Identification of clusters of investors from their real trading activity in a financial market

TL;DR

This paper employs statistically validated networks to identify investor clusters in Nokia's stock market, revealing synchronized trading behaviors and over-represented investor categories within clusters.

Contribution

It introduces the application of statistically validated networks to detect and analyze investor clusters based on real trading activity in a financial market.

Findings

Clusters exhibit high synchronization in trading timing and actions.

Certain investor categories are over-represented in specific clusters.

Method effectively uncovers structured trading behaviors.

Abstract

We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that many statistically detected clusters of investors show a very high degree of synchronization in the time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and we find that several of them show an over-expression of specific categories of investors.