# Hawkes-based cryptocurrency forecasting via Limit Order Book data

arXiv: https://arxiv.org/abs/2312.16190 · HTML: https://mlsanity.com/paper/2312.16190/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Flexible Spatio-Temporal Hawkes Process Models for Earthquake Occurrences](https://mlsanity.com/paper/2210.08053/) (2022)
- [Hawkes Process for Understanding the Influence of Pathogenic Social Media Accounts](https://mlsanity.com/paper/1902.01970/) (2019)

## Built on by (most cited first, 1 of 1)

- [Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach](https://mlsanity.com/paper/2403.04427/) (2024)

## Abstract (arXiv)

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.