Spring 2022

SEM217: Jose Blanchet, Stanford

Tuesday, April 26th @ 11:00-12:30 PM

Distributionally Robust Portfolio Selection and Estimation

Jose Blanchet, Stanford

Abstract: The focus of this talk is on decision-making rules that are designed to be min-max optimal. The decision-maker chooses a policy class (e.g. affine or even non-parametric classes) and chooses a member of the policy class by playing a (min-max) game against an adversary that chooses a...

SEM217: Hubeyb Gurdogan, CDAR

Tuesday, April 5th @ 11:00-12:30 PM

Bias reduction in optimized portfolios through Multiple Anchor Point Shrinkage (MAPS) Hubeyb Gurdogan, CDAR


Abstract: Estimation error in a covariance matrix distorts optimized portfolios, and the effect is pronounced when the number of securities p exceeds the number of observations n. In the HL regime where p >> n, we show that a material component of the distortion can be attributed to optimization biases that correspond to the constraints used to construct the portfolio. Using Multiple Anchor Point Shrinkage (MAPS) for...

SEM217: Jeongyoun Ahn, University of Georgia

Tuesday, April 19th @ 11:00-12:30 PM

Detecting Outliers in HDLSS Data

Jeongyoun Ahn, University of Georgia


Abstract: High-throughput data are usually a product of long and complex experiments in laboratories or fields. Due to the multi-step process when generating data, a concern for possible contamination in high-dimensional data is naturally more severe than low-dimensional counterparts. We propose a new two-stage procedure for detecting multiple outliers when the dimension of the data...

Spring 2022

SEM217: Roger Stein, NYU

Tuesday, April 12th @ 11:00-12:30 PM

Making sense of diagnostic performance when information is limited

Roger Stein, NYU


Abstract: In machine learning, drug trials and other domains that involve binary outcomes, it is common to measure the power of a predictive model by constructing an ROC curve and calculating the area under this curve. However, in some cases, it may be difficult to understand the AUC under imperfect conditions. We present results that provide bounds on the AUC in a number of such settings....

SEM217: Agostino Capponi, Columbia

Tuesday, March 15th @ 11:00-12:30 PM (ONLINE)

Robo-Advising: Personalization and Goals-Based Investing

Agostino Capponi, Columbia


Abstract: Robo-advising encompasses any form of algorithmic advice offered to clients. We begin by presenting a dynamic optimization framework based on human-machine interactions, where robo-advisors personalize their portfolios to the clients they serve. We characterize the interaction frequency which strikes the optimal balance between frequent interactions to learn clients'...

SEM217: Sanjiv Das, Santa Clara University

Tuesday, March 29th @ 11:00-12:30 PM (ONLINE)

Multimodal Machine Learning at Scale: Democratizing AI for Academic Research in Finance

Sanjiv Das, Santa Clara University


Abstract: Data analytics is mostly geared towards tabular data (numerical and categorical). Humans form decisions using not only tabular data but also make judgments based on text they read, such as news, reports, etc. Econometrics and Machine learning have been used successfully on...

SEM217: Alex Braun, University of St. Gallen

Tuesday, March 1st @ 11:00-12:30 PM (ONLINE)

Common Risk Factors in the Cross Section of Catastrophe Bond Returns

Alex Braun, University of St. Gallen


Abstract: Catastrophe bonds are an alternative asset class with high excess returns, for which no factor pricing model has emerged to date. We analyze the cross section of catastrophe bond returns for the complete market between 2001 and 2020. Our empirical results show that, of all known coupon and yield spread determinants, only (seasonal) event risk significantly...

SEM217: Kathleen Houssels, Aspire Ten25

Tuesday, March 8th @ 11:00-12:30 PM

ESG Alpha - Is the Deck Stacked Against It?

Kathleen Houssels, Aspire Ten25


Abstract: Billions of dollars continue to flow into ESG strategies as academics and practitioners seek to understand the alpha potential of ESG investments. We evaluate the question of ESG alpha by looking at a popular ESG index fund to see how its alpha potential stacks up against the extra cost investors pay for its ESG features.

SEM217: Ricardo Fernholz, Claremont McKenna College

Tuesday, February 1st @ 11:00-12:30 PM (ONLINE)

The Universality of Zipf's Law

Ricardo Fernholz, Claremont McKenna College

Abstract: A set of data with positive values follows a Pareto distribution if the log–log plot of value versus rank is approximately a straight line. A Pareto distribution satisfies Zipf's law if the log–log plot has a slope of −1. Since many types of ranked data follow Zipf's law, it is considered a form of universality. We show that time-dependent systems with growth and variance parameters that...