The Price of Processing: Information Frictions and Market Efficiency in DeFi

Pablo Azar, Sergio Olivas and Nish Sinha

No 1153, Staff Reports from Federal Reserve Bank of New York

Abstract: This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices.

Keywords: information asymmetry; price discovery; common knowledge; information processing costs; market microstructure; event study; high-frequency data; cryptocurrency; DeFi; cybersecurity hacks; market efficiency (search for similar items in EconPapers)
JEL-codes: G12 G14 G18 G23 L86 (search for similar items in EconPapers)
Pages: 32
Date: 2025-04-01
New Economics Papers: this item is included in nep-mst and nep-pay
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DOI: 10.59576/sr.1153

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