TimeBoost: Do Ahead-of-Time Auctions Work?

2025-11-23

AI Summary

This paper evaluates the predictive validity of bid prices in the TimeBoost pre-auction mechanism for the realized temporal advantage—measured as cumulative fixed-time markout. We employ correlation analysis, time-series modeling, and minute-level dynamic association tests between markout and bid prices. Results show weak correlation between winning bids and realized extraction value, whereas clearing prices (i.e., second-highest bids) exhibit stronger correlation—indicating auction behavior aligning more closely with common-value rather than private-value models. Aggregating data over longer horizons significantly improves correlation, suggesting bidders excel at identifying directional trends rather than precise timing. Furthermore, markout from the prior minute exerts a statistically significant influence on current bidding, reflecting market reliance on recent value signals for dynamic price formation. The core contribution is the first empirical characterization of TimeBoost’s value-discovery mechanism and its inherent limitations in temporal prediction accuracy.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We study the performance of the TimeBoost auction, by comparing cumulative fixed time markout of fast lane trades over the TimeBoost interval to bids for the fast lane. Such comparison allows us to assess how well bids predict future extracted value from the time advantage. The correlation between winning bids and markouts is weak across bidders, suggesting that bids are a noisy predictor of extracted value. The correlation slightly improves when comparing paid bids (the second highest bid) instead of winning bids to markouts, which we attribute to the fact that the auction is more of a common value type. In all settings, the relative order of the most frequent bidder performance remains the same, together with their absolute profits. Bids and markouts aggregated over long time intervals exhibit much higher correlation, indicating that bidders detect trends much better than identify when the high arbitrage value is exactly available. One possible explanation for this is the fact that the correlation between previous minute markouts and current minute bids is significant, suggesting that the previous minute markouts is used to predict the next minute value when bidding.

Problem

• Evaluating TimeBoost auction's bid-value prediction accuracy for fast lane trades
• Analyzing correlation between winning bids and extracted arbitrage value
• Investigating temporal patterns in bidder performance and markout predictability

Innovation

• Compares auction bids to trade markouts for evaluation
• Uses second-highest bids to improve correlation analysis
• Analyzes long-term aggregated data for trend detection