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Building trust takes time: limits to arbitrage for blockchain-based assets

Review of Finance 2024 28(4), 1345-1381 open access
A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible only by reinstalling trusted intermediation, which mitigates default risk

Overcoming Arbitrage Limits: Option Trading and Momentum Returns

Journal of Financial and Quantitative Analysis 2024 59(1), 97-120
Momentum profits depend mainly on the short leg and therefore on barriers to short sales. Our research indicates that the decline in momentum profitability in the past 2 decades is driven partly by a contemporaneous growth in stock options trading. Stock options offer an alternative to short selling, augmenting the stock lending market, and thereby contributing to improved pricing efficiency. The resulting reduction in barriers to short sales contributes to lower returns to momentum trading from the short leg. Our results persist after matching stocks with and without options based on different firm-level characteristics.

Double Machine Learning: Explaining the Post-Earnings Announcement Drift

Journal of Financial and Quantitative Analysis 2024 59(3), 1003-1030
We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance