Anomalous Price Impact and the Critical Nature of Liquidity in Financial Markets

Tóth, Bence; Lempérière, Yves; Deremble, Cyril; Lataillade, Joachim de; Kockelkoren, Julien; Bouchaud, Jean‐Philippe

doi:10.1103/physrevx.1.021006

Cited by 194 publications

(309 citation statements)

References 27 publications

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“…Thus, the origin of the scattered values of the different exponents found in the microstructure properties and functions across different markets (quantities such as α M , γ M , β M ) may have their roots in the different composition of market participants at different market places, and in the nature of the strategies of market members. This is in sharp contrast with theoretical models suggesting universal values (e.g., the "square-root law") for market impact functions based on homogeneous behavior of market participants [12,13,19,20,34]. It is our hope that our work will help to inform and to evolve models that include heterogeneity in price impact, to understand how such models operate on the market level [1], and to encourage further investigations into the relationship between individual and market behavior in other areas.…”

Section: Discussioncontrasting

confidence: 89%

“…The precise reasons for this variation are as yet not fully understood, making the current understanding of instantaneous market impact incomplete to a certain extent. Moreover, the aggregated impact of hidden orders also depends on the instantaneous market impact function [3], and despite there being several theories that suggest a "square-root law" for the market impact of large hidden orders [19,20], the observations suggest a power-law relationship with exponents in the range [0.4,0.7]; see Refs. [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Agent-specific impact of single trades in financial markets

2012

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We present an analysis of the price impact associated with single trades effected by different financial firms. Using data from the Spanish Stock Market, we find a high degree of heterogeneity across different market members, both in the instantaneous impact functions and in the time-dependent market response to trades by individual members. This heterogeneity is statistically incompatible with the existence of market-wide universal impact dynamics that apply uniformly to all trades and suggest that, rather, market dynamics emerge from the complex interaction of different behaviors of market participants. Several possible reasons for this are discussed, along with potential extensions one may consider to increase the range of applicability of existing models of market impact.

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Section: Discussioncontrasting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Agent-specific impact of single trades in financial markets

2012

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“…Empirically, the impact of meta-orders is only weakly dependent on the participation rate m 0 /J (see e.g. [7]). The corresponding square root law is commonly (resp. )…”

Section: Set Up Of the Problemmentioning

confidence: 99%

Market Impact with Multi-Timescale Liquidity

Benzaquen

Bouchaud

2017

SSRN Journal

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We present an extended version of the recently proposed "LLOB" model for the dynamics of latent liquidity in financial markets. By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of the latent order book. We compute in particular the finite memory corrections to the square root impact law, as well as the impact decay and the permanent impact of a meta-order. The latter is found to be linear in the traded volume and independent of the trading rate, as dictated by no-arbitrage arguments. In addition, we consider the case of a spectrum of cancellation and deposition rates, which allows us to obtain a square root impact law for moderate participation rates, as observed empirically. Our multi-scale framework also provides an alternative solution to the so-called price diffusivity puzzle in the presence of a long-range correlated order flow.

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“…While most studies using proprietary data agree that the price impact of an order is a monotonically increasing function of its size (e.g. see Almgren et al 2005, Tóth et al 2011, their reliance on such data is prone to several criticisms. Firstly, there are a number of factors that affect price impact but are difficult to control in a proprietary dataset, including the trading venues and algorithms used to execute a trade (Brandes et al 2007).…”

Section: Literature Reviewmentioning

confidence: 98%

Performance-weighted ensembles of random forests for predicting price impact

Booth

Gerding

McGroarty

2014

Quantitative Finance

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For any large player in financial markets, the impact of their trading activity represents a substantial proportion of transaction costs. This paper proposes a novel machine learning algorithm for predicting the price impact of order book events. Specifically, we introduce a prediction system based on ensembles of random forests (RFs). The system is trained and tested on depth-of-book data from the BATS and Chi-X exchanges and performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression. The results show that recency-weighted ensembles of RFs produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Feature importance ranking is used to explore the significance of various market features on the price impact, finding them to be highly variable through time. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process.

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Anomalous Price Impact and the Critical Nature of Liquidity in Financial Markets

Cited by 194 publications

References 27 publications

Agent-specific impact of single trades in financial markets

Agent-specific impact of single trades in financial markets

Market Impact with Multi-Timescale Liquidity

Performance-weighted ensembles of random forests for predicting price impact

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