Grasping asymmetric information in market impacts

TL;DR

This paper investigates the asymmetric nature of cross-market price impacts, revealing that impacts are non-random and contain useful information, with implications for understanding market dynamics and information flow.

Contribution

It introduces a novel analysis of asymmetries in cross-impacts using spectral statistics and Shannon entropy, highlighting the informational content of impact patterns.

Findings

Impacts are asymmetric and non-random across markets.

Small entropy impacts encode useful information.

Highly traded stocks tend to impact others more.

Abstract

The price impact for a single trade is estimated by the immediate response on an event time scale, i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of cross-impacts, and find that the impacts across the market are asymmetric and non-random. Using spectral statistics and Shannon entropy, we visualize the asymmetric information in price impacts. Also, we introduce an entropy of impacts to estimate the randomness between stocks. We show that the useful information is encoded in the impacts corresponding to small entropy. The stocks with large number of trades are more likely to impact others, while the less traded stocks have higher probability to be impacted by others.