Hybrid Vector Auto Regression and Neural Network Model for Order Flow Imbalance Prediction in High Frequency Trading

Paper: arXiv 2411.08382

Abstract

In high frequency trading, accurate prediction of Order Flow Imbalance (OFI) is crucial for understanding market dynamics and maintaining liquidity. This paper introduces a hybrid predictive model that combines Vector Auto Regression (VAR) with a simple feedforward neural network (FNN) to forecast OFI and assess trading intensity. The VAR component captures linear dependencies, while residuals are fed into the FNN to model non-linear patterns, enabling a comprehensive approach to OFI prediction. Additionally, the model calculates the intensity on the Buy or Sell side, providing insights into which side holds greater trading pressure. These insights facilitate the development of trading strategies by identifying periods of high buy or sell intensity. Using both synthetic and real trading data from Binance, we demonstrate that the hybrid model offers significant improvements in predictive accuracy and enhances strategic decision-making based on OFI dynamics. Furthermore, we compare the hybrid models performance with standalone FNN and VAR models, showing that the hybrid approach achieves superior forecasting accuracy across both synthetic and real datasets, making it the most effective model for OFI prediction in high frequency trading.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric modeling (VAR) and neural networks, indicating high mathematical density, and demonstrates rigor through real-world trading data from Binance, comprehensive model comparisons, and specific metrics for strategy evaluation.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict Order Flow Imbalance OFI in HFT"] --> B["Data Collection & Preprocessing"]
  B --> C{"Model Formulation"}
  
  C --> D["VAR Component<br>Captures Linear Dependencies"]
  C --> E["Residual Analysis<br>Extracts Non Linear Patterns"]
  
  D --> F["FNN Component<br>Models Non Linear Residuals"]
  E --> F
  
  F --> G["Hybrid VAR FNN Model<br>Combines Linear & Non Linear Predictions"]
  
  G --> H["Key Findings & Outcomes"]
  
  H --> I["Superior Accuracy<br>Hybrid outperforms standalone VAR and FNN"]
  H --> J["Trading Intensity Insights<br>Identifies Buy vs Sell Pressure"]
  H --> K["Strategic Utility<br>Facilitates HFT Strategy Development"]
  
  style A fill:#e1f5fe
  style H fill:#f3e5f5

Cited References (40)

1. A vector-autoregression analysis of credit and liquidity factor dynamics in US LIBOR and Euribor swap markets (2012) — link
2. Asymmetric heavy-tailed vector auto-regressive processes with application to financial data (2020) — link
3. Asymmetric post earnings announcement drift and order flow imbalance: The impact on stock market returns (2024) — link
4. Bond futures and order imbalance (2013) — link
5. Capturing the Order Imbalance with Hidden Markov Model: A Case of SET50 and KOSPI50 (2020) — link
6. Capturing the Order Imbalance with Hidden Markov Model: A Case of SET50 and KOSPI50 (2020) — link
7. Cross-impact of order flow imbalance in equity markets (2023) — link
8. Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book (2023) — link

1 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.