學術論文

We demonstrate that a broad set of asset pricing factors/anomalies are significantly exposed to “noise trader risk,” and the noise trader risk is priced in factor premia. We first confirm that mutual funds’ flow-induced trading of factors are uninformed, as they generate a large price impact on factor returns, followed by a complete reversal. We then show that asset pricing factors are subject to flow-driven noise trader risk in that expected variation (covariation) of flow-induced noise trading strongly forecasts variance (covariance) of factor returns. Importantly, factor premia are higher when flow-driven noise trader risk is expected to be more salient.

Problem definition: Scant empirical research studies the impact of drug shortages on the quality of medical care in hospitals. We study the causal relationship between drug shortages and medication errors using a natural experiment: hurricane damage to factories that produce heparin, an essential medication used frequently in hospitals. Methodology/results: We collect data on medication errors from the U.S. Food and Drug Administration’s Adverse Event Reporting System and drug sales from IQVIA’s National Sales Perspective. Applying the synthetic control method, we find that hurricane-related heparin supply disruptions increased medication error rates by 152%. In addition, we find significant spillover effects. The disruption increased medication error rates of a substitute drug, enoxaparin, by about 114%. Managerial implications: Our study uses an exogenous event to show that medication supply chain disruptions may negatively impact hospitals’ quality of care. We contribute to the literature by empirically linking the effects of supply chain disruptions to downstream service quality. Our results show that commonly used measures to mitigate the impact of drug shortages, such as substituting medications, may be unsafe. We discuss several measures that hospital managers may consider implementing to mitigate the potentially harmful effects of drug shortages.

This article develops a conformal prediction method for classification tasks that can adapt to random label contamination in the calibration sample, often leading to more informative prediction sets with stronger coverage guarantees compared to existing approaches. This is obtained through a precise characterization of the coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through a new calibration algorithm. Our solution can leverage different modelling assumptions about the contamination process, while requiring no knowledge of the underlying data distribution or of the inner workings of the classification model. The empirical performance of the proposed method is demonstrated through simulations and an application to object classification with the CIFAR-10H image data set.

This study investigates the operations of a novel service model, mobile charging, in which an e-commerce platform operator dispatches trucks equipped with charging piles to provide charging services for customers’ electric vehicles with low battery levels. We model the platform-customer interaction with a Stackelberg game and explicitly characterize customers’ optimal charging decisions under the platform’s various service plans. In a general scenario that involves charging requests within a transportation network, we develop a joint optimization model for the platform’s pricing and service fulfillment, utilizing an elaborately constructed augmented network. In addition, we explore a localized subproblem where multiple orders are concentrated within a specific region. With a simplified model structure, we propose an approximation algorithm with provable performance guarantees and further theoretically evaluate the resource consumption and associated platform benefits for serving the orders in each region. The results enable the batching of neighboring demands within the general scenario as a specialized node, resulting in a streamlined network with fewer nodes. Additionally, we can enhance the algorithm’s efficiency within the general framework through strategic prioritization of node visitations, leveraging the analytical findings. Furthermore, the insights derived can offer recommendations for the deployment of mobile charging and the selection of target areas in the initial stages. Overall, our study provides comprehensive guidelines and valuable insights for mobile charging operations.

Are landlocked countries at risk from sea-level rise? We identify a new mechanism where natural disaster shocks influence countries’ macroeconomic performance through cross-border trade spillovers. Analyzing global data on climate disasters, infrastructure, trade, and the macroeconomy from 1970 to 2019, we find that climate disasters impacting ports, critical infrastructure for international trade, reduce imports, exports, and economic output in both the affected country and its major trade partner (both upstream and downstream) countries. The GDP effects on main upstream and downstream countries are as large as those in directly impacted countries: While directly affected countries offset climate disaster damages with increased government spending and investment, trade partners do not. Effective adaptation efforts, including building climate-resilient infrastructure and implementing disaster relief measures, must account for the cross-border spillover effects of climate disasters.

We analyze a single-factor setting in which there is private information regarding cash flows as well as their betas. Private information about betas, together with market makers’ risk aversion and mean betas’ nonnegativity, implies a nonlinear price schedule whose stochastic slope covaries positively with order flow when the expected factor payoff is positive and vice versa. We predict a negative relation between the covariance and expected returns and an attenuation of the beta anomaly in asset returns after accounting for this relation. Empirical tests confirm these predictions.

We uncover the major drivers of macro aggregates and the real exchange rate at business cycle frequencies in Group of Seven countries. The estimated drivers of key macro variables resemble each other and account for a modest fraction of the real exchange rate variances. Dominant drivers of the real exchange rate are orthogonal to main drivers of business cycles, generate a significant deviation of the uncovered interest parity condition, and lead to small movements in net exports. We use these facts to evaluate international business cycle models accounting for the dynamics of both macro aggregates and the real exchange rate.

We re-examine monetary policy spillovers to Emerging Market Economies (EME) in the form of capital flow reversals, using sectoral-level securities holdings data for Euro Area investors. In response to a surprise monetary tightening, active investors such as investment funds re-balance their portfolios away from EME, while more passive, long term investors such as insurance funds and banks exhibit no significant reaction on average. For active investors, the reallocation out of EME appears stronger under synchronized monetary tightening between the Fed and the ECB. However, these investors may even inject more capital to EME securities when the monetary tightening surprises contain positive news about the Euro Area economy. Issuers’ monetary–fiscal stability may explain the heterogeneous impact of these spillovers.

In contemporary data analysis, it is increasingly common to work with non-stationary complex data sets. These data sets typically extend beyond the classical low-dimensional Euclidean space, making it challenging to detect shifts in their distribution without relying on strong structural assumptions. This paper proposes a novel o ine change-point detection method that leverages classiers developed in the statistics and machine learning community. With suitable data splitting, the test statistic is constructed through sequential computation of the Area Under the Curve (AUC) of a classier, which is trained on data segments on both ends of the sequence. It is shown that the resulting AUC process attains its maxima at the true change-point location, which facilitates the change-point estimation. The proposed method is characterized by its complete nonparametric nature, high versatility, considerable exibility, and absence of stringent assumptions on the underlying data or any distributional shifts. Theoretically, we derive the limiting pivotal distribution of the proposed test statistic under null, as well as the asymptotic behaviors under both local and xed alternatives. The localization rate of the change-point estimator is also provided. Extensive simulation studies and the analysis of two real-world data sets illustrate the superior performance of our approach compared to existing model-free change-point detection methods.