arXiv q-fin· Jung Hun Phee, Tucker Balch·· 2 days agoAI Score44
OrderFlowDiT: A Transport-Regularized Diffusion Transformer for Probabilistic Limit Order Supply Prediction
OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting
AI summary
Researchers propose OrderFlowDiT, a conditional diffusion Transformer trained with an unbalanced optimal transport (UOT) auxiliary loss, to predict the conditional distribution of limit order submissions over the next five minutes. Across 1,950 validation conditions covering 30 U.S. stocks, adding UOT reduces the full-field transport cost by 45.8% and the transport energy score by 26.1% compared with training using only mean squared error, with improvements in both metrics for every stock.
Source:arXiv q-fin · arxiv.org
Machine LearningHigh-Frequency and Market MicrostructurePaper Research#Papers / Research#Machine Learning#High-Frequency / Market Microstructure#US Stocks#arXiv