Does Information Asymmetry Matter to Equity Pricing? Evidence from Firms’ Geographic Location*
Bibliographic record
Abstract
Abstract The scarcity of suitable proxies for asymmetric information has impeded empirical research from providing reliable evidence on whether information risk shapes equity pricing. In reexamining this unresolved question, we rely on firms’ geographic distance from financial centers to gauge information asymmetry. We provide strong, robust evidence supporting the prediction that equity financing is cheaper for firms nearer central locations, implying that investors rationally require more compensation when information asymmetry is worse. The equity pricing role of geographic proximity is economically large, with our coefficient estimates translating into firms located within 100 kilometers of the city center of the nearest of six major financial centers, or in their metropolitan statistical areas, enjoying equity financing costs that are seven basis points lower. Our inferences are insensitive to measuring both the cost of equity capital and distance in several ways, controlling for corporate governance quality, and addressing endogeneity. Collectively, our analysis suggests that investors discount the price that they pay for their securities to reflect the greater information asymmetry that ensues when firms are far from major financial centers.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".