I am an Assistant Professor of Finance at the University of Notre Dame's Mendoza School of Business. Prior to this, I spent two years at Harvard Business School as a Post-Doc Fellow with the Behavioral Finance and Financial Stability Initiative. I completed my PhD in Finance at the London School of Economics.
Many asset managers limit the weights of their asset positions in order to ensure diversiﬁcation of active bets. Consequently, an individual manager will react to positive returns in his biggest positions by rebalancing into other assets. I document the pervasiveness of this practice in individual funds, and how trading to rebalance high exposure positions collectively leads to price pressure. A value-weighted strategy exploiting this predictability earns 2.83% (2.60% 4-factors adjusted) per quarter on the largest assets in the cross section of equities; contrasting priors that the demand channel for assets only circumstantially aﬀect stock prices. These results are consistent with trading pressure originating from risk management by individual asset managers.
The Slow Death of Active Management
November 13th, 2017
With Robin Greenwood and Adi Sunderam.
Latest Update: March 2020
Presentations: Ohio State University 2017, American University 2018, University of Delaware 2018, Northeastern University 2018, University of Notre Dame 2018, AQR Capital 2018, FMA Asia 2018, Southern Finance Association Conference 2018, American Finance Association 2019, Midwestern Finance Association Meeting 2019
I use cash-financed mergers to identify how cash payouts from public firms affect the cross-section of the equity market. Following the closure of a merger deal, managers who received cash distributions predictably demand other stocks. This redeployment demand mechanism seems to be at play in general for cash-returns in the form of dividends and stock buybacks. I show that excess returns and patterns of gross-issuance in non-payout stocks can be predicted and explained by their connections to cash returns.
Latest Update: November 10, 2019
With Lauren Cohen, and Umit Gurun.
Presentations: University of Notre Dame Brown Bag (2019), RAPS/RCFS Winter Conference (2020), Consortium on Asset Management in Cambridge (2020), London School of Economics Lunch Time Workshop (2020), FSU SunTrust Beach Conference (2020), 8th Fixed Income and Financial Institutions Conference (2020), 2nd Future of Financial Information Conference at Stockholm Business School (2020)
We provide evidence that mutual fund managers misclassify their holdings, and that these misclassifications have a real and significant impact on investor capital flows. Many funds report more investment grade assets to Morningstar, than are actually held in their portfolios, making these funds appear significantly less risky. This results in pervasive misclassifications across the universe of US fixed income mutual funds. The problem is widespread- resulting in about 30% of funds being misclassified with safer profiles, when compared against their actual, publicly reported holdings. “Misclassified funds” – i.e., those that hold risky bonds, but claim to hold safer bonds– outperform the actual low-risk funds in their peer groups. However, when we correctly classify them based on their actual risk, these funds are mediocre performers. Misreporting is stronger following several quarters of large negative returns, and it is strong at the fund family level.
Last Update July 2019
Accepted at the Journal of Financial Economics
Presentations: AFA 2018, The Miami Behavioral Finance Conference 2017*, The 6th Luxembourg Asset Management Summit, USC Marshall School of Business*, Securities Exchange Commission*
With Lauren Cohen, Dong Lou, Chris Malloy, and Umit Gurun.
Using a novel database that tracks web traffic on the SEC’s EDGAR servers between 2003 and 2016, we show that mutual funds exert effort to reduce the dimensionality of their portfolio selection problem. Specifically, we show that mutual fund managers’ gather information on a very particular subset of firms and insiders, and their surveillance stays largely unchanged over time. This tracking has powerful implications for their portfolio choice, and its information content. An institution that downloaded an insider-trading filling by a given firm last quarter increases its likelihood of downloading an insider-trading filing on the same firm by more than 41.3 % this quarter, which is 8 times larger than the unconditional probability of an institution downloading at least one insider trading filing in a quarter from any firm in her existing portfolio (4.8%). Moreover, the average tracked stock that an institution sells generates 7.5% annualized DGTW- adjusted alpha, whereas the sale of an average non-tracked stock has close to zero DGTW adjusted alpha. The outperformance of tracked trades continues for a number of quarters following the tracked insider/institution sale and does not reverse within the sample period. Collectively, these results suggest that the information in tracked trades is important for fundamental firm value, and is only revealed following the information-rich dual trading by insiders and linked institutions.
April 17, 2016
With Lauren Cohen and Dong Lou.
Published in the Review of Financial Studies.
We explore a new mechanism by which investors take correlated shortcuts, and present evidence that managers undertake actions – in the form of sales management – to take advantage of these shortcuts. Specifically, we exploit a regulatory provision wherein a firm’s primary industry is determined by the highest sales segment. Exploiting this regulation, we provide evidence that investors classify operationally nearly identical firms vastly differently depending on their placement around this sales cut-off. Moreover, managers appear to exploit this by manipulating sales to be just over the cutoff in favorable industries. Further evidence suggests that managers then engage in activities to realize large, tangible benefits from this opportunistic action.