Did Politicians Use Non-Public Macroeconomic Information in Their Stock Trades? Evidence from the STOCK Act of 2012
Bibliographic record
Abstract
Existing research shows that members of Congress made informed trades prior to the passage of the STOCK Act of 2012. There is also evidence in the literature to suggest that the STOCK Act was able to deter politicians from trading based on non-public information. However, the question of whether politicians made informed trades at the market level (using non-public macroeconomic information, not just firm-specific information) in the first place and whether they continued to do so even after the passage of the STOCK Act remains unexamined. We analyze 101,191 individual stock transactions covering the 2004–2014 period and find that the STOCK Act adversely affected the ability of politicians’ aggregated stock trades to predict the stock market returns. Our results imply that politicians used non-public macroeconomic information prior to the STOCK Act, and this legislation was influential in deterring politicians from using non-public macroeconomic information in their stock trades. Our findings also provide input on the current debate on the need for the STOCK Act 2.0.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".