Bibliographic record
Abstract
We study the patterns of stock returns around the Federal Reserve monetary policy announcements. Much of the existing literature interprets changes in short rates around the announcement windows as policy surprises. In contrast, we follow the “Fed information effect” literature, which posits that financial markets react to central bank announcements not just for unexpected changes in monetary policy stances (monetary policy news), but also for central bank’s assessment of economic conditions (non-monetary policy news). We identify the good/bad news using a combination of sign restrictions with high-frequency financial data. “Bad news” events are times when the market interpreted the Fed decisions/announcements as revealing negative Fed information about the economy, and vice versa for “good news” events. A novel finding is that following bad news events, we observe significantly positive stock returns in a 20-day period. This observation is largely consistent with a story of asymmetric effects of good and bad news on the level of uncertainty. Further analysis shows that the post-FOMC drift to economic news in Fed announcements is a market-wide phenomenon. A trading strategy that buys following “bad news” earns an excess return of 2.5% per year with a Sharpe ratio of 0.43.
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.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".