This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.

翻译：本研究探索利用深度学习模型预测高频价格变动。尽管现有最先进方法表现良好，但其复杂性阻碍了对成功预测机制的理解。我们发现，目标价格过程定义不当可能因包含历史信息而导致预测失去意义。资产价格预测中常用的三分类问题通常可分解为波动率预测与方向预测。当仅依赖价格过程时，方向预测性能并不显著。然而，成交量不平衡性能够有效提升方向预测性能。