Text Mining and Machine Learning on 10-K Risk Factors and Net Income: Evidence from Apple
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
<p>This study investigates whether risk factor disclosures in 10-K annual filings contain predictive signals about firms’ future financial performance. Both structured (financial statements) and unstructured (risk factor narratives) data were analyzed using text mining and machine learning techniques. The JCAATs XBRL Connector and AI audit functions were employed to streamline data extraction, sentiment analysis, clustering, machine learning modeling, and SHAP-based interpretability. Sentiment scores and textual clusters were constructed as independent variables to explain subsequent- year net income. Empirical findings demonstrate that both sentiment and textual style are significant predictors of net income, supporting the view that risk disclosures provide forward-looking information. These findings extend language signal theory to the risk factor section and underscore the practical value of JCAATs in auditing and regulatory monitoring, highlighting how AI-driven text analytics can enhance disclosure assessment and financial supervision.</p>
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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