with Michael Pagano and John Sedunov
Finance Research Letters, Forthcoming.
Abstract: Using data on stocks held by individual investors at retail brokerage firm Robinhood, we document that these investors are actively engaged in both momentum and contrarian trading strategies. In response to the increased volatility and uncertainty in financial markets due to the COVID-19 pandemic in March 2020, we find that retail investors reduce momentum trading and increase contrarian trading activity during the initial phase of this crisis. We also find that the impact of Robinhood investors on several measures of market quality varied depending on market conditions, coinciding with better market quality during less-stressful periods and worse market quality during the early weeks of the pandemic in the U.S.
Index Membership and Small Firm Financing
with Charles Cao and Matthew Gustafson
Management Science, Volume 65, Issue 9, September 2019, Pages 4156-4178
Abstract: Small public firms are subject to a bank hold-up problem whereby a bank’s information monopoly precludes competition from other financing sources, leading to an overreliance on bank lending and increased borrowing costs. Exploiting quasi-random variation in Russell 2000 index assignment, we find that index membership affects how small public firms obtain financing, in a manner consistent with index membership mitigating the bank hold-up problem. Russell 2000 firms initiate 34% fewer bank loans, conduct more seasoned equity offerings, and obtain 58 basis points lower bank loan spreads than similar firms outside the index. These effects are largest for recent Russell 2000 additions and do not reverse in the year following index deletion. Overall, our findings suggest that index membership creates an information environment that increases the feasibility of non-bank financing and persists for some time after index deletion.
with Tony Kwasnica and Jared Williams
Review of Finance, Volume 23, Issue 2, March 2019, Pages 325–361
Abstract: How do people update their beliefs upon observing others' forecasts? We conduct a series of forecasting experiments to determine whether people can recognize that others can see news that is qualitatively similar, but distinct, from the news that they observe. We document that subjects frequently fail to revise their forecasts even though they should always revise them in our setting. This tendency is most pronounced when subjects learn that another subject observed news that is qualitatively similar ("good" or "bad") to the news that they observed. Our findings reveal concrete situations where forecasts can be expected to be biased.
Finalist for 2018/2019 Pagano/Zechner award for best non-investments paper in the Review of Finance
with Charles Cao and Peter Iliev
Journal of Banking and Finance 78, May 2017, p. 42-57
Abstract: This paper documents that small-cap mutual funds allocate on average 27% of their portfolio to mid- and large-cap stocks. We find that larger and older small-cap funds are more likely to hold mid- and large-cap stocks, consistent with funds straying from their objective over time. Funds that invest heavily in mid- and large-cap stocks expose their investors to unanticipated risks but investors do not experience higher abnormal returns or performance persistence overall. These funds did outperform their peers by 3% annually in the most recent period between January 2003 and March 2010.
with Rabih Moussawi and Ke Shen
Abstract: We study the use of “heartbeat trades” by ETFs in explaining their superior tax efficiency. By relying on the in-kind-redemption exemption rule, authorized participants help ETFs avoid distributing realized capital gains and reduce their tax overhang. In recent years, ETFs end up with 0.92% lower tax burden per year than active mutual funds, partly due to heartbeat trades. Challenged by ETFs’ tax efficiencies, mutual funds exhibit higher flow-tax sensitivity than flow-fee sensitivity. Active mutual funds with relatively higher tax burdens had more outflows from tax-sensitive investors at the same time when ETFs with similar investment styles experienced stronger inflows. Using holdings data of institutions with high-net-worth clients, we find that investment advisors with tax-sensitive investors allocate four times more assets to ETFs than other institutions, representing an important driver behind the overall surge in ETF flows, especially after the increase of capital gains tax rate in 2013. We conclude that the migration of flows from active mutual funds to ETFs is driven primarily by tax considerations.
Are Hedge Fund Capacity Constraints Binding? Evidence on Scale and Competition
with Charles Cao and Tim Simin
Abstract: An important question in hedge fund management is whether hedge funds experience decreasing returns to scale, as hedge fund managers often pursue arbitrage opportunities which are limited and short-lived. Extant literature has presented evidence of decreasing returns to scale at the hedge fund level based on OLS regressions. Employing a recently developed, unbiased estimation method based on recursive demeaning, we find no evidence of decreasing returns to scale at the hedge fund level. However, we do find evidence that hedge fund returns are decreasing in industry size. Further tests suggest that inter-hedge fund competition drives this result. Additionally, we examine the evolution of raw managerial skill of hedge funds over time and find that while fund performance deteriorates as funds grow older, controlling for this deterioration does not mitigate the detrimental effects on performance due to the industry becoming more competitive
Below are a few programs that I wrote for my own research or to replicate other researchers' papers. Most of my code is written in SAS or MatLab. You are free to use my programs, under the condition that you will notify me in case you find any error.
Imports Russell Indexes constituent data and adds CRSP permnos.
SAS program to perform two-stage least squares regression with double clustered standard errors using IML. The model has one regressor and one instrument, and does not include intercepts, as in Pastor, Stambaugh, & Taylor (2015).
The following is a selection of websites and software products that I enjoy using:
Popular Python distribution for data science and machine learning
Tool that provides insights into FOIA requests
Network visualization tool
Form ADV information
High-level programming language for simulations and matrix oriented computations
Online LaTeX editor that allows for collaboration
Statistical software suite that is very fast on large datasets
Access company filings
Collaboration software that facilitates teamwork and logs conversations
Statistical software package that makes regressions easy to perform
Python source code editor with debugger
My research interests lie in the areas of empirical asset pricing, institutional investors, and alternative investments. I have worked on a variety of projects concerning mutual funds, hedge funds, indexing, and investor behavior. Recently, I have started exploring other areas such as crypto-currencies and machine learning as well.
Together with Caitlin Dannhauser, I organize The Robert T. LeClair Finance Department Seminar Series. I am also a member of the VU Women in Tech committee and the Faculty Service Evaluation committee.