The 15th Annual R/Finance Conference 2023
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The fifteenth annual R/Finance conference for applied finance using R will be held on May 19 and 20, 2023 in Chicago, IL, at the University of Illinois at Chicago.
The conference brings together experienced R users in the field to discuss quantitative finance – covering R (or Python or Julia!), portfolio construction, statistics, and more! Some of the topics that will be covered include advanced risk tools, decentralized finance, econometrics, high-performance computing, market microstructure, portfolio management, and time series analysis.
All will be discussed within the context of using R and other programming languages as primary tools for financial model development, portfolio construction, risk management, and trading.
The keynote speakers for this year’s conference are:
Dr. Chandni Bhan
Dr. Chandni Bhan serves as the Global Head of Quantitative Research and Model Risk at Morgan Stanley Investment Management (MSIM).
Dr. Carlos Carvalho
Carlos Carvalho is a La Quinta Centennial Professor of statistics at The University of Texas McCombs School of Business. His research focuses on Bayesian statistics in high-dimensional problems with applications ranging from finance to genetics.
Dr. Peter Cotton
Peter currently leads data science for Intech Investments where he works on the theory and practice of portfolio construction. Peter is also the creator and maintainer of various things “microprediction” (packages, books and live probability exchange) and the author of a book on the topic published by MIT Press.
Dr. Sam Savage
Dr. Sam L. Savage is the author of The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty (John Wiley & Sons, 2009, 2012), and Chancification: How to Fix the Flaw of Averages (2022). He is a cofounder of the Discipline of Probability Management, Executive Director of ProbabilityManagement.org, a 501(c)(3) nonprofit devoted to the communication and calculation of uncertainty, and is the inventor of the SIP (Stochastic Information Packet) a standardized data structure for the communication of uncertainty.
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