Paper: arXiv 2503.18199

Abstract

This paper introduces a novel algorithm for generating realistic metaorders from public trade data, addressing a longstanding challenge in price impact research that has traditionally relied on proprietary datasets. Our method effectively recovers all established stylized facts of metaorders impact, such as the Square Root Law, the concave profile during metaorder execution, and the post-execution decay. This algorithm not only overcomes the dependence on proprietary data, a major barrier to research reproducibility, but also enables the creation of larger and more robust datasets that may increase the quality of empirical studies. Our findings strongly suggest that average realized short-term price impact is not due to information revelation (as in the Kyle framework) but has a mechanical origin which could explain the universality of the Square Root Law.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.2: transaction costs not modelled; no out-of-sample test; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper involves algorithmic complexity and statistical modeling, requiring moderate mathematical sophistication, while being heavily data-driven with specific backtesting methodology and validation against established stylized facts.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop algorithm<br>to generate realistic metaorders<br>from public trade data"] --> B["Methodology: Constrained Optimization<br>to recover metaorders"]
  B --> C["Inputs: Public trade data<br>without labels"]
  C --> D["Computation: Constrained optimization<br>to find non-overlapping metaorders"]
  D --> E{"Verification against<br>stylized facts?"}
  E -- Yes --> F["Key Findings: Model replicates<br>Square Root Law & impact profiles"]
  E -- No --> B
  F --> G["Conclusion: Impact is mechanical<br>not informational<br>(refuting Kyle model)"]

Cited References (44)

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  2. A fully consistent, minimal model for non-linear market impact (2015)
  3. Market impact with multi-timescale liquidity (2018)
  4. The two square root laws of market impact and the role of sophisticated market participants (2023) — link
  5. The Square-Root Impace Law Also Holds for Option Markets (2016)
  6. Institutional Investors and Stock Market Volatility* (2006) — link
  7. Universal scaling and nonlinearity of aggregate price impact in financial markets (2018)
  8. How efficiency shapes market impact (2013)
and 36 more
  1. A Bayesian theory of market impact — → 2303.08867
  2. Market impacts and the life cycle of investors orders (2015)
  3. Market impact: A systematic study of limit orders (2017)
  4. The" double" square-root law: Evidence for the mechanical origin of market impact using Tokyo Stock Exchange data — → 2502.16246
  5. Does the square-root price impact law belong to the strict universal scalings?: quantitative support by a complete survey of the Tokyo stock exchange market — → 2411.13965
  6. Anomalous price impact and the critical nature of liquidity in financial markets (2011)
  7. Trades, quotes and prices: financial markets under the microscope (2018)
  8. Slow decay of impact in equity markets: insights from the ANcerno database (2018)
  9. Exactly solvable model of the square-root price impact dynamics under the long-range market-order correlation (2025) — link
  10. Crossover from Linear to Square-Root Market Impact (2019) — link
  11. Why is the estimation of metaorder impact with public market data so challenging? (2025) — link
  12. “Microstructure Modes” – Disentangling the Joint Dynamics of Prices & Order Flow (2024) — link
  13. in preparation (2025)
  14. Continuous auctions and insider trading (1985)
  15. Online learning of order flow and market impact with Bayesian change-point detection methods — → 2307.02375
  16. Quantitative statistical analysis of order-splitting behavior of individual trading accounts in the Japanese stock market over nine years (2023) — link
  17. A million metaorder analysis of market impact on the Bitcoin (2015)
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  24. Limit order book simulation with generative adversarial networks (2023)
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  28. Trading costs (2018)
  29. The inelastic market hypothesis: a microstructural interpretation (2022)
  30. Market impact and trading profile of hidden orders in stock markets (2009)
  31. Empirical market microstructure: The institutions, economics, and econometrics of securities trading (2007)
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  35. In search of the origins of financial fluctuations: The inelastic markets hypothesis (2021)
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4 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.