The Price of Processing: Information Frictions and Market Efficiency in DeFi
This paper examines price discovery in DeFi markets during hacks, highlighting information processing delays and strategic informed trading.
What it examines
This paper studies how asset prices adjust when a crypto hack occurs, focusing on the delay between on-chain data and public announcements. It uses high-frequency event analysis to examine how expensive information processing limits immediate price updates, revealing that sophisticated traders act before the news becomes common knowledge.
What it concludes
The results indicate that significant price declines happen before public disclosure, as skilled traders exploit early signals. This research can help monitor risks in both digital and traditional finance, highlighting cybersecurity impacts and suggesting future studies on market transparency and financial stability.
Evidence objects
Study reveals dynamic price discovery in decentralized finance as $36%$ of total $27%$ drop occurs before public announcements, indicating market participants with advanced processing act prior to common knowledge readily.
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Innovative approach incorporates a high-frequency event study with precise on-chain and social media timing, alongside a strategic three-period theoretical model including information processing costs, representing a novel empirical design effectively.
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Paper distinguishes between raw public information and common knowledge, while acknowledging rigorous methodology with limitations such as 49 hack events and dependency on social media channels, impacting market regulation implications.
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This paper presents an original examination of market efficiency in DeFi by contrasting raw blockchain data with social media knowledge. Its novel empirical design and focus on processing costs reveal unique price discovery mechanisms, exploiting discrete hack timings to uncover broader financial implications, making it a compelling, groundbreaking, and remarkably significant study.
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Raw abstract and provenance
- … Our goal is to understand how the informed trader chooses their trades and how market makers set prices, ultimately showing how the probability of information disclosure (…
Source row: 1984 · abstract type: snippet