The classification of financial products
Bibliographic record
Abstract
In the wake of the global financial crisis, the U.S. Dodd‐ Frank Wall Street Reform and Consumer Protection Act (Dodd‐Frank) was enacted to provide increased transparency in financial markets. In response to Dodd‐Frank, a series of rules relating to swaps record keeping have been issued, and one such rule calls for the creation of a financial products classification system. The manner in which financial products are classified will have a profound effect on data integration and analysis in the financial industry. This article considers various approaches that can be taken when classifying financial products and recommends the use of facet analysis. The article argues that this type of analysis is flexible enough to accommodate multiple viewpoints and rigorous enough to facilitate inferences that are based on the hierarchical structure. Various use cases are examined that pertain to the organization of financial products. The use cases confirm the practical utility of taxonomies that are designed according to faceted principles.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".