Connecting the Dots: Helping Investors Use Risk Disclosures When Evaluating Financial Statements
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
SYNOPSIS The SEC recently adopted a requirement for firms to provide a two-page summary of risk factor disclosures, intending to improve decision-usefulness for investors. This study evaluates the practical effectiveness of this requirement and an alternative proposal to enhance investors’ understanding: hyperlinking risk factor disclosures directly to related financial statement line items. Using experimental evidence from retail investors, we find that the SEC’s summarization requirement does not significantly influence investor judgments. However, our proposed hyperlinking approach significantly increases investor assessments of risk where appropriate, seemingly by improving investor acquisition and integration of relevant information in the risk disclosures. These findings question the efficacy of the SEC’s current summarization requirement as well as offer practitioners and policymakers a potential improvement for presenting risk disclosures. Implementing hyperlinks could enhance transparency and investor decision-making, aligning risk factor disclosures more closely with investors’ informational needs. Data Availability: Contact the authors. JEL Classifications: D81; G11; G41; M41.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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