Paper: arXiv 2411.13594

Abstract

We propose an error-correcting model for the microprice, a high-frequency estimator of future prices given higher order information of imbalances in the orderbook. The model takes into account a current microprice estimate given the spread and best bid to ask imbalance, and adjusts the microprice based on recent dynamics of higher price rank imbalances. We introduce a computationally fast estimator using a recently proposed hyperdimensional vector Tsetlin machine framework and demonstrate empirically that this estimator can provide a robust estimate of future prices in the orderbook.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents advanced mathematical structures including recursive definitions of higher-order adjustments, Markov chain transition matrices, and a complex theoretical framework for microprice convergence, resulting in a high math complexity score. It provides empirical validation using high-frequency level 3 orderbook data from a real provider (Databento) and discusses practical implementation speed, which supports a high empirical rigor score.

Research Flowchart

  flowchart TD
  A["Research Goal: High-Fres Microprice<br>Estimation from Limit Orderbook"] --> B["Input Data:<br>Limit Orderbook Dynamics"]
  B --> C["Methodology:<br>Hyperdimensional Vector<br>Tsetlin Machine"]
  C --> D["Computational Process:<br>Error-Correcting Model<br>Adjusts Microprice based on<br>High-Rank Orderbook Imbalances"]
  D --> E["Outcome:<br>Fast & Robust<br>High-Frequency<br>Price Prediction"]