Authors: Pablo Azar, Sergio Olivas, and Nish D. Sinha
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Staff Reports
The Price of Processing: Information Frictions and Market Efficiency in DeFi
Number 1153
April 2025
April 2025
JEL classification: G12, G14, G18, G23, L86
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.
Author Disclosure Statement(s)
Pablo D. Azar
I want to disclose that I own:
1. Profits interests in Algorand Inc. and affiliated entities valued at more than $10,000.
2. Less than $10,000 worth of Ripple and Monero in a Binance account.
Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Nish Sinha
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Sergio Olivas
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
I want to disclose that I own:
1. Profits interests in Algorand Inc. and affiliated entities valued at more than $10,000.
2. Less than $10,000 worth of Ripple and Monero in a Binance account.
Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Nish Sinha
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Sergio Olivas
The author declares that he has no relevant or material financial interests that relate to the research described in this paper. Prior to circulation, this paper was reviewed in accordance with the Federal Reserve Bank of New York review policy, available at https://www.newyorkfed.org/research/staff_reports/index.html.
Suggested Citation:
Azar, Pablo D., Sergio Olivas, and Nish D. Sinha. 2025. “The Price of Processing: Information Frictions and Market Efficiency in DeFi.” Federal Reserve Bank of New York Staff Reports, no. 1153, April. https://doi.org/10.59576/sr.1153
Azar, Pablo D., Sergio Olivas, and Nish D. Sinha. 2025. “The Price of Processing: Information Frictions and Market Efficiency in DeFi.” Federal Reserve Bank of New York Staff Reports, no. 1153, April. https://doi.org/10.59576/sr.1153
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