Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets

TL;DR

This paper introduces a novel co-trading network approach to model dynamic interdependencies among US stocks, revealing meaningful clusters and improving high-dimensional covariance estimation for better portfolio optimization.

Contribution

It proposes a new co-trading-based method to construct dynamic stock networks and develops a covariance estimator that enhances portfolio performance.

Findings

Co-trading networks uncover meaningful stock clusters beyond traditional sectors.

Positive correlation between co-trading intensity and return covariance.

Improved portfolio performance with lower volatility and higher Sharpe ratios.

Abstract

The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States. In this article, we investigate how such concurrent cross-stock trading behaviors, which we denote as co-trading, shape the market structures and affect stock price co-movements. By leveraging a co-trading-based pairwise similarity measure, we propose a novel method to construct dynamic networks of stocks. Our empirical studies employ high-frequency limit order book data from 2017-01-03 to 2019-12-09. By applying spectral clustering on co-trading networks, we uncover economically meaningful clusters of stocks. Beyond the static Global Industry Classification Standard (GICS) sectors, our data-driven clusters capture the time evolution of the dependency among stocks. Furthermore, we demonstrate statistically significant positive relations between low-latency co-trading and return covariance. With the aid of co-trading networks, we develop a robust estimator for high-dimensional covariance matrix, which yields superior economic value on portfolio allocation. The mean-variance portfolios based on our covariance estimates achieve both lower volatility and higher Sharpe ratios than standard benchmarks.