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

ArXiv ID: 2512.14134 “View on arXiv”

Authors: Sungwoo Kang

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.

Keywords: Volume Anomalies, Institutional Trading, Market Microstructure, Korean Stock Market, Return Premium

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders
  • Why: The paper’s math is primarily statistical (quartile analysis, CAR) and does not involve advanced theoretical derivations, keeping complexity low. However, it is highly data-driven, using specific Korean market data (2020-2024), performing investor-type decomposition, and presenting detailed empirical results like cumulative abnormal returns, making it backtest-ready and empirically rigorous.
  flowchart TD
    A["Research Goal<br>Resolve LVRP vs HVRP Puzzle in Korea"] --> B["Data & Methodology<br>Korean Market (2020-2024)<br>Investor-Type Decomposition<br>Quartile Analysis"]
    B --> C{"Normalization Test<br>Market Cap vs Trading Value"}
    C -- Trading Value --> D["Masked Signal<br>Low Volume Premium"]
    C -- Market Cap --> E["Exposed Signal<br>High Volume Premium"]
    E --> F{"Investor Identity Analysis"}
    F --> G["Institutions<br>Monotonic HVRP<br>Q4: +50CAR<br>Q1: +LowCAR"]
    F --> H["Retail Investors<br>Flat Pattern<br>Noise Traders"]
    H --> I["Exception<br>Donghak Ant Movement<br>Temp. Informed"]
    G & I --> J["Key Findings<br>Decomposition Required<br>Conviction > Participation<br>Reconciles Global Evidence"]