Paper: arXiv 2605.11180

Authors: Ohad Kadan, Asaf Manela

Abstract

We show that under mild assumptions, the total value of information to informed traders in the market can be measured by the covariance between price changes and order flow. This covariance captures noise trader losses, which equal informed trader gains when market making is competitive. We estimate the value of information using high frequency data on US equities at about $3.5 million per year for the average stock. The aggregate value of information is about 0.04% of market cap, which is considerably lower than the 0.67% in fees investors pay each year searching for superior returns (French 2008). We discuss potential resolutions for these puzzling findings.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous mathematical derivation linking market microstructure theory (Kyle models) to a measurable covariance estimator, supported by extensive empirical application to large-scale, high-frequency US equity data over a two-decade period.

Research Flowchart

  flowchart TD
    A[<b>Goal:</b> Measure the Value of Information] --> B[<b>Data:</b> High-Frequency US Equity Data]
    B --> C[<b>Method:</b> Covariance of Price Changes & Order Flow]
    C --> D[<b>Process:</b> Quantify Informed Trader Gains vs Noise Trader Losses]
    D --> E[<b>Findings:</b> $3.5M/yr per stock & 0.04% of Market Cap]
    E --> F[<b>Conclusion:</b> Puzzle – Value < Search Fees (0.67%)]

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  365. Secret and Overt Information Acquisition in Financial Markets (2023) — link
  366. Strategic Informed Trading and Dark Pools (2020) — link
  367. Dynamic Trade Informativeness (2022) — link
  368. FDA Advisory Committees: Does Approval Mean Safety? (2006)

0 of 376 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.