Quarterly Lecture Series – Fall 2013 – Fall 2019

Fall 2019

Professor Vojislav Maksimovic: The Rise of Star Firms: Intangible Capital and Competition:

Abstract: There is a divergence in the returns of top-performing firms and the rest of the economy, especially in industries that rely on a skilled labor force, raising concerns of their market power. We show that the divergence is explained by the mismeasurement of intangible capital. In fact, star firms produce more per dollar of invested capital, have higher growth, innovation, and productivity and are not differentially affected by exogenous competitive shocks than other firms. Their pricing power supports their high intangible capital investment. Some exceptional firms may pose concerns due to their potential to foreclose competition in the future.

Winter 2018

Professor Albert “Pete” Kyle: Toward a Fully Continuous Exchange:

Abstract: In modern financial markets, some of the main problems associated with high-frequency trading and dark pools arise because transactions occur in continuous time but in discrete quantities and at discrete prices. This CAFIN lecture will propose a radically new design for financial exchanges that overcome such problems by allowing continuous prices and quantities.
A pioneer in modeling price formation and information aggregation in financial markets, Professor Kyle served on the faculty at Princeton, UC Berkeley and Duke University
before accepting his current position in 2006.
Download flyer for Toward a Fully Continuous Exchange here.

Fall 2017

Yan Chen: “Recommending teams promotes pro-social lending in online microfinance”:

Abstract: We reported the results of two large-scale field experiments designed to test the hypothesis that group membership can increase participation and pro-social lending for an online crowdlending community, Kiva. The experiment used variations on a simple email manipulation to encourage Kiva members to join a lending team, testing which types of team recommendation emails are most likely to get members to join teams as well as the subsequent impact on lending. We found that emails do increase the likelihood that a lender joins a team, and that joining a team increases lending in a short window following our intervention. The impact on lending is large relative to median lender lifetime loans. We also found that lenders are more likely to join teams recommended based on location similarity rather than team status. Our results suggested that team recommendation can be an effective behavioral mechanism to increase pro-social lending. Yan Chen is the Daniel Kahneman Collegiate Professor of Information at the University of Michigan, and Distinguished Visiting Professor of Economics at Tsinghua University. Her research interests include market and mechanism design, behavioral and experimental economics, and information economics. Chen has published in leading economic journals, such as the American Economic Review, Journal of Political Economy, Journal of Economic Theory, Journal of Public Economics, and Games and Economic Behavior. She has also published in conference proceedings in computer science, such as CHI and WSDM. She serves as an associate editor of Management Science, an advisory editor of Games and Economic Behavior, and an associate editor of Experimental Economics.

Spring 2017

Jonathan Wright: “Extracting Density Forecasts from Asset Prices”:

Abstract: This talk discussed methods for extracting risk-neutral and physical density forecasts for macro-finance variables, notably interest rates, inflation and exchange rates. The practical uses that can be made from these densities was discussed. Special attention was given to risks of deflation and/or high inflation, and their implications for asset pricing. There was also a discussion of measuring the effects of Federal Reserve forward guidance, especially at the zero lower bound.
Download flyer for Extracting Density Forecasts from Asset Prices here.

Darrell Duffie: “Efficient Contracting in Network Financial Markets”:

Abstract: We model bargaining in over-the-counter network markets over the terms and prices of contracts. Of concern is whether bilateral non-cooperative bargaining is sufficient to achieve efficiency in this multilateral setting. For example, will market participants assign insolvency-based seniority in a socially efficient manner, or should bankruptcy laws override contractual terms with an automatic stay? We provide conditions under which bilateral bargaining over contingent contracts is efficient for a network of market participants. Examples include seniority assignment, close-out netting and collateral rights, secured debt liens, and leverage-based covenants. Given the ability to use covenants and other contingent contract terms, central market participants efficiently internalize the costs and benefits of their counterparties through the pricing of contracts. We provide counterexamples to efficiency for less contingent forms of bargaining coordination.

Spring 2016

Mike West: “Bayesian Predictive Synthesis”:

Abstract:  In this talk, Mike West, Duke University, discusses Bayesian reasoning and resulting methodology for model and forecast comparison, calibration, and combination. Beginning with a foundational perspective that in part responds to recent “pragmatic” modelling developments in macroeconomic forecasting,  I revisit a theoretical framework for model and forecast uncertainty assessment that links back to 1980s/90s literatures cutting across statistics, economics, management science and other fields.  Fast-forwarding and updating to 2016 sees the emergence of Bayesian  predictive synthesis (BPS)– a coherent theoretical basis for combining multiple forecast densities, whether from models, individuals, or other sources, and extending existing forecast pooling and Bayesian model mixing methods. Time series extensions are implicit dynamic latent factor models, allowing adaptation to time-varying biases, mis-calibration, and dependencies among models or forecasters. Bayesian simulation-based computation enables implementation. A macroeconomic time series study highlights insights into dynamic relationships among synthesized forecast densities, as well as the potential for improved forecast accuracy at multiple horizons.

Fall 2015

Ingrid Werner: “Dark Pool Trading Strategies and Diving into Dark Pools”:

Abstract: In this talk, Professor Ingrid Werner, OSU, analyzed the role of dark pools in a fragmented market based on a theoretical model. She discussed factors that drive volume away from the lit and into the dark pools and discuss the consequences of dark trading for market quality. She used a unique dataset on dark trading to empirically assess the role of dark pools in U.S. equity markets.

Winter 2015

Rajnesh Mehra: “The Macroeconomic Determinants of Financial Predictability.”:

Abstract: In this paper Mehra took a first pass at formalizing the underlying questions and articulating a pertinent theory for predictability. The fundamental construct he employed is the well-known family of dynamic stochastic general equilibrium macroeconomic models that form the foundation of business cycle theory, growth theory, monetary theory, and nearly every other inter-temporal equilibrium construct whose attributes can be conveniently related to the data. He showed that under fairly general assumptions the neo classical growth model implies that the stochastic process characterizing equity returns is stationary and mean reverting. This forms the theoretical underpinnings of predictability.

Spring 2014

Terrence Hendershott: “High Frequency Trading and the 2008 Short Sale Ban”:

Abstract: Professor Terrence Hendershott of the Haas School of Business at UC Berkeley spoke on “High Frequency Trading and the 2008 Short Sale Ban.” In the research on which his talk is based, Hendershott and collaborators examined the effects of high-frequency traders (HFTs) on liquidity and price efficiency using the September 2008 short sale ban as an example.
Read more about the High Frequency Trading lecture on UCSC’s news page.

Fall 2013

Ila Patnaik: “Did QE Unleash a Monetary Tsunami?”

Abstract: Emerging Market policymakers have claimed that Quantitative Easing (QE) unleashed a monetary tsunami on their financial markets. Academic studies, however, have so far found only small or even ambiguous effects. In part, this may have been because these studies examine the impact on capital flows, exchange rates, or interest rates separately, even though policymakers have clearly been referring to the combined pressure, which has been absorbed in many different ways. Patnaik proposed a summary measure, a modern version of the Exchange Market Pressure (EMP) approach, which adds up the pressure absorbed in various markets based on their exchange rate change equivalents. Patnaik found that the initiation of QE1 and QE2 typically generated 2-3 months of unusually high exchange market pressure in emerging economies. On average, nearly 40 per cent of the pressure was absorbed by exchange rate changes, about 50 percent by intervention.
Slides: Did QE Unleash a Monetary Tsunami: Download