An Empirical Analysis of Bankruptcy Risk in Oil and Gas Companies
Bibliographic record
Abstract
In this thesis, we firstly presented a deep analysis of different bankruptcy prediction models, aiming to find out which model provides more accurate predictions. Secondly, we conducted an empirical analysis to determine the main factors led to the recent crisis in oil and gas industry, using a sample of 240 oil and gas companies from U.K., U.S. and Canada markets. \nWe sought to develop a comprehensive model to forecast default probability, and we are particularly interested in incorporating the financial, macroeconomic, risk management and corporate governance variables into the same model in order to find out all possible internal and external factors that causing corporate bankruptcy. To the best of our knowledge, this is the first study to account for study to account for risk management and corporate governance bankruptcy prediction literature. \nAs a result of our research we found that the profitability, leverage, liquidity, firm size, risk management, corporate governance and macroeconomic changes are all significant determinants of bankruptcy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".