The Effect of Cybersecurity Breaches on Analysts’ Earnings Forecasts
Bibliographic record
Abstract
We investigate the implications of cybersecurity breaches for financial analysts because they play an important role as information intermediaries in capital markets, and it is unknown whether analysts’ earnings forecasts are affected by cybersecurity breaches. Based on a sample of cybersecurity breaches from 2005 to 2018, we find that analysts’ earnings forecasts for firms with cybersecurity breaches are less accurate and more dispersed after a breach than for firms without such breaches. In cross-sectional analyses, we find that the adverse effects of cybersecurity breaches on analysts’ earnings forecasts are more pronounced for firms operating in more volatile business environments, for firms operating in industries with greater growth opportunities, and for firms with poorer internal information environments; however, these effects are attenuated when management provides more earnings guidance for the fiscal year. These results suggest that the economic and reporting complexities associated with cybersecurity breaches can hinder analysts’ ability to forecast earnings. Finally, we find some evidence that the adverse effect of cybersecurity breaches on analysts’ earnings forecasts also varies with the type and severity of breaches. Overall, our study extends the literature on the consequences of cybersecurity breaches and the factors influencing analysts’ earnings forecast properties.
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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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".