Paper: arXiv 2409.14157

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs standard statistical definitions and neural network architectures without deep theoretical derivations, but conducts a thorough empirical analysis using a large, recent dataset (AAPL Nasdaq ITCH) with detailed daily evaluation, cross-validation, and comparisons against naive baselines.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Predict High-Frequency Price Changes"] --> B["Methodology:<br>Deep Learning on Limit Order Book Data"]
  B --> C{"Key Inputs<br>Price Process vs. Volume Imbalance"}
  C --> D["Computational Process:<br>Three-Class Classification<br>Directional vs. Volatility Prediction"]
  D --> E{"Analysis of<br>Predictability Drivers"}
  E --> F["Outcome 1:<br>Price Process Alone<br>Lacks Directional Signal"]
  E --> G["Outcome 2:<br>Volume Imbalance<br>Significantly Improves Prediction"]