Information Propagation Across Investor Types: Transfer Entropy Networks in the Korean Equity Market

TL;DR

This study constructs transfer entropy networks among different investor types in the Korean stock market to analyze information flow, revealing sparse, heterogeneous networks with limited private information transmission, supporting market efficiency at daily frequencies.

Contribution

It provides the first detailed transfer entropy network analysis of investor-type flows in the Korean market, highlighting the limited private information transfer and the dominance of shared information processing.

Findings

TE networks are sparse and heterogeneous.

Cross-investor information is redundant, not synergistic.

Network centrality adds negligible alpha in regressions.

Abstract

Whether heterogeneous investor flows transmit private information across stocks or merely reflect coordinated responses to public signals remains an open question in market microstructure. We construct Transfer Entropy (TE) networks from investor-type flows -- foreign, institutional, and individual -- for \numNStocks{} Korean equities over \numNDates{} trading days (January 2020 to February 2025), and evaluate their economic content through interaction information (II), conditional TE, mutual information (MI), Kelly criterion bounds, and Fama-MacBeth regressions. Three findings emerge. First, TE networks are sparse and structurally heterogeneous: foreign investors maintain few but strong links (\numEdgesFor{} edges, mean TE = \numMeanTEFor{}), while individual investors form many but weak links (\numEdgesInd{} edges, mean TE = \numMeanTEInd{}). Second, cross-investor information is redundant rather than synergistic, no investor type directionally dominates another, and MI between signals and returns is zero at the daily horizon. Third, network centrality adds negligible alpha in cross-sectional regressions, with only one of six signal-centrality interactions reaching marginal significance. These results indicate that the observed propagation structure captures shared information processing rather than private signal cascades, consistent with daily-frequency market efficiency.