Paper: arXiv 2611.05218

Authors: Jingqiu Ding, Daojing Zhai

Abstract

Conventional public order-book data summarize trading without identifying the accounts behind it. Hyperliquid’s Level-4 record also exposes persistent pseudonymous wallet addresses. We reconstruct full-depth books from consensus-ordered messages and evaluate wallet identity as a feature for short-horizon return prediction. The July 1-27, 2026 record covers 17.1 billion messages, 14.3 million aggressive orders by 147,113 wallets, and $84.3 billion in taker notional across ten markets. In the three model markets, notional-weighted ten-second markout rankings persist across adjacent ten-day windows (Spearman rho = 0.52); covariate-adjusted validation markouts reach 3.11 basis points in the top ventile. On a 100-millisecond grid, eleven features describing previously scored top-decile wallets raise one-second out-of-sample R2 from 10.88% to 12.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a novel approach to short-horizon return prediction by leveraging wallet identity from Level-4 decentralized exchange data. It demonstrates strong empirical rigor through extensive data analysis, statistical validation, and out-of-sample testing, making a significant contribution to market microstructure research.

Research Flowchart

  flowchart TD
    A[Research Goal: Predict Short-Horizon Returns using Wallet Identity] --> B{Data: Hyperliquid Level-4 Record};
    B --> C[Reconstruct Full-Depth Order Books from 17.1 Billion Messages];
    C --> D[Extract Features: Aggressive Orders by 147,113 Wallets, Taker Notional ($84.3B)];
    D --> E[Compute: Notional-Weighted Ten-Second Markout Rankings];
    E --> F{Analyze: Persistence of Rankings (rho=0.52) & Validation Markouts (3.11 bps)};
    F --> G[Outcome: Wallet Identity Features Improve 1-Sec Out-of-Sample R^2 from 10.88% to 12%];