We study the performance and information acquisition behavior of mutual funds for both their long and short positions. We show that managers acquire relatively more information about their shorts because the benefit of acquiring information about shorts is larger.

This paper uses transaction-level import data at the shipment level to examine how multinational companies importing to the US have restructured their supply chains during the COVID-19 pandemic. We find that companies sourced from fewer locations, reduced the share of imports from China, and increased the share of imports from other Asian countries, such as India and Vietnam, and North American countries, such as Canada and Mexico. For managers, our results imply that a one-size-fits-all mentality regarding supply chain disruption responses is not appropriate, and companies’ disruption-response strategies need to be tailored to individual supply chains’ circumstances.

This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.

General Partners (GPs) in private equity face a trade-off between focusing their skills and effort on fewer investments to earn higher returns, or investing more broadly to reduce risk through diversification. Using a novel, deal-level dataset of 5,925 global investments from 1999 to 2016, we show that these portfolio considerations are important for understanding fund-level private equity returns.

This paper presents the development, validation, and implementation of a data-driven optimization model designed to dynamically plan the assignment of anesthesiologists across multiple hospital locations within a large multi-specialty healthcare system. We formulate the problem as a multi-stage robust mixed-integer program incorporating on-call flexibility to address demand uncertainty. The optimized dynamic staffing plan has been successfully implemented in the University of Pittsburgh Medical Center healthcare system, leading to estimated annual cost savings of 12\% compared to current practice, or about \$800,000 annually.

Health care costs in the United States make up a larger proportion of gross domestic product (GDP) than in any other developed country and continue to rise. We examine whether the use of consistent metrics in costing information systems across hospitals provides one avenue to reduce these costs. We refer to such consistency as “costing information consistency” or CIC and empirically measure it by identifying whether hospitals in a multihospital system share the same costing system vendor. Using M&A activity among vendors as an instrument for exogenous changes in hospital CIC, we find that CIC is associated with a 13.3% reduction in operating expenses, suggesting that increased cost comparability from CIC helps hospitals identify ways to reduce operating expenses by identifying clinical and administrative best practices.

The 2022 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was awarded to Ben S. Bernanke, Douglas W. Diamond, and Philip H. Dybvig “for research on banks and financial crises”. This article surveys the contributions of the three laureates and discusses how their insights have changed the way that academics and policymakers understand banks and their roles in financial crises.