The financial industry has eagerly adopted machine learning algorithms to improve on traditional predictive models. In this paper we caution against blindly applying such techniques. We compare forecasting ability of machine learning methods in evaluating future payoffs on synthetic variance swaps.
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates.
As deep learning and big data increasingly shape modern artificial intelligence (AI) tools, it is essential to consider the broader impact of integrating AI into workplaces. While AI applications can optimize processes and improve productivity, their long-term effects on workers’ learning curves and overall performance are still underexplored. This paper investigates the intricate relationship between AI-enabled technology and workers’ learning dynamics through a large-scale randomized field experiment conducted on the Instacart platform.
A February cyberattack targeting Change Healthcare resulted in the most extensive healthcare data breach to date, raising questions about industrywide risk management and regulation.
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed-frequency time series panel data structures.
Chief Economist Gerald Cohen outlines mid-year updates to our 2023 economic forecasts, discussing which EMAs have changed since our January projections.
Ignoring consideration sets in modeling customer purchase decisions may lead to biased estimation of customer preferences, yet consideration sets are difficult to infer in brick-and-mortar contexts. We show that the challenge of estimating consideration set models in brick-and-mortar contexts can partially be overcome with an emerging source of data: “heatmap data” collected using in-store sensors.
We consider a firm that can use one of several costly learning modes to dynamically reduce uncertainty about the unknown value of a project. Each learning mode incurs cost at a particular rate and provides information of a particular quality. In addition to dynamic decisions about its learning mode, the firm must decide when to stop learning and either invest or abandon the project.
We are now in the age of Big, and, seemingly, ever Bigger Data. The current public discussion focuses on the avalanche of data, due to fact that nearly all written (and other) materials are now available in a digital format, which simplifies their accessibility, extraction, classification, and analysis. Even more so, the adoptions of online digital platforms create new and ever-larger data quantities every day. While created for other purposes the potential for scientific socio-economic research appears simultaneously extremely promising and extremely uncertain – very much like answers in search of good questions.
This article introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails.
Most organizational leaders have come to recognize that hiring and retaining a diverse workforce is a business imperative. But many struggle to achieve their diversity goals. In this Kenan Insight, we explore how organizations can measure their “organizational equity” — that is, their internal distribution of power and resources — and build a diverse workforce that leads to greater organizational success.
Despite strong economic indicators—2.5% GDP growth, unemployment under 4%, and easing inflation—American consumer sentiment remains low. Kenan Institute experts explore why the public's mood doesn’t match the upbeat data, highlighting deeper sources of economic unease.
Abstract The data boom in e-commerce has spurred AI-powered marketplace analytics, but platforms hold the data reins. Some adopt open data-access policies with third-party analytics providers (e.g., permitting data-scraping or...
Sharecare, the digital health company that helps people manage all their health in one place, and The University of North Carolina at Chapel Hill’s Center for the Business of Health announced the results of the North Carolina Well-Being Data Analysis Competition, a student competition designed to drive local insights around well-being in North Carolina.
Learning from negative outcomes is of fundamental interest to scholars. Yet most research in this area explores learning from actual outcomes. By contrast, we add to the literature by setting forth a theoretical framework that highlights learning from the anticipation of negative outcomes rather than actual outcomes. Using an inductive, multiple case research design, we develop an emergent typology for how anticipatory learning occurs.
In Never Stop Learning, behavioral scientist and operations expert Bradley R. Staats describes the principles and practices that comprise dynamic learning and outlines a framework to help you become more effective as a lifelong learner. Replete with the most recent research about how we learn as well as engaging stories that show how real learning happens, Never Stop Learning will become the operating manual for leaders, managers, and anyone who wants to keep thriving in the new world of work.
More than four years since the start of the COVID-19 pandemic, we examine the essential elements that build small-business resilience, emphasizing the importance of personal fortitude and intangible resources in ensuring business survival.
Much has been written about the disproportionate number of women who have suffered pandemic-related job losses during COVID-19, but a related consequence has not been as well explored: the serious disruption of women’s careers, particularly in fields in which “path dependence” matters for success. In this Kenan Insight, we examine this more subtle asymmetry in the pandemic’s impact as indicative of far broader issues for women’s advancement in the workplace.