Nonlinear Evidence of Investor Heterogeneity: Retail Cash Flows as Drivers of Market Dynamics

Paper: arXiv 2508.20426

Authors: Gabjin Oh

Abstract

This study measures the long memory of investor-segregated cash flows within the Korean equity market from 2015 to 2024. Applying detrended fluctuation analysis (DFA) to BUY, SELL, and NET aggregates, we estimate the Hurst exponent ($H$) using both a static specification and a 250-day rolling window. All series exhibit heavy tails, with complementary cumulative distribution exponents ranging from approximately 2 to 3. As a control, time-shuffled series yield $H \approx 0.5$, confirming that the observed persistence originates from the temporal structure rather than the distributional shape. Our analysis documents long-range dependence and reveals a clear ranking of persistence across investor types. Persistence is strongest for retail BUY and SELL flows, intermediate for institutional flows, and lowest for foreign investor flows. For NET flows, however, this persistence diminishes for retail and institutional investors but remains elevated for foreign investors. The rolling $H$ exhibits clear regime sensitivity, with significant level shifts occurring around key events: the 2018–2019 tariff episode, the COVID-19 pandemic, and the period of disinflation from November 2022 to October 2024. Furthermore, regressions of daily volatility on the rolling $H$ produce positive and statistically significant coefficients for most investor groups. Notably, the $H$ of retail NET flows demonstrates predictive power for future volatility, a characteristic not found in institutional NET flows. These findings challenge the canonical noise-trader versus informed-trader dichotomy, offering a model-light, replicable diagnostic for assessing investor persistence and its regime shifts.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a rigorous empirical investigation of investor heterogeneity using Detrended Fluctuation Analysis (DFA) on segmented cash flows rather than prices. While it relies on standard, non-complex time series statistics (DFA, Hurst exponent, shuffling tests), its empirical design, clear methodology, and novel insights regarding retail order flow persistence and volatility prediction make it a strong applied quantitative contribution.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess Investor Long Memory & Market Dynamics] --> B[Data Input: Korean Equity Cash Flows 2015-2024<br/>BUY, SELL, NET for Retail, Inst., Foreign]
    B --> C[Methodology: Detrended Fluctuation Analysis & Controls<br/>Static/Rolling H Exponent + Shuffled Control]
    C --> D[Key Finding 1: Persistence Ranking<br/>Retail > Institutional > Foreign Flows]
    C --> E[Key Finding 2: Regime Sensitivity<br/>H Shifts During Tariffs, COVID-19, Disinflation]
    D & E --> F[Outcome: Retail NET H Predicts Future Volatility<br/>Challenges Traditional Noise-Trader Dichotomy]

Cited References (33)

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3. Mosaic organization of DNA nucleotides (1994) — link
4. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics (1994)
5. Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series (2010) — link
6. On Hurst exponent estimation under heavy-tailed distributions (2010) — link
7. Modified detrended fluctuation analysis based on empirical mode decomposition (2011) — link
8. Modified DFA and DCCA approach for quantifying the multiscale correlation structure of financial markets (2013) — link

0 of 33 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.