Fall 2021

SEM217: Simge Ulucam, BlackRock: Transparency and Best Practices are Essential for ESG Investing

Tuesday, August 31st @ 11:00-12:30 PM (ONLINE)

ABSTRACT: Many investors have shifted their asset allocations to account for Environmental, Social, and Governance (ESG) issues. While we welcome this shift from an ethical perspective, the financial and non-financial benefits of ESG investing as well as best practices for portfolio construction are subjects of heated debate. We look at aspects of the debate through a series of practical examples.

SEM217: Markus Pelger, Stanford University: Deep Learning Statistical Arbitrage

Tuesday, September 7th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven and flexible way.

SEM217: Robert Anderson, UC Berkeley: General Equilibrium and Climate Change

Tuesday, September 14th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: General Equilibrium (GE) models are the most natural economic models for studying policies to mitigate human-induced climate change. In particular, they provide a setting in which we can analyze the effects on income distribution of climate mitigation policies such as carbon taxes or cap-and-trade, and develop policies to offset the income distribution effects. Most economic models of climate change are partial equilibrium, and thus have a limited ability to address the effects of climate mitigation policies on income distribution.

SEM217: Alexander Braun, University of St. Gallen: Hurricane Risk and Asset Prices

Tuesday, September 21st @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT:  We examine hurricane exposure as a systematic risk factor in the US stock market. Using a consumption-based asset pricing model with heterogeneous agents subject to uninsurable shocks, we derive a necessary and a sufficient condition for a hurricane risk premium.

SEM217: Xiaowu Dai, UC Berkeley: Learning in Economics and Market Design

Tuesday, September 28th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking the two-sided matching market as a running example, we focus on the decentralized setting, where agents do not share their learned preferences with a central authority.

SEM217: Gustavo Schwenkler, Santa Clara University: News-Driven Peer Co-Movement in Crypto Markets

Tuesday, October 5th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: When large idiosyncratic shocks hit a cryptocurrency, some of its peers experience un- usually large returns of the opposite sign. The co-movement is concentrated among peers that are co-mentioned with shocked cryptos in the news, and that are listed in the same exchanges as shocked cryptos. It is a form of mis-pricing that vanishes after several weeks, giving rise to predictable returns. We propose a profitable trading strategy that exploits this predictability, and explain our results with a slow information processing mechanism.

SEM217: Vikramaditya (Vic) Khanna, University of Michigan: Insuring against wrongdoing? Socially responsible investing by mutual funds

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

ABSTRACT: We examine whether mutual funds increase the level of their socially-responsible investing (SRI) to reduce the negative consequences stemming from alleged wrongful behavior by fund insiders. Relying on a novel hand-collected dataset covering 17 years of funds’ fidelity bond claims (for theft and embezzlement by insiders) and errors and omissions claims (for mistakes) we find that funds significantly increase their SRI in the year of a claim, but not in the year before or after the claim.

SEM217: Emilio Calvano, University of Bologna: Artificial Intelligence, Algorithmic Pricing and Collusion

Tuesday, October 19th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. 

SEM217: Jeff Bohn, One Concern & CDAR: Evaluating commercial property resilience with hybrid physics-based/ machine learning (ML) models

Tuesday, October 26th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: As more property-related data are becoming available, unprecedented granular analyses have become facilitated with hybrid methodologies that incorporate machine learning (ML) into physics-based models. 

SEM217: Daniele Ballinari, University of Basel: From Chatter to Action: An Index of Sustainability Sentiment

Tuesday, November 2nd @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: Investments in sustainable assets have grown at a rapid pace over the last decade, amounting to a third of assets under management in the US today. Theoretical models partially attribute this increase to changes in investor taste for sustainability.

SEM217: Dan diBartolomeo, Northfield: How the Four Horsemen of the Financial Apocalypse Turned Smart Beta into Real Alpha

Tuesday, November 9th @ 11:00 - 12:30 PM (ONLINE)

ABSTRACT: The ongoing COVID 19 pandemic has sharpened the attention of the financial community to the impact of rare but extreme events.   We will begin the presentation with the historical context of research on how four types of large events: War, Pandemic, Corruption, and Climate Change are believed to impact financial markets and the prosperity of both nations and individual households, (diBartolomeo, Journal of Performance Measurement, Spring 2021). 

SEM217: Iordanis Kerenidis, QC Ware: Prospects and challenges of quantum finance

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

ABSTRACT: Quantum computers are expected to have substantial impact on the finance industry, as they will be able to solve certain problems considerably faster than the best known classical algorithms. In this talk we describe such potential applications of quantum computing to finance. We consider quantum speedups for Monte Carlo methods, portfolio optimization, and machine learning. This article is targeted at financial professionals and no particular background in quantum computation is assumed.