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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".