Fall 2023

SEM217: Priya Donti, MIT: Optimization-in-the-loop ML for energy and climate

Tuesday, September 26th @ 11:00-12:30 PM via ZOOM

Addressing climate change will require concerted action across society, including the development of innovative technologies. While methods from machine learning (ML) have the potential to play an important role, these methods often struggle to contend with the physics, hard constraints, and complex decision-making processes that are inherent to many climate and energy problems. To address these limitations, I present the...

SEM217: Alex Shkolnik, UC Santa Barbara: On the Markowitz Enigma for Minimum Variance

Tuesday, September 19th @ 11:00-12:30 PM, 648 Evans Hall [ZOOM]

The Markowitz enigma entails the observation (by R. Michaud) that risk minimizers are, fundamentally, “estimation-error maximizers”. No exception to this principle, is principal component analysis (PCA), which is often used to construct equity risk models. We show that a PCA constructed minimum variance portfolio displays highly counterintuitive properties as more securities are added. For example, the ratio of the...

SEM217: Shota Ishii, ProssimoTech: Optimizing Financial Supply Chains - a Network Modeling Approach

Tuesday, September 12th @ 11:00-12:30 PM, 648 Evans Hall

Pandemics, labor shifts, and geopolitics all represent significant long-term challenges for the global economy — and this shift toward a more uncertain world is leading to significant re-alignment of supply chains. Currently, most modeling is focused around physical supply chains, but tremendous opportunities lie in unleashed "locked up cash" in financial supply chains -- i.e. the web of payments across multiple buyers and suppliers. However, this requires the ability to model the flow of capital and risk through a complex...

SEM217: Martin Lettau, UC Berkeley: High-Dimensional Factor Models and the Factor Zoo

Tuesday, Aug 29th @ 11:00-12:30 PM, 648 Evans Hall

This paper proposes a new approach to the “factor zoo” conundrum. Instead of applying dimension-reduction methods to a large set of portfolios that are obtained from sorts on characteristics, I construct factors that summarize the information in characteristics across assets and then sort assets into portfolios according to these “characteristic factors”. I estimate the model on a data set of mutual fund characteristics. Since the data set is 3-dimensional (characteristics of funds over time), characteristic factors are based on a tensor...

SEM217: CANCELLED

Tuesday, September 5th @ 11:00-12:30 PM, 648 Evans Hall