Paper: arXiv 2508.06914

Authors: Ying Peng, Yifan Zhang, Xin Wang

Abstract

In this paper, we mainly focus on the prediction of short-term average return directions in China’s high-frequency futures market. As minor fluctuations with limited amplitude and short duration are typically regarded as random noise, only price movements of sufficient magnitude qualify as statistically significant signals. Therefore data imbalance emerges as a key problem during predictive modeling. From the view of data distribution imbalance, we employee the mean-uncertainty logistic regression (mean-uncertainty LR) classification method under the sublinear expectation (SLE) framework, and further propose the mean-uncertainty support vector machines (mean-uncertainty SVM) method for the prediction. Corresponding investment strategies are developed based on the prediction results. For data selection, we utilize trading data and limit order book data of the top 15 liquid products among the most active contracts in China’s future market. Empirical results demonstrate that comparing with conventional LR-related and SVM-related imbalanced data classification methods, the two mean-uncertainty approaches yields significant advantages in both classification metrics and average returns per trade.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced nonlinear expectation theory (sublinear expectation) and develops novel mean-uncertainty LR/SVM methods with theoretical proofs, indicating high math complexity. Empirically, it uses real high-frequency futures data (top 15 products) for backtesting investment strategies and reports performance metrics against benchmarks, showing strong data/implementation rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict Short-Term Avg.<br>Return Directions in<br>China's Futures Market"] --> B["Data & Problem<br>High-Freq. Futures & Limit Order Book Data<br>Data Imbalance Issue"]
  B --> C["Methodology<br>Mean-Uncertainty Logistic Regression<br>Under SLE Framework"]
  B --> D["Methodology<br>Mean-Uncertainty Support Vector Machines<br>Under SLE Framework"]
  C & D --> E["Investment Strategy<br>Based on Prediction Results"]
  E --> F["Findings<br>Superior Classification Metrics &<br>Higher Avg. Returns vs. Conventional Methods"]

Cited References (30)

  1. How and When are High-Frequency Stock Returns Predictable? (2022)
  2. Short-term stock price trend prediction with imaging high frequency limit order book data (2024)
  3. Attention-based CNN–LSTM for high-frequency multiple cryptocurrency trend prediction (2024)
  4. Do high-frequency financial data help forecast oil prices? The MIDAS touch at work (2015)
  5. Stock market volatility forecasting: Do we need high-frequency data? (2021)
  6. Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities (2005)
  7. Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model (2024)
  8. SMOTE: synthetic minority over-sampling technique (2002)
and 22 more
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  2. Efficient hybrid oversampling and intelligent undersampling for imbalanced big data classification (2024)
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  4. Undersampling method based on minority class density for imbalanced data (2024)
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0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.