Policy News and Stock Market Volatility
Bibliographic record
Abstract
We create a newspaper-based Equity Market Volatility (EMV) tracker that moves with the VIX and with the realized volatility of returns on the S&P 500.Parsing the underlying text, we find that 72 percent of EMV articles discuss the Macroeconomic Outlook, and 44 percent discuss Commodity Markets.Policy news is another major source of volatility: 35 percent of EMV articles refer to Fiscal Policy (mostly Tax Policy), 30 percent discuss Monetary Policy, 25 percent refer to one or more forms of Regulation, and 13 percent mention National Security matters.The contribution of particular policy areas fluctuates greatly over time.Trade Policy news, for example, went from a virtual nonfactor in equity market volatility to a leading source after Donald Trump's election and especially after the intensification of U.S-China trade tensions.The share of EMV articles with attention to government policy rises over time, reaching its peak in 2017-18.We validate our measurement approach in various ways.For example, tailoring our EMV tracker to news about petroleum markets yields a measure that rises and falls with the implied and realized volatility of oil prices.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".