Use of Derivative and Firm Performance: Evidence from the Chinese Shenzhen Stock Exchange
Bibliographic record
Abstract
Financial derivatives have been increasingly used by firms to hedge against financial risks. However, it is still not clear what factors at the firm level lead to firms’ derivative use and whether derivative use can generate performance improvement, especially in the context of firms operating in emerging economies. Using the unbalanced panel data consisting of 2529 listed firms from China covering an 11-year period from 2005 to 2015, this study examines these two questions regarding firms’ use of financial derivatives. Based on results from the empirical analysis, this study identified operational cash flow, tax shield, R&D investment, and the possibility of bankruptcy, as the firm-level factors that enable firms’ decision to invest in financial derivatives. More importantly, empirical findings from this study suggest that a firm’s derivative use tends to negatively affect firm performance, rather than improve firm performance. The negative effect of derivative use on firm performance is not consistent between the two groups of the better performer and poorer performer firms. While the poorly performed firms are more likely to use financial derivatives for the purpose of performance improvement, their derivative use tends to further damage, rather than improve, performance. These research findings have theoretical and practical implications.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".