Sources and Nonlinearity of High Volume Return Premium: An Empirical Study on the Differential Effects of Investor Identity versus Trading Intensity (2020-2024)

TL;DR

This study reveals that high volume return premiums in Korea are driven by institutional investor conviction levels and proper normalization methods, while retail investors act as noise traders unless coordinated during specific events.

Contribution

It demonstrates that investor type decomposition and conviction-based normalization are essential to accurately detect informed trading signals in high volume return premium analysis.

Findings

Institutional buying intensity normalized by market cap shows a clear positive relationship with returns.

Retail investors' trading yields near-zero returns, supporting the noise trader hypothesis.

During the Donghak Ant Movement, retail investors temporarily acted as liquidity providers, generating significant returns.

Abstract

Chae and Kang (2019, \textit{Pacific-Basin Finance Journal}) documented a puzzling Low Volume Return Premium (LVRP) in Korea -- contradicting global High Volume Return Premium (HVRP) evidence. We resolve this puzzle. Using Korean market data (2020-2024), we demonstrate that HVRP exists in Korea but is masked by (1) pooling heterogeneous investor types and (2) using inappropriate intensity normalization. When institutional buying intensity is normalized by market capitalization rather than trading value, a perfect monotonic relationship emerges: highest-conviction institutional buying (Q4) generates +\institutionLedQFourDayPlusFiftyCAR\ cumulative abnormal returns over 50 days, while lowest-intensity trades (Q1) yield modest returns (+\institutionLedQOneDayPlusFiftyCAR). Retail investors exhibit a flat pattern -- their trading generates near-zero returns regardless of conviction level -- confirming the pure noise trader hypothesis. During the Donghak Ant Movement (2020-2021), however, coordinated retail investors temporarily transformed from noise traders to liquidity providers, generating returns comparable to institutional trading. Our findings reconcile conflicting international evidence and demonstrate that detecting informed trading signals requires investor-type decomposition, nonlinear quartile analysis, and conviction-based (market cap) rather than participation-based (trading value) measurement.