Volume-Synchronised Probability of Informed Trading on Chinese Index Futures: A Comparative Approach1
Bibliographic record
Abstract
Abstract In light of the controversial debate over the measure of Volume-Synchronised Probability of Informed Trading (VPIN), this paper investigates the effectiveness of VPIN as a risk warning signal in the Chinese market. Using intraday transaction data on Chinese stock index futures from 2012 to 2013, we conduct a comparative analysis of VPIN metrics using three trade classification methods: the tick rule (TR), the Lee-Ready (LR) algorithm, and bulk volume (BV). We assess the predictive ability of VPIN metrics for two highly volatile market events in China: the Money Shortage Event in June 2013 and the Fat Finger Event on 16 August 2013. Our results suggest that BV-VPIN has the best risk warning effect in signalling the occurrence of volatile events. Our work suggests that BV-VPIN can be used in the prevalent high-frequency trading (HFT) mechanism of the current financial world.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".