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.

翻译：准确预测金融收益方向是一项严峻挑战，这源于金融时间序列固有的不可预测性。当应用于加密货币收益时，由于加密货币市场的混沌与复杂本质，该任务更具挑战性。在本研究中，我们提出一种基于限价订单簿数据的新型预测算法，该算法根植于霍克斯过程（一类点过程模型）。结合连续输出误差模型，我们的方法通过预测未来金融交互行为，实现了对收益符号的精确预测。通过利用原始时间序列的非均匀采样结构，我们的策略在交易环境中的预测准确性和累计利润均超越基准模型。通过50个场景的蒙特卡洛模拟验证了该方法的有效性。本研究采用的数据源自中心化加密货币交易所中稳定币Tether与美元交易的限价订单簿测量值。