Using internet search data to predict aggregate retail sales and enhance firm‐level revenue expectations
Bibliographic record
Abstract
Abstract This study examines whether a simple measure of internet search intensity for publicly traded retail firms can enhance the capital market's firm‐level revenue expectations and provide insights into economy‐wide retail sales. At the firm level, the search index is predictive of analyst nowcast and forecast errors after controlling for past sales, deferred revenue, firm characteristics, and firm and time fixed effects. An implementable trading strategy generates abnormal returns of roughly 2% to 3% from the fiscal quarter end through the earnings announcement, well above transaction costs. We also find that approximately two‐thirds of the abnormal returns occur around earnings announcements, with an even greater fraction for firms with coarser information environments. At the macro level, we find that the permanent, seasonal, and transitory components of our search intensity index align with those of the Census Bureau's retail sales data and US real gross domestic product, suggesting our measure is a leading indicator of personal consumption expenditures, a key driver of aggregate output. The aggregated search index nowcasts aggregated publicly traded retail firm sales both within and out‐of‐sample after controlling for past sales.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".