State of Information Technology Support for Traders in Fixed Income Markets
Bibliographic record
Abstract
A fixed-income security is defined as one whose income stream is fixed for the duration of the loan and where the maturity and face value are known. It is estimated that the global fixed-income market is about $40 trillion with the US having the lion’s share of $19 trillion. There were at least 74 trading platforms in North America and Europe in 2004. However, it is estimated that only about five percent of fixed-income trade is performed through electronic transaction systems. This is very low when compared with use of information systems in support of equity trade. Our research is guided by the following central question: What are the implications of using IT to mediate electronic brokerage relationships that are enacted through the work practices and interactions of actors representing buyers and sellers in financial institutions within the context of fixed income market. This paper, based on interviews with the senior managements and traders of 10 major financial institutions, provides an overview of information system support for traders in fixed-income trade markets.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 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".