Catering to Investors Through Security Design: Headline Rate and Complexity*
Bibliographic record
Abstract
Abstract This study investigates how banks design financial products to cater to yield-seeking investors. We focus on a large market of investment products targeted exclusively at households: retail structured products. These products typically offer a high return under their best-case scenario—the headline rate—that is nested in a complex payoff formula. Using a text analysis of the payoff formulas of the 55,000 products issued in Europe from 2002 to 2010, we measure product headline rates, complexity, and risk. Over this period, product headline rates depart from the prevailing interest rates as the latter decrease, complexity increases, and risky products become more common. In the cross section, the headline rate of a product is positively correlated with its level of complexity and risk. Higher headline rate, more complex, and riskier products appear more profitable to the banks distributing them. Our results suggest that financial complexity is a by-product of banks catering to yield-seeking investors.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".