Market impact measurement of a VWAP trading algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it