The Launch of a Night Trading Session and Currency Futures Market Liquidity: Evidence from the Thailand Futures Exchange
Bibliographic record
Abstract
The Thailand Futures Exchange launched USD Futures as the first currency futures contract on 5 June 2012. However, it has been available for night trading since 27 September 2021. This research aims to analyze the effect of adding a night trading session on USD Futures market liquidity and to make a liquidity comparison between day and night session trading. By adding a dummy variable into the vector autoregression model of order 5 to capture the effect of a night session introduction on market liquidity, the results show that market depth and breadth are even stronger after a longer trading session. In addition, the t-test results show the presence of lower tightness but stronger depth and breadth in day session trading than in night session trading, because of the availability of a large number of orders and the ability of the market to have smoother trading in day as opposed to night. Due to the positive effect of extended trading hours on market depth and breadth, TFEX should consider a longer night session in line with other global futures markets. Night traders should also be aware of liquidity risk due to low night session trading volume.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.001 |
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".