The Relevance of Accounting Information in a Stock Market Bubble: Evidence from Internet IPOs
Bibliographic record
Abstract
Abstract: Prior research shows that accounting information is relevant for stock valuation, failure prediction, performance evaluation, optimal contracting, and other decision‐making contexts in relatively stable market settings. By contrast, accounting's role during stock market bubbles such as those involving a revolutionary emerging technology is the subject of considerable debate, and prominent market observers have alleged that outdated and flawed accounting practices contributed to the crash of the Internet‐led high‐tech bubble of the late 1990s. We address the issue of whether accounting data is informative in a stock market bubble by examining its failure prediction ability in the context of Internet IPOs, one of the most egregious and economically significant sectors of the high tech bubble. Our setting of young start‐up firms is one in which there is relatively little room for managerial discretion with respect to accounting accruals; Internet firms' accounting earnings closely approximate operating cash flows. Yet in contrast to widespread criticisms of accounting and its alleged role in fueling the bubble, we find that accounting variables are highly informative for failure prediction; specifically, they are significant in explainingex post realized Internet IPO failures. Using an existing IPO failure prediction methodology and two alternative definitions of innovative IPOs, we further show thatex ante, out‐of‐sample Internet IPO failure forecasts are associated with economically and statistically significant hedge returns. Our analyses suggest that the traditional financial reporting system could serve as an anchor during speculative bubbles.
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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 | 0.028 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".