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Clearing time randomization and transaction fees for auction market design

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arxiv 2405.09764 v2 pith:KX6RQYRC submitted 2024-05-16 q-fin.TR math.OC

classification q-fin.TRmath.OC
keywords auctionmarketperiodicpricecontinuousefficiencystrategicavailable
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Flaws of a continuous limit order book mechanism raise the question of whether a continuous trading session and a periodic auction session would bring better efficiency. This paper wants to go further in designing a periodic auction when both a continuous market and a periodic auction market are available to traders. In a periodic auction, we discover that a strategic trader could take advantage of the accumulated information available along the auction duration by arriving at the latest moment before the auction closes, increasing the price impact on the market. Such price impact moves the clearing price away from the efficient price and may disturb the efficiency of a periodic auction market. We thus propose and quantify the effect of two remedies to mitigate these flaws: randomizing the auction's closing time and optimally designing a transaction fees policy for both the strategic traders and other market participants. Our results show that these policies encourage a strategic trader to send their orders earlier to enhance the efficiency of the auction market, illustrated by data extracted from Alphabet and Apple stocks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Regulation or Competition:Major-Minor Optimal Liquidation across Dark and Lit Pools

    q-fin.MF 2025-09 reject novelty 6.0 of 10

    A dynamic make-take fee and compensation scheme is constructed for optimal liquidation across lit and dark pools and is claimed to reduce market impact relative to a competitive major-minor market.

  2. Learning Market Making with Closing Auctions

    q-fin.TR 2026-01 conditional novelty 5.0 of 10

    A neural-fitted Q-learning market maker that anticipates the closing auction beats Avellaneda-Stoikov and TWAP benchmarks on mean returns in the paper's simulations.

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