Information search volume as a predictor of information explanatory power
Bibliographic record
Abstract
Purpose – This paper aims to examine whether information search frequency of accounting information is related to the explanatory power of accounting information for firm market value. It also examines whether information content and state of nature can have an impact on this relationship. Design/methodology/approach – The paper is an empirical study using Web search volume data collected from Google Trends and financial and market data collected from Compustat. Findings – This paper finds that investors use Web search engines as an alternative way to search for information they need, search frequency of accounting information is positively related to the explanatory power of accounting information for firm market value, the relationship is found differential between statements and categories within a statement depending on the information content and the relationship is found stronger during economic upturns. Research limitations/implications – This paper examines 59 accounting items that are cross-firm commonly reported and that have data availability in Compustat. The external validity might be an issue. Practical implications – This paper is of interest to standard setters, corporate management and academics who wish to understand and improve the value of accounting information in the capital market. Originality/value – This paper is the first study which provides a comprehensive examination of the impact of investors’ information search volumes on the explanatory power of accounting information. It is also the first paper that intrudes Google Trends search volume data into accounting research.
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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 | 0.003 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".