Wholesale price prediction: The role of information and transparency
Bibliographic record
Abstract
The extent of real-time information disclosure in electricity markets has been a longstanding debate. Regulators have the difficult task of striking a careful balance between transparency to improve market outcomes under uncertainty while limiting the potential for coordinated action. We consider the case of Alberta’s electricity market where, until 2017, firms observed anonymized price-quantity offers in the wholesale market in near-real-time. We empirically evaluate the role that this information played in improving firms’ abilities to forecast wholesale prices, a key argument raised by stakeholders for this information to be published. While we find that this information improved firms’ abilities to forecast wholesale prices under certain market conditions, we present evidence to suggest that the economic significance of this improvement is minimal. We point to other types of near-real-time information that could help improve expectations of future market outcomes and provide suggestions on information disclosure policies that aim to strike a balance in motivating efficient outcomes, while reducing the risk of coordination. • We investigate the role of data transparency in improving wholesale electricity price forecasts. • Our paper is motivated by the trade-off between information disclosure and its effect on efficiency versus risks of coordinated behavior. • Our results suggest that the release of near real-time offer behavior improves price forecasts, but has small economic effects on operational decisions. • Our results point to the release of market-level information to promote market efficiency without the associated competition policy risks.
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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 | 0.018 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".