Single Curve Collapse of the Price Impact Function for the New York Stock Exchange

TL;DR

This paper demonstrates that after rescaling, the average price impact of trades on the NYSE for the top stocks collapses onto a universal curve, revealing a power-law relationship for small trades and a slower increase for larger trades.

Contribution

It introduces a universal scaling law for the price impact function across multiple stocks and years, highlighting a power-law behavior and its relation to market capitalization.

Findings

Price impact scales as a power law with trade size for small trades.

Rescaled data from 1995-1998 collapses onto a single universal curve.

Small volume liquidity scales as a power of stock capitalization.

Abstract

We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 for small volumes, but then increases more slowly for large volumes. We obtain similar results in each year from the period 1995 - 1998. We also find that small volume liquidity scales as a power of the stock capitalization.