Spring 2018

SEM217: Jeffrey Bohn, Swiss Re: Digitally-driven change in the insurance industry—disruption or transformation?

Tuesday, August 2nd @ 12:30-2:00 PM (1011 Evans Hall)

Digitally-driven change in the insurance industry—disruption or transformation?

Jeffrey Bohn, Swiss Re

Abstract: As technology continues to insinuate itself into all facets of financial services, the insurance industry faces a slow-motion parade of promise, possibilities, prematurity, and pared-down expectations. Digitization, the birth of InsurTech, machine intelligence, and the collection & curation of (orders of magnitude)...

SEM217: John Wu, LBL: Could Probability of Informed Trading Predict Market Volatility?

Tuesday, April 19th @ 12:30-2:00 PM (1011 Evans Hall)

Could Probability of Informed Trading Predict Market Volatility?

John Wu, LBL

Significant market events such as Flash Crash of 2010 undermine the trust of the capital market system. An ability to forecast such events would give market participants and regulators time to react to such events and mitigate their impact. For this reason, there have been a number of attempts to develop early warning indicators. In this work, we explore...

SEM217: Ananth Madhavan, Blackrock: Factor Strategies: Crowding, Capacity and Sources of Active Returns

Tuesday, March 8th @ 12:30-2:00 PM (1011 Evans Hall)

Factor Strategies: Crowding, Capacity and Sources of Active Returns

Ananth Madhavan, Blackrock

We develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics, which is especially useful when factor loadings significantly vary over time. In comparison, standard regression approaches assume the factor loadings are constant over a particular window. Applying the methodology to a dataset of U.S.-...

SEM217: Nick Gunther, UC Berkeley: The Financing Rate Implied by Equity Futures

Tuesday, March 22nd @ 11:00-12:30 PM (1011 Evans Hall)

The Financing Rate Implied by Equity Futures

Nick Gunther, UC Berkeley

This talk will explore the cost of implicit leverage associated with an S&P 500 Index futures contract and derive an implied financing rate. While this implicit financing rate was often attractive relative to market rates on explicit financings, the relationship between the implicit and explicit financing rates was volatile and varied considerably based on...

Nick Gunther, UC Berkeley: The Financing Rate Implied by Equity Futures

This talk will explore the cost of implicit leverage associated with an S&P 500 Index futures contract and derive an implied financing rate. While this implicit financing rate was often attractive relative to market rates on explicit financings, the relationship between the implicit and explicit financing rates was volatile and varied considerably based on legal and economic regimes. Among other findings, regulatory reform in 2000 appeared to reduce significantly the spreads between this implicit financing rate and contemporaneous Eurodollar and US Treasury rates.

Start date: 2018-...

Ananth Madhavan, Blackrock: Factor Strategies: Crowding, Capacity and Sources of Active Returns

We develop a methodology to estimate dynamic factor loadings using cross-sectional risk characteristics, which is especially useful when factor loadings significantly vary over time. In comparison, standard regression approaches assume the factor loadings are constant over a particular window. Applying the methodology to a dataset of U.S.-domiciled mutual funds we distinguish the components of active returns attributable to (1) constant factor exposures, for example, a tilt to value stocks; (2) time-varying factor exposures; and (3) security selection. We find large-cap growth funds tend...

John Wu, LBL: Could Probability of Informed Trading Predict Market Volatility?

Significant market events such as Flash Crash of 2010 undermine the trust of the capital market system. An ability to forecast such events would give market participants and regulators time to react to such events and mitigate their impact. For this reason, there have been a number of attempts to develop early warning indicators. In this work, we explore one such indicator named Probability of Informed Trading (typically shorten as PIN) and its variants. In an earlier test, a variant known as VPIN was demonstrated to show a strong signal more than an hour before the Flash Crash of 2010....

Jeffrey Bohn, Swiss Re: Digitally-driven change in the insurance industry—disruption or transformation?

Abstract: As technology continues to insinuate itself into all facets of financial services, the insurance industry faces a slow-motion parade of promise, possibilities, prematurity, and pared-down expectations. Digitization, the birth of InsurTech, machine intelligence, and the collection & curation of (orders of magnitude) more structured & unstructured data are changing (and will continue to change) the industry in material ways—not always in line with predictions. This presentation describes (from a large insurer’s perspective) trends and challenges related to how technology...

Ulrike Malmendier, UC Berkeley: The Long-lasting Effects of Propaganda on Financial Risk-Taking

We argue that emotional coloring of experiences via political propaganda has long-term effects on risk taking. We show that living in an anti-capitalist system reduces individuals' willingness to invest in the stock market even decades later. Utilizing a large comprehensive data set of 300,000 clients of a German discount broker, we find that even today East Germans invest less in the stock market, both at the extensive and the intensive margin, are more likely to hold stocks of communist countries, and are less likely to hold stocks of capitalist institutions and countries. Effects are...

George Papanicolaou, Stanford: Statistical Arbitrage

Statistical arbitrage is a collection of trading algorithms that are widely used today but can have very uneven performance, depending on their detailed implementation. I will introduce these methods and explain how the data used as trading signals are prepared so that they depend weakly on market dynamics but have adequate statistical regularity. The trading algorithm itself will be presented and then a well calibrated version of it will be used on daily SP500 data from 2003-2014. Well calibrated means that the risk associated with this trading algorithm can be identified and controlled...