Use of Derivatives and Market Valuation of the Banking Sector: Evidence from the European Union
Bibliographic record
Abstract
(1) Background: This paper aims to investigate whether the derivatives usage by the banking sector in the European Union has impacted its market valuation in the aftermath of the financial crisis. (2) Methods: Our analysis takes 120 European financial institutions listed on the European Union stock exchange over a period of 14 years into account (2008–2021). We use the generalized method of moments (GMM) to assess whether the use of derivatives allows financial intermediaries to increase their market value. Control variables, such as size, profitability, expectations of the market, bank risk, liquidity performance, and financial condition, are also taken into consideration. (3) Results: Our main findings suggest that market value is affected negatively by derivative asset accumulation. (4) Conclusions: The results are in line with the studies that investigated the impact of financial derivatives on the market value and found a negative connection between the two, justified by the suboptimal hedging or the higher volatility of the earnings.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".