Fall 2017

Nick Gunther, UC Berkeley: The Futures Financing Rate

We estimate the financing rate implicit in equity index futures (“FIR”) by comparing the prices of the near and next contracts and adjusting for expected dividends and convexity. We provide a direct estimate ...

Jeremy Evnine, Evnine & Associates: Social Finance and the Postmodern Portfolio: Theory and Practice

We formulate the portfolio construction problem as a mean/variance problem which includes a linear term representing an investor’s preference for expected “social return”, in addition to her expected “financial return” of the classical theory. By making various assumptions, we are able to exploit the heterogeneous expectations version of the CAPM to derive an equilibrium model which is an extension of the standard Capital Asset Pricing Model. Among other things, the model implies that, in equilibrium, assets with higher expected social return that is valued by investors will have, ceteris...

Ben Gum, AXA Rosenberg: Machine Learning and Alternative Data in Fundamental-based Quantitative Equity

We begin with a survey of machine learning techniques and applications outside of finance. Then we discuss our use of Machine Learning techniques at Rosenberg. Finally, we explore some alternative data sources.

Start date: 2017-09-26 11:00:00 End date: 2017-09-26 12:30:00 Venue: 639 Evans Hall at UC Berkeley Address: 639 Evans Hall, Berkeley, CA, 94720

Michael Ohlrogge, Stanford: Bank Capital and Risk Taking: A Loan Level Analysis

I examine whether whether low capital levels incentivize banks to systematically originate and hold riskier loans. I construct a novel data set consisting of 1.8 million small business and home mortgage loans, matched to the specific banks that originated them and the capital levels of those banks at the time of origination, and verified to be held on bank portfolios, rather than sold. A one point increase in capital ratios (e.g. from 12% to 13%) is associated with a 4% decrease in the default risk of mortgage loans held on portfolio (from a net foreclosure rate of 2.5% to 2.4%). Bank...

Bob Anderson, UC Berkeley: Sparse Low Rank Dictionary Learning

Sparse Dictionary Learning (SDL) can be used to extract narrow factors driving stock returns from a stock returns matrix, provided the returns are generated by sparse factors alone. We describe progress on a variant called Sparse Low Rank Dictionary Learning (SLRDL), designed to simultaneously extract broad and narrow factors for the returns matrix, when the returns are generated by both types of factors.

Start date: 2017-09-05 11:00:00 End date: 2017-09-05 12:30:00 Venue: 639 Evans Hall at UC Berkeley Address: 639 Evans Hall, Berkeley, CA, 94720

Lionel Martellini (EDHEC-Risk Institute), Mass Customisation versus Mass Production in Retirement Investment Management: Addressing a “Tough Engineering Problem”

The seminar will be held at 1011 Evans Hall, UC Berkeley. Abstract: Triggered by the introduction of ever stricter accounting and prudential pension fund regulations, a massive shift from defined-benefit to defined-contribution pension schemes is taking place across the world. As a result of this massive shift of retirement risks on individuals, the investment management industry is facing an increasing responsibility in terms of the need to provide households with suitable retirement solutions. Existing retirement products such as target date funds, annuities and variable annuities suffer...