Market impact measurement of a VWAP trading algorithm
Bibliographic record
Abstract
This paper proposes a model for the market impact of algorithmic trades. Usually large orders cannot be executed immediately without significant trading costs. For optimised execution one relies on the help of a VWAP (volume-weighted average price) trading algorithm. It is demonstrated that the VWAP algorithm is the optimal solution of the optimisation problem using the market impact models presented in this paper. The purpose of this work is the empirical market impact analysis of a homogeneous set of algorithmic trades. The underlying data set contains trades resulting from a hedge fund trading strategy. The analysis shows that the participation rate is the most important description variable. Therefore, a linear model and also a concave power law model of the market impact, dependent on the participation rate, are used. The estimated parameters lead to interesting consequences for verifying certain aspects of the market microstructure theory. The results also suggest different behaviour of the various analysed markets. On the one hand the market impact dependency on the participation rate behaves differently for the Japanese market compared to the European, US and Canadian markets. On the other hand, the individualised linear regression results suggest a dependency of the market impact on tick size for the Japanese and US markets.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".