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on Market Microstructure |
| By: | Pablo D. Azar; Sergio Olivas; Nish Sinha |
| 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 |
| JEL: | G12 G14 G18 G23 L86 |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednsr:99907 |
| By: | Ran, Ling |
| Abstract: | This paper examines the evolving landscape of cross-border trading enabled by blockchain technology and artificial intelligence (AI). It explores the mechanisms through which blockchain decentralizes trading infrastructure, enhances transaction transparency, and eliminates intermediaries to reduce operational costs. AI integration is analyzed in the context of high-frequency trading, focusing on real-time data processing, algorithmic decision-making, and smart contract automation. The discussion addresses technical and regulatory barriers, including algorithmic failures, cyber threats, jurisdictional discrepancies, and integration complexity. The paper evaluates the resulting shifts in market liquidity, compliance strategies, fraud mitigation, and overall trading efficiency. The convergence of blockchain and AI is framed as a paradigm shift in financial technology infrastructures, with both opportunities and limitations in scalability, regulation, and cost of deployment. The findings suggest a potential for optimized trade execution and autonomous risk-adjusted decision systems under constrained legal and technical environments. |
| Date: | 2025–04–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:knz94_v1 |
| By: | Simon Caspar Zeller; Paul-Niklas Ken Kandora; Daniel Kirste; Niclas Kannengie{\ss}er; Steffen Rebennack; Ali Sunyaev |
| Abstract: | Concentrated liquidity automated market makers (AMMs), such as Uniswap v3, enable liquidity providers (LPs) to earn liquidity rewards by depositing tokens into liquidity pools. However, LPs often face significant financial losses driven by poorly selected liquidity provision intervals and high costs associated with frequent liquidity reallocation. To support LPs in achieving more profitable liquidity concentration, we developed a tractable stochastic optimization problem that can be used to compute optimal liquidity provision intervals for profitable liquidity provision. The developed problem accounts for the relationships between liquidity rewards, divergence loss, and reallocation costs. By formalizing optimal liquidity provision as a tractable stochastic optimization problem, we support a better understanding of the relationship between liquidity rewards, divergence loss, and reallocation costs. Moreover, the stochastic optimization problem offers a foundation for more profitable liquidity concentration. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16542 |
| By: | Toomas Laarits; Jeffrey Wurgler |
| Abstract: | Browser data from an approximately representative sample of individual investors offers a detailed account of their search for information, including how much time they spend on stock research, which stocks they research, what categories of information they seek, and when they gather information relative to events and trades. The median individual investor spends approximately six minutes on research per trade on traded tickers, mostly just before the trade; the mean spends around half an hour. Individual investors spend the most time reviewing price charts, followed by analyst opinions, and exhibit little interest in traditional risk statistics. Aggregate research interest is highly correlated with stock size, and salient news and earnings announcements draw more attention. Individual investors have different research styles, and those that focus on short-term information are more likely to trade more speculative stocks. |
| JEL: | G02 G11 G12 G4 G40 G41 G5 G50 G53 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33625 |