Events

Chengdu 80 Competition: Team Berkeley

SWUFE & CDAR Symposium 2019

Chengdu 80 is a FinTech design and development competition for academic participants utilizing open source technology. As part of the SWUFE-CDAR annual conference, the Chengdu 80 competition will provide opportunities for students to interact with leading academics in finance and technology, international financial institutions, and government and regulatory leaders. Each participating team will consist of up to 6 students from a single academic institution led by a faculty member or postdoctoral scholar and...

Frank Partnoy, UC Berkeley: The Ratio Problem

ABSTRACT: We describe two problems – omitted variable bias and measurement error – that arise when a ratio is the dependent variable in a linear regression. First, we show how bias can arise from the omission of two variables based on a ratio’s denominator, and we describe tests for the degree of bias. As an example, we show that the familiar “inverse U” relationship between managerial ownership and Tobin’s Q is reversed when omitted variables are included. Second, we show how measurement error in the ratio denominator can lead to bias. We urge caution about using ratios as dependent...

CANCELLED

Start date: 2019-10-29 11:00:00 End date: 2019-10-29 12:30:00 Venue: 1011 Evans Hall Address: 1011 Evans Hall, Berkeley, CA, 94720

Bryan Graham, UC Berkeley: Testing for strategic interaction in social and economic network formation

Seminar slides

Start date: 2019-10-15 11:00:00 End date: 2019-10-15 12:30:00 Venue: 1011 Evans Hall Address: 1011 Evans Hall, Berkeley, CA, 94720

CANCELLED

Start date: 2019-10-22 11:00:00 End date: 2019-10-22 12:30:00 Venue: 1011 Evans Hall Address: 1011 Evans Hall, Berkeley, CA, 94720

CANCELLED

Start date: 2019-10-08 11:00:00 End date: 2019-10-08 12:30:00 Venue: 1011 Evans Hall Address: 1011 Evans Hall, Berkeley, CA, 94720

Ayako Yasuda, UC Davis: Private Company Valuations by Mutual Funds

Mutual funds that invest in private securities value those securities at stale prices. Prices change on average every 2.5 quarters, vary across fund families, and are revised upward dramatically at follow-on funding events. The infrequent, but dramatic price changes yield predictably large fund returns. Fund investors can exploit the stale pricing by buying (selling) before (after) the follow-on funding events (though we find little evidence of this behavior to date). Fund families can opportunistically save up and unleash dry powder (unused markup of private securities) when doing so...

Martin Lettau, UC Berkeley: Characteristics of Mutual Fund Portfolios: Where Are the Value Funds?

This paper provides a comprehensive analysis of portfolios of active mutual funds, ETFs and hedge funds through the lens of risk (anomaly) factors. We show that these funds do not systematically tilt their portfolios towards profitable factors, such as high book-to-market (BM) ratios, high momentum, small size, high profitability and low investment growth. Strikingly, there are almost no high-BM funds in our sample while there are many low-BM “growth” funds. Portfolios of “growth” funds are concentrated in low BM-stocks but “value” funds hold stocks across the entire BM spectrum. In fact,...

Alec Kercheval, Florida State University: Self-excited Black-Scholes models for option pricing

Beginners first learn to price stock options with a simple binomial tree model for random price changes. It is well known that this classical one-dimensional random walk converges weakly to Brownian motion in the proper space-time scaling limit. Actual stock prices changes occur not at regular times but at random times according to the order flow in an electronic limit order book (LOB), and these are observed to have heteroscedastic and self-exciting characteristics. In this talk, we consider random walks in which jumps occur at random times described by an independent general point...

Xiaowu Dai, UC Berkeley: Towards theoretical understanding of large batch training in stochastic gradient descent

Stochastic gradient descent (SGD) is almost ubiquitously used in training non-convex optimization tasks. Recently, a hypothesis by Keskar et al. (2017) that large batch SGD tends to converge to sharp minima has received increasing attention. We justify this hypothesis by providing new properties of SGD in both finite-time and asymptotic regimes, using tools from Partial Differential Equations. In particular, we give an explicit escaping time of SGD from a local minimum in the finite-time regime. We prove that SGD tends to converge to flatter minima in the asymptotic regime (although it may...