Impacts of COVID-19 and contractual changes on the financial performance of lumber futures
Bibliographic record
Abstract
The United States lumber futures market has followed a volatile path since 2017. Two key events in this market are the COVID-19 pandemic and the release of newly revised lumber futures contracts in August 2022. This study aims to examine how these two events affect the financial performance of lumber futures. We employ the event study methodology and the generalized autoregressive conditional heteroskedasticity (GARCH) model to explore the market’s reaction. A market model that includes supply and demand factors, along with the GARCH effect, is used to calculate abnormal returns and their associated volatility. A standardized trading volume ratio is also constructed to assess volume effects. The results reveal different patterns of abnormal returns over the event windows: COVID-19 caused significant daily abnormal returns and cumulative negative effects, while new contracts generated positive abnormal returns that declined over time. Second, contractual changes induced immediate abnormal return volatility, whereas COVID-19 led to sustained volatility across longer windows. Lastly, COVID-19 caused below-normal trading volume effects, while the new contracts caused short-lived increases in trading activity above normal levels. These findings can help understand how different market events affect the financial performance of lumber futures, providing valuable insights for market participants.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".