UK corporate use of derivatives
Bibliographic record
Abstract
The recent, and ongoing, large losses on derivatives transactions announced by UK corporates and the ensuing fears for systemic risk, highlight the need for focused research on derivative practices and corporate risk management activity in particular. This paper provides a descriptive analysis of the derivative practices of UK non-financial institutions, and attempts to evaluate whether these practices are consistent with value maximizing behaviour. Starting from the basic Modigliani and Miller Capital Structure proposition, more recent academic works on the subject are reviewed and analysed in the light of market Imperfections. Unlike previously published UK based research, this study encompasses a broad spectrum of both derivative instruments and companies surveyed, and offers a wider picture of derivative activity. The paper starts with an analysis of potential benefits and pitfalls of derivative instruments in risk management. The results of a survey, sent to 629 of the 637 corporates listed in the FTSE actuaries as of 1 April 1998 are discussed and compared to existing surveys which report mainly from North American markets. As a whole, the derivatives activity of UK corporates appears to be fairly similar to that of Canadian and US firms, but is still limited in view of the potential benefits that can be derived from their use in risk management. Small firms in particular do not seem to take advantage of the products available to manage their exposure to financial price risks, and initial findings suggest that this is because of a lack of knowledge in derivatives. The results support a positive relationship between derivative usage and firm size and a strong positive correlation between interest rate derivatives usage and firm size. Results also confirm that a significant proportion of firms appear to take unnecessary chances on financial markets using derivatives, although as expected, UK corporates do not use equity derivatives. Finally, although large firms seem to have adopted fairly consistent practices towards derivatives’ risk management; smaller firms have a far less consistent approach. Indeed, a significant number of them also do not appear to report their activity to the board of directors, and/or do not have a policy covering the use of these instruments.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".