the path to even incomplete revelation occurs at the then‐optimal price. Because dynamic efficiency is unlikely, we should stop being surprised to see evidence that lightly regulated markets fall short on key dimensions. Instead, we should use our well‐developed understanding of market inefficiency to guide 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".