The Impact of Expertise and Investment Familiarity on Investors' Use of Online Financial Report Information
Bibliographic record
Abstract
In this study we use a unique dataset to examine whether professional and nonprofessional investors use different online quarterly financial information when making investment decisions, and whether the online information they use depends on whether they are researching a new investment or evaluating a current investment. Our results suggest that professional investors prefer to view PDF-formatted quarterly reports and tend to rely directly on the financial statements compared with nonprofessional investors who prefer to view HTML-formatted reports and have a tendency to rely more on management's discussion of the quarter's results. Our results also suggest that, for nonprofessional investors, investment familiarity (i.e., whether they are evaluating a current investment or researching a new investment) strongly affects the type of financial information they view within a firm's quarterly reports. Our results have implications for the design of experimental studies, and provide information useful to managers, financial report users, standard setters, and researchers as they attempt to better understand the types of information that professional and nonprofessional investors use when making investment decisions.
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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 | 0.006 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".