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

Paper: arXiv 2512.14134

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.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; no out-of-sample test; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (37)

1. Abnormal Increase Trading Volume Effect on the Stock Return (2006)
2. Trading Behavior, Performance, and Stock Preference of Foreigners, Local Institutions, and Individual Investors: Evidence from the Korean Stock Market (2011) — link
3. The Unprecedented Stock Market Reaction to COVID-19 (2020) — link
4. All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors (2007) — link
5. Attention-Induced Trading and Returns: Evidence from Robinhood Users (2022) — link
6. Resolving a paradox: Retail trades positively predict returns but are not profitable (2024)
7. Market Statistics and Technical Analysis: The Role of Volume (1994) — link
8. Tracking Retail Investor Activity (2021) — link

0 of 37 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.