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"]

Cited References (44)

  1. Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome (2015) — link
  2. Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines (2024) — link
  3. Coalesced Multi-Output Tsetlin Machines with Clause Sharing (2021) — link
  4. HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing (2022) — link
  5. Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences (2022) — link
  6. Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability (2020) — link
  7. Theory of games and economic behavior (commemorative edition) (2007)
  8. Learning Automata: An Introduction (1989)
and 36 more
  1. On behaviour of finite automata in random medium (1961)
  2. The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses (2019) — link
  3. The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic (2018) — link
  4. Adaptive Machine Learning for Credit Card Fraud Detection (2015)
  5. A Tsetlin Machine Approach for Learning and Prediction in Multivariate Time Series (2020) — link
  6. A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks (2019) — link
  7. A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning (2020) — link
  8. Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing (2020) — link
  9. A Tsetlin Machine with Multigranular Clauses (2019) — link
  10. The Convolutional Tsetlin Machine (2019) — link
  11. Sparse spatial autoregressions (1997)
  12. A two-armed bandit collective for hierarchical examplar based mining of frequent itemsets with applications to intrusion detection (2014)
  13. Learning Automata-based Solutions to the Nonlinear Fractional Knapsack Problem with Applications to Optimal Resource Allocation (2007)
  14. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (2019)
  15. Stochastic searching on the line and its applications to parameter learning in nonlinear optimization (1997)
  16. A Tsetlin Machine with Multigranular Clauses (2019)
  17. Explainability and Dependability Analysis of Learning Automata based AI Hardware (2020)
  18. Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing (2020)
  19. Learning Automata based Energy-efficient AI Hardware Design for IoT (2020)
  20. The Regression Tsetlin Machine - A Novel Approach to Interpretable Non-Linear Regression (2019)
  21. Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications (2019) — link
  22. Hyperdimensional Vector Tsetlin Machines with Applications to Sequence Learning and Generation (2024) — link
  23. Accelerating Hyperdimensional Computing on FPGAs by Exploiting Computational Reuse (2020) — link
  24. Neural Additive Models: Interpretable Machine Learning with Neural Nets (2020) — link
  25. The Micro-Price: A High Frequency Estimator of Future Prices (DOI: 10.1080/14697688.2018.1489139)
  26. Using SHAP values for XGboost interpretability (2018 (accessed May 20, 2020)) — link
  27. Five practical steps to make Artificial Intelligence (AI) interpretable (2019 (accessed July 27, 2020)) — link
  28. COMPAS Analysis (2016 (accessed May 20, 2020)) — link
  29. A Unified Approach to Interpreting Model Predictions (2017) — link
  30. The UCR Time Series Classification Archive (2018)
  31. Contrastive Principal Component Analysis (2017)
  32. The Sparse Tsetlin Machine: Sparse Representation with Active Literals (2024) — link
  33. A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations (2022) — link
  34. ``Why Should I Trust You?’’: Explaining the Predictions of Any Classifier (2016) — link
  35. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods (2019) — link
  36. An FPGA Architecture for Online Learning using the Tsetlin Machine (2023) — link

0 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.