IVE: Enhanced Probabilistic Forecasting of Intraday Volume Ratio with Transformers

Paper: arXiv 2411.10956

Abstract

This paper presents a new approach to volume ratio prediction in financial markets, specifically targeting the execution of Volume-Weighted Average Price (VWAP) strategies. Recognizing the importance of accurate volume profile forecasting, our research leverages the Transformer architecture to predict intraday volume ratio at a one-minute scale. We diverge from prior models that use log-transformed volume or turnover rates, instead opting for a prediction model that accounts for the intraday volume ratio’s high variability, stabilized via log-normal transformation. Our input data incorporates not only the statistical properties of volume but also external volume-related features, absolute time information, and stock-specific characteristics to enhance prediction accuracy. The model structure includes an encoder-decoder Transformer architecture with a distribution head for greedy sampling, optimizing performance on high-liquidity stocks across both Korean and American markets. We extend the capabilities of our model beyond point prediction by introducing probabilistic forecasting that captures the mean and standard deviation of volume ratios, enabling the anticipation of significant intraday volume spikes. Furthermore, an agent with a simple trading logic demonstrates the practical application of our model through live trading tests in the Korean market, outperforming VWAP benchmarks over a period of two and a half months. Our findings underscore the potential of Transformer-based probabilistic models for volume ratio prediction and pave the way for future research advancements in this domain.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced Transformer architectures with probabilistic distribution heads (Student’s t-distribution) and log-normal data transformations, demonstrating high mathematical complexity. Empirical rigor is strong, featuring live trading tests over 2.5 months, specific performance metrics (outperforming VWAP benchmarks), and details on data features and model architecture suitable for implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Accurate<br>Intraday VWAP Volume<br>Ratio Prediction"] --> B["Data Preparation &<br>Feature Engineering"]
  
  subgraph B ["Data Preparation & Feature Engineering"]
      B1["Raw Volume Data"]
      B2["Log-Normal Transformation"]
      B3["External Features &<br>Stock Characteristics"]
  end

  B --> C["Model: Encoder-Decoder<br>Transformer Architecture"]
  
  subgraph C ["Model: Encoder-Decoder<br>Transformer Architecture"]
      C1["Encoder: Processes Input Features"]
      C2["Decoder: Predicts Volume Ratio"]
  end

  C --> D["Probabilistic Forecasting<br>Mean & Standard Deviation"]
  
  D --> E{"Outcomes"}
  
  E --> F["High Accuracy<br>on High-Liquidity Stocks<br>KR & US Markets"]
  E --> G["Live Trading Agent<br>Outperforms VWAP Benchmark<br>Korean Market 2.5 Months"]

Cited References (19)

1. Optimal Execution: A Review (2022) — link
2. VWAP execution as an optimal strategy (2015) — link
3. Dynamic Execution of VWAP Orders with Short-Term Predictions (2013) — link
4. A Theory of Trading Volume (1986)
5. Intraday Seasonalities and Nonstationarity of Trading Volume in Financial Markets: Individual and Cross-Sectional Features (2016) — link
6. Volume Prediction With Neural Networks (2019) — link
7. Quintet Volume Projection (2019) — link
8. Intraday volume percentages forecasting using a dynamic SVM-based approach (2016) — link

0 of 19 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.