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Universal scaling and nonlinearity of aggregate price impact in financial markets

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  • Felix Patzelt
  • Jean-Philippe Bouchaud

Abstract

How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day scale. Its shape varies little across instruments, but drastically different master curves are obtained for order-volume and -sign impact. The scaling is largely determined by the relevant Hurst exponents. We further show that extreme order flow imbalance is not associated with large returns. To the contrary, it is observed when the price is "pinned" to a particular level. Prices move only when there is sufficient balance in the local order flow. In fact, the probability that a trade changes the mid-price falls to zero with increasing (absolute) order-sign bias along an arc-shaped curve for all intra-day scales. Our findings challenge the widespread assumption of linear aggregate impact. They imply that market dynamics on all intra-day timescales are shaped by correlations and bilateral adaptation in the flows of liquidity provision and taking.

Suggested Citation

  • Felix Patzelt & Jean-Philippe Bouchaud, 2017. "Universal scaling and nonlinearity of aggregate price impact in financial markets," Papers 1706.04163, arXiv.org, revised Aug 2017.
  • Handle: RePEc:arx:papers:1706.04163
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    Cited by:

    1. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    2. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.

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