Co-impact: Crowding effects in institutional trading activity

TL;DR

This paper investigates how multiple simultaneous institutional trades influence market impact, revealing that the collective effect depends on the number and correlation of these trades, and explaining the persistence of impact laws.

Contribution

It introduces the concept of co-impact, analyzing crowding effects on market impact using empirical data and a simple model, highlighting the dependence on metaorder number and sign correlation.

Findings

Market reacts mainly to net order flow of metaorders.

Co-impact depends on total metaorders and sign correlation.

Empirical impact curves match a calibrated heuristic model.

Abstract

This paper is devoted to the important yet unexplored subject of crowding effects on market impact, that we call "co-impact". Our analysis is based on a large database of metaorders by institutional investors in the U.S. equity market. We find that the market chiefly reacts to the net order flow of ongoing metaorders, without individually distinguishing them. The joint co-impact of multiple contemporaneous metaorders depends on the total number of metaorders and their mutual sign correlation. Using a simple heuristic model calibrated on data, we reproduce very well the different regimes of the empirical market impact curves as a function of volume fraction $\phi$: square-root for large $\phi$, linear for intermediate $\phi$, and a finite intercept $I_0$ when $\phi \to 0$. The value of $I_0$ grows with the sign correlation coefficient. Our study sheds light on an apparent paradox: How can a non-linear impact law survive in the presence of a large number of simultaneously executed metaorders?