Clearing time randomization and transaction fees for auction market design

ArXiv ID: 2405.09764 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: periodic auction, limit order book, market efficiency, transaction fees, price impact, Equity

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical modeling, including optimal stopping theory and game-theoretic analysis for auction design, which indicates high math complexity. It is empirically validated with real-world data from stocks (Alphabet and Apple), demonstrating backtest-ready implementation, though the data processing appears less extensive than a full trading system backtest.

  flowchart TD
    A["Research Goal<br>Determine optimal mechanisms<br>for periodic auction markets"] --> B["Identify Key Problem<br>Strategic traders delay orders<br>to exploit information"]
    B --> C["Methodology<br>Model auction dynamics<br>with time-varying arrival rates"]
    C --> D{"Data Inputs<br>Alphabet & Apple stock data<br>Empirical order arrival patterns"}
    D --> E["Computational Process<br>Simulate price impact<br>under different designs"]
    E --> F["Proposed Remedies<br>1. Randomize closing time<br>2. Optimize transaction fees"]
    F --> G["Simulation & Estimation<br>Calibrate model & test<br>efficiency gains"]
    G --> H["Key Findings<br>Remedies reduce delay incentive<br>Enhance market efficiency"]