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Public Trader Identity: Adverse Selection and Return Predictability

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  • Daojing Zhai

Abstract

Informed traders are supposed to need anonymity: they profit by hiding among the uninformed. A decentralized exchange now publishes the counterparty. Every committed order, cancellation, rejection, and fill carries a persistent pseudonymous wallet address. We reconstruct the full-depth limit order book from a record of 17.1 billion messages and 14.3 million aggressive orders by 147,113 wallets, covering $84.3 billion in taker notional. We report three findings. First, informativeness is a persistent wallet attribute. Wallets ranked by the price movement following their aggressive orders retain that ordering across adjacent ten-day windows, with a rank correlation of 0.52. Second, the ranking predicts returns. Adding the live activity of the highest-ranked wallets to a standard anonymous benchmark of prices, quotes, and order flow raises the out-of-sample R2 for one-second returns to 12.31%, a 13.2% gain (t = 9.2) that is 1.6 times the largest of 200 activity-matched placebo cohorts. Third, measured at realized trades rather than at every sampled moment, the increment grows from 1.43 to 2.47 percentage points of R2. Public wallet histories therefore carry short-horizon price information that anonymous order-book data leave unmeasured.

Suggested Citation

  • Daojing Zhai, 2026. "Public Trader Identity: Adverse Selection and Return Predictability," Papers 2608.04373, arXiv.org, revised Aug 2026.
  • Handle: RePEc:arx:papers:2608.04373
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    References listed on IDEAS

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    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Marco Avellaneda & Sasha Stoikov, 2008. "High-frequency trading in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 217-224.
    3. Pierre Collin-Dufresne & Vyacheslav Fos, 2015. "Do Prices Reveal the Presence of Informed Trading?," Journal of Finance, American Finance Association, vol. 70(4), pages 1555-1582, August.
    4. Petter N. Kolm & Jeremy Turiel & Nicholas Westray, 2023. "Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book," Mathematical Finance, Wiley Blackwell, vol. 33(4), pages 1044-1081, October.
    5. Admati, Anat R & Pfleiderer, Paul, 1991. "Sunshine Trading and Financial Market Equilibrium," The Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 443-481.
    6. Glosten, Lawrence R. & Harris, Lawrence E., 1988. "Estimating the components of the bid/ask spread," Journal of Financial Economics, Elsevier, vol. 21(1), pages 123-142, May.
    7. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    8. Álvaro Cartea & Ryan Donnelly & Sebastian Jaimungal, 2018. "Enhancing trading strategies with order book signals," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 1-35, January.
    9. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    10. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    11. Andrea Barbon & Marco Di Maggio & Francesco Franzoni & Augustin Landier, 2019. "Brokers and Order Flow Leakage: Evidence from Fire Sales," Journal of Finance, American Finance Association, vol. 74(6), pages 2707-2749, December.
    12. Sasha Stoikov, 2018. "The micro-price: a high-frequency estimator of future prices," Quantitative Finance, Taylor & Francis Journals, vol. 18(12), pages 1959-1966, December.
    13. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. "An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    14. 'Alvaro Cartea & Gerardo Duran-Martin & Leandro S'anchez-Betancourt, 2023. "Detecting Toxic Flow," Papers 2312.05827, arXiv.org, revised Jan 2026.
    15. Juhani T. Linnainmaa & Gideon Saar, 2012. "Lack of Anonymity and the Inference from Order Flow," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1414-1456.
    16. Alfred Lehar & Christine Parlour, 2025. "Decentralized Exchange: The Uniswap Automated Market Maker," Journal of Finance, American Finance Association, vol. 80(1), pages 321-374, February.
    17. Pierre Collin-Dufresne & Vyacheslav Fos, 2013. "Do Prices Reveal the Presence of Informed Trading?," Swiss Finance Institute Research Paper Series 13-69, Swiss Finance Institute, revised Sep 2015.
    18. Barber, Brad M. & Lee, Yi-Tsung & Liu, Yu-Jane & Odean, Terrance, 2014. "The cross-section of speculator skill: Evidence from day trading," Journal of Financial Markets, Elsevier, vol. 18(C), pages 1-24.
    19. Vincent Van Kervel & Albert J. Menkveld, 2019. "High‐Frequency Trading around Large Institutional Orders," Journal of Finance, American Finance Association, vol. 74(3), pages 1091-1137, June.
    20. Ke Xu & Martin D. Gould & Sam D. Howison, 2019. "Multi-Level Order-Flow Imbalance in a Limit Order Book," Papers 1907.06230, arXiv.org, revised Oct 2019.
    21. Huang, Roger D & Stoll, Hans R, 1997. "The Components of the Bid-Ask Spread: A General Approach," The Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 995-1034.
    22. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    23. Baron, Matthew & Brogaard, Jonathan & Hagströmer, Björn & Kirilenko, Andrei, 2019. "Risk and Return in High-Frequency Trading," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(3), pages 993-1024, June.
    24. Álvaro Cartea & Gerardo Duran-Martin & Leandro Sánchez-Betancourt, 2026. "Detecting toxic flow," Quantitative Finance, Taylor & Francis Journals, vol. 26(4), pages 541-561, April.
    25. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.
    26. Peter C. Reiss, 2005. "Anonymity, Adverse Selection, and the Sorting of Interdealer Trades," The Review of Financial Studies, Society for Financial Studies, vol. 18(2), pages 599-636.
    27. Justin Sirignano & Rama Cont, 2019. "Universal features of price formation in financial markets: perspectives from deep learning," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1449-1459, September.
    28. Tom Grimstvedt Meling, 2021. "Anonymous Trading in Equities," Journal of Finance, American Finance Association, vol. 76(2), pages 707-754, April.
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