Hawkes-based cryptocurrency forecasting via Limit Order Book data

ArXiv ID: 2312.16190 “View on arXiv”

Authors: Unknown

Abstract

Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency returns, given the chaotic and intricately complex nature of crypto markets. In this study, we present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model, a category of point processes. Coupled with a continuous output error (COE) model, our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. Capitalizing on the non-uniformly sampled structure of the original time series, our strategy surpasses benchmark models in both prediction accuracy and cumulative profit when implemented in a trading environment. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios. The research draws on LOB measurements from a centralized cryptocurrency exchange where the stablecoin Tether is exchanged against the U.S. dollar.

Keywords: Limit Order Book (LOB), Hawkes process, Cryptocurrency, Time series forecasting, Point processes, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced stochastic processes (Hawkes models) with detailed mathematical formulations and derivations, indicating high mathematical complexity. It is heavily data-driven with specific empirical validation using real limit order book data, Monte Carlo simulations, and reported trading performance, showing high empirical rigor.

  flowchart TD
    A["Research Goal<br>Predict cryptocurrency return signs using LOB data"] --> B{"Methodology"}
    B --> C["Hawkes Process Model"]
    B --> D["Continuous Output Error COE Model"]
    C & D --> E["Data Input<br>Cryptocurrency LOB Data Tether USD"]
    E --> F["Computational Process<br>Monte Carlo Simulations 50 Scenarios"]
    F --> G["Key Findings Outcomes"]
    G --> H["Superior Prediction Accuracy vs Benchmarks"]
    G --> I["Higher Cumulative Profit in Trading"]