FIFO execution partial identification research paper v3 released
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Riya Danait, Yuliana Zamora, and Ioana Boier released v3 of the FIFO execution partial identification research paper, defining the recovery of tick-level queue history from aggregated L2 limit order book snapshots as a conditional partial identification problem. They constructed a path-preserving compiler that only varies among three cancellation allocation rules: front, quantity-weighted random, and back, while fixing the aggregation path, reconciled market removal volume, potential order partitioning, and new orders. The paper is based on 7 months of synchronized data from the Tokyo Stock Exchange in 2025, selecting two underlying assets with different trading activity levels: 1301.T and 7911.T. Each asset contributes 1080 matched five-minute samples within the same 18 held-out trading days. The study shows that compared to back cancellations, front cancellations increase the pre-terminal completion rate of 1301.T by 8.01 percentage points and reduce the implementation shortfall by 1.010 basis points, with corresponding differences for 7911.T being 7.39 percentage points and 0.384 basis points respectively.
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- arXiv q-finPartial Identification of FIFO Passive Execution under Aggregated Order Books: Different Fill Outcomes from the Same Book
This arXiv paper defines the recovery of tick-level queue history from aggregated L2 limit order book snapshots as a conditional partial identification problem. It constructs a path-preserving compiler that only varies among three cancellation allocation rules—front-of-queue, quantity-weighted random, and back-of-queue—while fixing the aggregated path, reconciled market removals, latent order partitioning, and new order arrivals.
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