Crossover from Linear to Square-Root Market Impact

Using a large database of 8 million institutional trades executed in the U.S. equity market, we establish a clear crossover between a linear market impact regime and a square-root regime as a function of the volume of the order. Our empirical results are remarkably well explained by a recently proposed dynamical theory of liquidity that makes specific predictions about the scaling function describing this crossover. Allowing at least two characteristic timescales for the liquidity ("fast" and "slow") enables one to reach quantitative agreement with the data.

[1]  J. Bouchaud,et al.  How does latent liquidity get revealed in the limit order book? , 2018, Journal of Statistical Mechanics: Theory and Experiment.

[2]  J. Bouchaud,et al.  Co-impact: crowding effects in institutional trading activity , 2018, Quantitative Finance.

[3]  J. Bouchaud,et al.  Trades, Quotes and Prices: Financial Markets Under the Microscope , 2018 .

[4]  J. Bouchaud,et al.  Market impact with multi-timescale liquidity , 2017, Quantitative Finance.

[5]  J. Bouchaud,et al.  Universal scaling and nonlinearity of aggregate price impact in financial markets. , 2017, Physical review. E.

[6]  H. Takayasu,et al.  Derivation of the Boltzmann Equation for Financial Brownian Motion: Direct Observation of the Collective Motion of High-Frequency Traders. , 2017, Physical review letters.

[7]  J. Bouchaud,et al.  The Square-Root Impace Law Also Holds for Option Markets: The Square-Root Impace Law Also Holds for Option Markets , 2016 .

[8]  J. Bouchaud,et al.  The square-root impact law also holds for option markets , 2016, 1602.03043.

[9]  Jonathan Donier,et al.  A Million Metaorder Analysis of Market Impact on the Bitcoin , 2014, 1412.4503.

[10]  Fabrizio Lillo,et al.  Beyond the Square Root: Evidence for Logarithmic Dependence of Market Impact on Size and Participation Rate , 2014, 1412.2152.

[11]  E. Bacry,et al.  Market Impacts and the Life Cycle of Investors Orders , 2014, SSRN Electronic Journal.

[12]  J. Bouchaud,et al.  A fully consistent, minimal model for non-linear market impact , 2014, 1412.0141.

[13]  J. Bouchaud,et al.  Slow Decay of Impact in Equity Markets , 2014, 1407.3390.

[14]  J. Bouchaud,et al.  Anomalous impact in reaction-diffusion financial models. , 2014, Physical review letters.

[15]  Jaroslaw Kwapien,et al.  Stock returns versus trading volume: is the correspondence more general? , 2013, 1310.7018.

[16]  E. Bacry,et al.  Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data , 2012, The European Physical Journal B.

[17]  E. Bacry,et al.  Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data , 2011, 1112.1838.

[18]  Jean-Philippe Bouchaud,et al.  Anomalous Price Impact and the Critical Nature of Liquidity in Financial Markets , 2011, 1105.1694.

[19]  Esteban Moro,et al.  Market impact and trading profile of hidden orders in stock markets. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Fabrizio Lillo,et al.  Diffusive behavior and the modeling of characteristic times in limit order executions , 2007, physics/0701335.

[21]  F. Lillo Limit order placement as an utility maximization problem and the origin of power law distribution of limit order prices , 2006, physics/0612016.

[22]  Jeffrey R. Russell,et al.  Measuring and Modeling Execution Cost and Risk , 2006 .

[23]  V. Plerou,et al.  A theory of power-law distributions in financial market fluctuations , 2003, Nature.

[24]  Yi-Cheng Zhang Toward a theory of marginally efficient markets , 1999, cond-mat/9901243.

[25]  A. Kyle Continuous Auctions and Insider Trading , 1985 .

[26]  R. Almgren,et al.  Direct Estimation of Equity Market Impact , 2005 .