Content of Annual Reports as a Predictor for Long Term Stock Price Movements.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
1 This paper examines the possibility of automatic extraction of future stock price information from the annual Form 10-K produced by publicly traded companies in the United States of America. While previous approaches to automatically interpreting corporate documents have tended to utilize extensive expert knowledge to preprocess and analyze documents, our approach inputs documents verbatim to a compression classifier. We demonstrate the effectiveness of the new approach on a newly constructed dataset based around the Dow Jones Industrial Average over the period 1994-2009. We find statistically significant increase in average returns of stocks recommended by the new system as compared with the Dow as a whole. Also examined are two hypotheses regarding the predictive power of 10-K reports. First, whether congressional attempts to make Form 10-K filings more informative had a measurable impact, and second, whether the filings have long-term predictive value in a dynamically changing market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it