Information Disclosure in Financial Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Information disclosure is an essential component of regulation in financial markets. In this article, we provide a cohesive analytical framework to review certain key channels through which disclosure in financial markets affects market quality, information production, efficiency of real investment decisions, and traders’ welfare. We use our framework to address four main aspects. First, we demonstrate the conventional wisdom that disclosure improves market quality in an economy with exogenous information. Second, we illustrate that disclosure can crowd out the production of private information and that its overall market-quality implications are subtle and depend on the specification of information-acquisition technology. Third, we review how disclosure affects the efficiency of real investment decisions when financial markets are not just a side show, as real decision makers can learn information from them to guide their decisions. Last, we discuss how disclosure in financial markets affects investors’ welfare through changing trading opportunities and through beauty-contest motives. Overall, our review suggests that information disclosure is an important factor for understanding the functioning of financial markets and that there are several trade-offs that should be considered in determining its optimal level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it