SEM217: Lionel Martellini (EDHEC-Risk Institute), Mass Customisation versus Mass Production in Retirement Investment Management: Addressing a “Tough Engineering Problem”
Tuesday, July 25th @ 11:00-12:30 PM (1011 Evans Hall)
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.
SEM217: Michael Ohlrogge, Stanford: Bank Capital and Risk Taking: A Loan Level Analysis
Tuesday, August 29th @ 11:00-12:30 PM (639 Evans Hall)
I examine 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.
SEM217: Robert Anderson, UC Berkeley: Sparse Low Rank Dictionary Learning
Tuesday, September 5th @ 11:00-12:30 PM (639 Evans Hall)
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.
SEM217: Jeremy Evnine, Evnine & Associates: Social Finance and the Postmodern Portfolio: Theory and Practice
Tuesday, September 12th @ 11:00-12:30 PM (639 Evans Hall)
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.
SEM217: Ben Gum, AXA Rosenberg: Machine Learning and Alternative Data in Fundamental-based Quantitative Equity
Tuesday, September 26th @ 11:00-12:30 PM (639 Evans Hall)
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.
SEM217: Nick Gunther, UC Berkeley: The Futures Financing Rate
Tuesday, November 28th @ 11:00-12:30 PM (639 Evans Hall)
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 of the FIR volatility, along with the correlation of the FIR and the underlying stock index, which are required for the convexity adjustment and the specification of confidence intervals.
SEM217: Alexander N D'amour, UC Berkeley: Advances in Basketball Analytics Using Player Tracking Data
Tuesday, October 10th @ 11:00-12:30 PM (639 Evans Hall)
In the 2013-2014 season, the National Basketball League, in conjunction with STATS LLC, implemented a league-wide program to collect player-tracking data for all NBA games. The data feed now provides 25-FPS records of all players' XY coordinates on the court, as well as XYZ coordinates for the ball.
SEM217: David Bailey, UC Davis: Backtest overfitting, stock fund design and forecast performance
Tuesday, October 17th @ 11:00-12:30 PM (639 Evans Hall)
Backtest overfitting means the usage of backtests (historical market data) to construct an investment strategy, fund or portfolio, when the number of variations explored exceeds limits of statistical reliability. We show that backtest overfitting is inevitable when computer programs are employed to explore millions or even billions of parameter variations (as is typical) to select an optimal variant.
SEM217: Samim Ghamami, UC Berkeley and US Treasury Office of Financial Research: Submodular Risk Allocation
Tuesday, October 24th @ 11:00-12:30 PM (639 Evans Hall)
We analyze the optimal allocation of trades to portfolios when the cost associated with an allocation is proportional to each portfolio's risk. Our investigation is motivated by changes in the over-the-counter derivatives markets, under which some contracts may be traded bilaterally or through central counterparties, splitting a set of trades into two or more portfolios.
SEM217: Mathieu Rosenbaum, École Polytechnique: Rough Heston model: Pricing, hedging and microstructural foundations
Tuesday, November 7th @ 11:00-12:30 PM (639 Evans Hall)
It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with a small Hurst parameter, provide very relevant dynamics in order to reproduce the behavior of both historical and implied volatilities.
SEM217: John Arabadjis, State Street: Investor Behavior and Market Dynamics
Tuesday, November 14th @ 11:00-12:30 PM (639 Evans Hall)
The Market is a consensual hallucination that commands attention by wielding its Invisible Hand. In this talk, we will examine the ways that Adam Smith’s 250-year-old appendage makes itself felt – positioning, trading, and hurting herding – and their implications for the investment process.
SEM217: Sveinn Olafsson, UC Santa Barbara: Change-point detection for stochastic processes
Tuesday, September 19th @ 11:00-12:30 PM (639 Evans Hall)
Since the work of Page in the 1950s, the problem of detecting an abrupt change in the distribution of stochastic processes has received a great deal of attention. There are two main formulations of such problems: A Bayesian approach where the change-point is assumed to be random, and a min-max approach under which the change-point is assumed to be fixed but unknown.
SEM217: Kellie Ottoboni, UC Berkeley: Nonparametric Risk Attribution for Factor Models of Portfolios
Tuesday, October 3rd @ 11:00-12:30 PM (639 Evans Hall)
Factor models are used to predict the future returns of a portfolio with known positions in many assets. These simulations yield a distribution of future returns and various measures of the risk of the portfolio.