Paper: arXiv 2503.09988

Abstract

In financial trading, return prediction is one of the foundation for a successful trading system. By the fast development of the deep learning in various areas such as graphical processing, natural language, it has also demonstrate significant edge in handling with financial data. While the success of the deep learning relies on huge amount of labeled sample, labeling each time/event as profitable or unprofitable, under the transaction cost, especially in the high-frequency trading world, suffers from serious label imbalance issue.In this paper, we adopts rigurious end-to-end deep learning framework with comprehensive label imbalance adjustment methods and succeed in predicting in high-frequency return in the Chinese future market. The code for our method is publicly available at https://github.com/RS2002/Label-Unbalance-in-High-Frequency-Trading .

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on practical deep learning methods and mentions public code and real market data (Chinese futures), indicating high empirical rigor, while the mathematical depth is limited to standard ML concepts like cost matrices and sampling techniques.

Research Flowchart

  flowchart TD
  A["Research Goal: Predict HFT Returns<br>in Chinese Futures Market"] --> B["Data: Tick-level Futures Data"]
  B --> C["Problem: Severe Label Imbalance<br>Due to Transaction Costs"]
  C --> D["Method: End-to-End Deep Learning<br>with Label Imbalance Adjustment"]
  D --> E["Computational Process: Training<br>with Adjusted Loss Functions"]
  E --> F["Key Finding: High Prediction Accuracy<br>on Chinese Futures Market"]
  F --> G["Outcome: Public Code Release<br>on GitHub"]

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0 of 26 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.