IVE: Enhanced Probabilistic Forecasting of Intraday Volume Ratio with Transformers (2411.10956v2)

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.

• The paper introduces IVE, a novel model leveraging a Transformer encoder-decoder with probabilistic forecasting to accurately predict intraday volume ratios for VWAP strategies.
• It employs comprehensive feature engineering by integrating statistical volume metrics and time encoding to capture long-term dependencies in high-frequency market data.
• Experimental results demonstrate lower RMSE and MAE than traditional RNN, LSTM, and BiLSTM models, achieving a 4.82 basis point execution advantage in live trading tests.

An Analysis of IVE: Enhanced Probabilistic Forecasting of Intraday Volume Ratio with Transformers

The paper "IVE: Enhanced Probabilistic Forecasting of Intraday Volume Ratio with Transformers" explores a significant advancement in financial modeling, specifically focusing on the prediction of intraday volume ratios critical for Volume-Weighted Average Price (VWAP) execution strategies. This paper leverages a Transformer-based architecture to refine prediction accuracy and incorporates diverse data features, contributing to both the theoretical and practical domains in quantitative finance and machine learning.

Key Contributions and Methodology

The authors propose a novel model named IVE (Intraday Volume Estimator) that diverges from conventional methodologies. Key innovations include the application of a Transformer encoder-decoder architecture enriched with statistical volume properties, time-related features, and stock-specific information. The paper underscores the application of probabilistic forecasting, utilizing a distribution head that allows the model to predict means and standard deviations of volume ratios. This probabilistic approach delivers more comprehensive market insights, particularly useful for navigating the stochastic nature of financial markets.

Significant knowledge is drawn from a detailed feature engineering approach that includes an array of volume-related metrics like accumulated volume and turnover rate. Its integration with time encoding via both time embedding and the adoption of a full day's historical context captures long-term dependencies in financial data. The application of this enriched data within a Transformer model reflects a sophisticated attempt to improve financial predictions while acknowledging the high-volatility and liquidity divergences across markets.

Experimental Evaluation and Results

The evaluation of the IVE model stretches across various dimensions utilizing high-frequency trading data from the Korean and U.S. markets. The results denote superior predictive accuracy as evidenced by lower RMSE and MAE values compared to established baselines such as RNN, LSTM, and BiLSTM models. For the Korean market, the IVE model achieved an RMSE of 0.2028 and MAE of 0.1229, showcasing proficiency in capturing high-frequency market dynamics more effectively than prior models. Marginal yet meaningful improvements were observed in the U.S market as well.

Practical Application and Implications

In practical trading applications, live tests conducted in the Korean market—through real-time operations over an extensive period—validate the IVE model's efficacy. The model was able to outpace established VWAP benchmarks, achieving an average execution performance advantage of 4.82 basis points. Such outcomes highlight the model's significant application potential in algorithmic trading, providing a robust tool for traders to enhance execution strategies and minimize transaction costs.

Possibilities and Future Directions

The findings from the paper open avenues for augmented predictive systems that blend machine learning with quant finance strategies. The demonstrated ability of the IVE model to predict volume spikes and its observed correlation with market volatility is a pivotal leap. Further research could focus on expanding the feature set to include more varied market indicators that could enhance decision-making accuracy. Additionally, refining the system's probabilistic elements to better handle non-stationarities intrinsic to financial data is crucial. Exploring real-time adaptability to new market conditions would mark a notable endeavor in enhancing trading strategies.

In conclusion, the work presented in this paper constitutes a substantive contribution to the domain of financial volume prediction using advanced machine learning techniques. By integrating Transformer architectures with probabilistic forecasting in financial strategy execution, this research potentially impacts the future of high-frequency trading and the broader frontier of AI applications in finance.