Open outcry versus electronic trading: tests of market efficiency on crude palm oil futures
Bibliographic record
Abstract
Given the widespread transfer of trading to electronic platforms it is important to ask whether such trading is more efficient than traditional open outcry. To empirically assess this we examine the Crude Palm Oil market from 1995:06 to 2008:07 - a market where all trading swapped over from open outcry to electronic trading at the end of 2001. Results indicate that both forms of trading are long-run efficient but that short-run inefficiencies do exist. Our main findings, derived from the application of a novel threshold autoregressive relative efficiency measure, is that market efficiency is conditional on (i) the volatility of the underlying asset (ii) the maturity of the futures contract and (iii) the market trading system. Specifically, bootstrap results from the efficiency measure suggest that the open outcry trading method is superior for shorter maturities when volatility is high, and indistinguishable from electronic trading when volatility is low or maturity is long. These results suggest that electronic trading should not supersede open outcry, but rather that there are clear benefits to their coexistence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".