How do Firms Hedge in Financial Distress? - Classification of hedging strategies in the US oil industry 2013-2015
Bibliographic record
Abstract
We examine how firms hedge in financial distress. Using hand-collected data from oil and gas producers, we find that derivative portfolios in these firms are characterized by short put options. These positions are part of a composite three-way collar strategy that combines buying put options and selling put and call options with differing strike prices. We show that because liquidity demand varies with the degree of financial distress, the three-way collar strategy is the optimal risk management strategy that preserves incentives for future growth. The sample consists of publicly traded oil and gas producers in the US (SIC code 1311) between Q1:2013 and Q4:2015. Hedging strategies are hand-coded based on quarterly reports (10Q/10-Q reports). We sum each firm's outstanding derivatives positions regardless of maturity for each quarter and create a variable per hedging strategy that takes the value 1 if the sum is positive, zero otherwise. We classify individual firms’ hedge portfolios into five distinct hedging strategies based on the character of the provided protection and the cash flow impact. The dataset contains the classifiers for these five hedging strategies and is identified by quarter and global company key (GVKEY). The dataset contains quarterly classification of US oil companies' hedging strategies over the period 2013-2015. The strategies are classified based on reporting in each company's quarterly report. Five strategies are identified (described in the data file). Companies are identified by Global Company Key. The Global Company Key or GVKEY is a unique six-digit number key assigned to each company in the Capital IQ Compustat database
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".