The Eyeballs Have It: Searching for the Value in Internet Stocks
Bibliographic record
Abstract
In this paper we provide insights into the manner in which (relatively sparse) accounting information, along with measures of internet usage, are employed by the market in the valuation of internet firms. Consistent with those who claim that financial statement information is of very limited use in the valuation of internet stocks, we are unable to detect a significant positive association between bottom-line net income and our sample firms' market prices; in fact, the association is actually negative. However, when we decompose net income into its components, we find gross profits to be positively and significantly associated with prices. In addition, both unique visitors and pageviews, as measures of internet usage, are found in most instances to provide incremental explanatory power (in some cases considerable) for stock prices. We also separately analyze the e-tailers, and the portal and content/community firms (the p/c firms) in our sample. For the e-tailers we find that bottom-line net income generally has a negative association with stock prices (as for the sample as a whole), while a positive and significant association exists for the p/c firms. In this respect, p/c firms' shares behave more like those of non-internet companies. Further, we find for the p/c firms that the incremental explanatory power of pageviews and of unique visitors is approximately the same; in contrast, pageviews has much greater incremental explanatory power for the e-tailers than does unique visitors. This suggests that pages viewed per visitor is an especially important metric for the e-tailers, as compared to the p/c firms.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".