Survival Analysis of Canadian Oil and Gas Firms
Bibliographic record
Abstract
In the context of oil price fluctuations inducing boom and bust cycles, this empirical study is a survival analysis of the Canadian oil and gas exploration and production (E&P) industry. The population includes 540 public Canadian E&P firms that have their headquarters and production activity in Canada, and the data covers the periods of Q1-2002 to Q1-2016 representing over 15,850 firm-quarter observations. The method is an extended Cox model with repeating events, allowing for the use of time-varying predictor variables and the analysis is executed in R, a free statistical software. The study introduces a new definition of financial distress as two consecutive quarters of negative operating cash flow to total assets ratio, develops a baseline model with financial ratios and industry-specific covariates and tests three hypotheses. The first two hypotheses examine the extent to which hedging and company size respectively correlate to the state of financial distress, and the third hypothesis explores how being financially distressed contributes to being a target in a merger and acquisitions (M&A) transaction. The findings show that a hedging firm is 18.5 times less exposed to the hazard of financial distress than a non-hedging firm; and with each unit size increase, a firm is 1.18 times less likely to experience financial distress, but financial distress is not a valid predictor of the hazard of being an M&A target. This study provides a new perspective supporting the use of hedging and size increase for increasing corporate resilience in Canadian oil and gas firms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".