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Market-Based Solutions to Vital Economic Issues


Market-Based Solutions to Vital Economic Issues

machine learning


To characterize ambiguity we use machine learning to impose guidance and discipline on the formulation of expectations in a data-rich environment. In addition, we use the bootstrap to generate plausible synthetic samples of data not seen in historical real data to create statistics of interest pertaining to uncertainty. While our approach is generic we focus on robust portfolio allocation problems as an application and study the impact of risk versus uncertainty in a dynamic mean-variance setting. We show that a mean-variance optimizing investor achieves economically meaningful wealth gains (33%) across our sample from 1996-2019 by internalizing our uncertainty measure during portfolio formation.

Join Rethinc. Labs to hear from Seth Lloyd, Professor of Mechanical Engineering and Physics at MIT, as he shares his findings on quantum algorithms for analyzing financial data and predicting time series.

Join our panel of experts who will share their technological, legal and social expertise to answer the questions raised by the real-world performance of risk assessment instruments.

In a Rethinc. Labs webinar, our panel of experts shared their technological, legal and social expertise to answer the questions raised by the real-world performance of risk assessment instruments.

Corporate executives have begun to glimpse the strategic value of incorporating artificial intelligence as an “employee” within their organization. In this Kenan Insight, we explore a framework that outlines the critical elements for harnessing the potential of human-AI working relationships.

The Kenan Institute of Private Enterprise at the University of North Carolina at Chapel Hill will host a virtual conference on machine learning in finance on March 5, 2021. The conference is co-sponsored by the Journal of Financial Econometrics (JFEC) and the International Center for Finance (ICF) at Yale University.

Emerging artificial intelligence (AI) capabilities are ushering in significant changes in how enterprises operate – and raising a host of questions for organizations. In this Kenan Insight, we explore how changing the organizational mindset to treat AI as an “employee” may pave the way to fully reaping the benefits of AI systems.

Faculty Director of the Rethinc. FinTech Lab, Eric Ghysels was featured as the keynote speaker at the 2nd Crypto Asset Lab Conference. The conference, which took place on Tuesday, October 27th, focuses on all aspects of bitcoin and crypto assets, especially those pertaining to investment, banking, finance, monetary economics, and regulation. Topics included cryptocurrency adoption and transition dynamics, digital cash and payment systems, economics and/or game theoretic analysis of cryptocurrency protocols, economic and monetary aspects of cryptocurrencies and the legal, ethical and societal aspects of (decentralized) cryptocurrencies.

The list of stores that have closed or gone bankrupt in 2020 reads like a “who’s who” of venerable retail giants. Although retailing has been experiencing tectonic shifts for several years, the COVID-19 pandemic has accelerated both challenges and opportunities. In this Kenan Insight, we explore four major trends in retail, with a particular focus on food retailing.

Join our panel of industry and academic leaders, who will share their technological, legal, organizational and social expertise to answer the questions raised by emerging artificial intelligence capabilities.

Time series regression analysis in econometrics typically involves a framework relying on a set of mixing conditions to establish consistency and asymptotic normality of parameter estimates and HAC-type estimators of the residual long-run variances to conduct proper inference. This article introduces structured machine learning regressions for high-dimensional time series data using the aforementioned commonly used setting.

Artificial intelligence, or AI, enhancements are increasingly shaping our daily lives. Financial decision-making is no exception to this. We introduce the notion of AI Alter Egos, which are shadow robo-investors, and use a unique data set covering brokerage accounts for a large cross-section of investors over a sample from January 2003 to March 2012, which includes the 2008 financial crisis, to assess the benefits of robo-investing. The man versus machine comparison allows us to shed light on potential benefits the emerging robo-advising industry may provide to certain segments of the population, such as low income and/or high risk averse investors.