Measuring Riskfrom IT Initiatives UsingImplied Volatility
Bibliographic record
Abstract
We propose an underrecognized measure to capture changes in firm risk from information technology (IT) announcements: implied volatility (IV) from a firm’s exchange-traded options. An IV is obtained from a priced stock option and represents the option market’s expectation of the firm’s average stock return volatility over the remaining duration of the option. Using the change in IV around IT announcements, we can directly assess changes in IT-induced firm risk. IVs are straightforward to obtain, and are forward-looking based on option market investors’ estimates of future stock return volatility. They do not rely on historical volatility that is confounded with other events. In addition, options have different expiration dates—each with an IV—allowing us to distinguish between short- and long-term risk. We show how a change in IV can be employed to assess changes in short- and long-term firm risk from IT announcements and demonstrate this methodological innovation empirically using a set of IT announcements that have been utilized in previous studies.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".