A decentralized exchange, or DEX, is an application deployed on a blockchain that allows investors to exchange digital assets. We focus on the most prominent type of DEX, an Automated Market Maker (AMM), where at pricing terms are determined by a preset exchange rate formula. This technology has several unique features, including accessibility to all investors, transparency of pricing, and near simultaneity of execution and settlement. In particular, trading via a DEX is feasible for any asset tokenized on a blockchain. In turn, given that assets such as stocks and bonds could be easily tokenized, it is particularly important to understand the risks posed by DEXs. This paper examines both the benefits and risks for investors from DEXs, explores the role of private and public liquidity pools, and analyzes possible regulatory approaches.

Business sentiment is a closely watched economic signal, but measuring it is slow and costly: surveys reach only a few hundred firms, arrive periodically, and take time to compile. We show that large language models hold the potential to address these shortcomings. We prompt an LLM to role-play as the CFO of a specific company at a specific date and focus on the economic-optimism question on the Duke-Federal Reserve CFO Survey over 2002-2025. We find that the LLM reproduces individual human responses: the predicted optimism score significantly forecasts the CFO's actual answer, surviving firm and year-quarter fixed effects and a control for the most recent prior response. Predictive accuracy increases with the amount of information supplied, as both respondent history and firm characteristics improve fit, and the relationship persists under quarterly aggregation. With appropriate conditioning, LLMs may be able to serve as credible digital twins of executives, offering scalable, high-frequency expectations data for financial research and policy.

We propose a new systematic method for detecting the current economic regime and show how to use this information for predicting returns. Rather than presupposing a set of possible regimes, we rely on economic state variables and determine at which historical dates the values of these variables were most similar. To establish our position in an asset today, we identify historically similar periods and measure subsequent performance of the asset. If the historical performance is positive, we initiate a long position; conversely, if it is negative, we initiate a short position. We illustrate the efficacy of our method on six common long-short equity factors over 1985-2024. Our results show that our regime classification leads to significant outperformance. Interestingly, we also find important information in what we call anti-regimes-periods in the past that are the most dissimilar to today.

Disagreement among investors is central to modern theories of trading and asset prices, yet it is fundamentally unobservable. We review the empirical literature that measures it, which now spans dozens of proxies drawn from analyst forecasts, return volatility, trading and short interest, institutional holdings, option prices, and, more recently, text and machine learning methods. We organize this literature around the constructs these proxies are meant to capture, their data requirements, and the alternative interpretations that limit each one. A recurring theme is that the leading proxies are only weakly correlated with one another in the cross section of firms, so empirical conclusions about disagreement can depend heavily on which proxy is used. We discuss why this pattern arises, survey recent efforts to combine proxies and to discipline the choice among them and offer practical guidance for researchers who measure disagreement or who must assess findings that rest on a particular proxy.

Conventional growth indices suffer from two important shortcomings. First, stocks that are anti-value (very expensive) are not necessarily growth stocks. The decision to include a stock in a growth index should be based on fundamental growth measures, such as growth in sales, profits, or R&D spending, rather than price-based measures. Second, when these indices are weighted by objective measures of growth, rather than by market value, performance markedly improves. Overpaying for growth is unhelpful. We also assert that some stocks with poor growth prospects and unattractive valuations may have no place in either value or growth indices.

Decentralized exchanges (DEXs) present challenges for regulation and investor protection; nevertheless, they also present many opportunities for disintermediation and transparency. We explore the past and present state of DEXs, and argue that regulators ought to recognize the both their risks and promises, pursuing oversight strategies that balance flexibility and investor protection.

We analyze a panel of over 28,400 S&P 500 return forecasts by CFOs to examine whether the extent of CFOs' miscalibration-providing forecast confidence intervals that are too narrow-decreases over time. We find no improvement with task repetition nor evidence of learning, that is, no improvement in response to past performance. Across CFOs, miscalibration appears to be a persistent personal trait. We find some evidence that the degree of miscalibration is related to birth cohort and stock market familiarity.

The advent of cryptocurrencies and digital assets holds the promise of improving financial systems by offering cheap, quick, and secure transfer of value. However, it also opens up new payment channels for cybercrimes. Assembling a diverse set of public on- and off-chain, proprietary, and hand-collected data, including attacker–victim negotiations and dark web conversations in Russian, we present an initial anatomy of crypto-enabled cybercrimes, highlighting relevant economic issues and proposing areas for future research. Among others, we find ransomware, as the most dominant organized crypto-enabled cybercrime, entails criminal gangs that operate like firms who adopt modern revenue models and carefully manage their reputations. We suggest that blanket restrictions on cryptocurrency usage may prove counterproductive. Instead, blockchain transparency enables effective forensics for tracking, monitoring, and shutting down dominant cybercriminal organizations, which potentially facilitates a more secure and reliable crypto ecosystem in the longer term. This paper was accepted by David Simchi-Levi, finance. Funding: This work was supported by the National University of Singapore (NUS), Ripple’s University Blockchain Research Initiative (UBRI), the Israel Science Foundation (ISF), and the Cornell FinTech Initiative. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03691 .

The standard approach to portfolio selection involves two stages: forecast the asset returns and then plug them into an optimizer. We argue that this separation is deeply problematic. The first stage treats cross-sectional prediction errors as equally important across all securities. However, given that final portfolios might differ given distinct risk preferences and investment restrictions, the standard approach fails to recognize that the investor is not just concerned with the average forecast error - but the precision of the forecasts for the specific assets that are most important for their portfolio. Hence, it is crucial to integrate the two stages. We propose a novel implementation utilizing machine learning tools that unifies the expected return generation process and the final optimized portfolio. Our empirical example provides convincing evidence that our end-to-end method outperforms the traditional two-stage approach. In our framework, each investor has their own, endogenously determined, efficient frontier that depends on risk preferences, investor-specific constraints, as well as exposure to market frictions. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

The question of whether, when, and how to hedge foreign exchange (FX) risk has been a vexing one for investors since the end of the Bretton Woods system in 1973. Our study provides a comprehensive empirical analysis of dynamic FX hedging strategies over several decades, examining various domestic and foreign currency pairs. Although traditional approaches often focus on risk mitigation, we explore the broader implications for expected returns, highlighting the interplay between hedging and strategies such as the carry trade. Ourfindings reveal that incorporating additional factors-such as trend (12-month FX return), value (deviation from purchasing power parity), and carry (interest rate differential)-into hedging decisions delivers significant portfolio benefits. By adopting a dynamic, active approach to FX hedging, investors can enhance returns and manage risk more effectively than with static hedged or unhedged strategies.

Over the last decade, the green shoots of a new economic order have emerged as decentralized technologies challenge traditional financial systems. Decentralized finance (DeFi) holds the potential to transform international business (IB) by offering accessible financial services across borders, disrupting traditional intermediaries, and promoting financial inclusion. While traditional fintech has challenged banks, DeFi operates outside legacy systems, leveraging blockchain technology and smart contracting to introduce a new range of products and services that provide first-movers with an upper hand to both expand their business across the globe as well realize cost savings on existing business. Despite offering advantages like efficiency, transparency, and security, DeFi faces regulatory uncertainties and scalability, adoption, and stability concerns. Our study explores how DeFi can seamlessly integrate into the IB space while addressing these challenges. In addition to offering insights for investors, multinational firms, and regulators, we also lay the groundwork for future IB research in the fintech domain. As the DeFi innovation unfolds, understanding and harnessing its potential can empower stakeholders to engage responsibly and effectively in this transformative landscape.