  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.
