PhD Students in the Job Market

 

2019-20

Manasa Gopal | Joseph Kalmenovitz | Sung Lee | Heebum Lee
 
Manasa Gopal
Manasa Gopal
Dissertation Committee:  Philipp Schnabl (Chair), Anthony Saunders, Viral Acharya
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            How Collateral Affects Small Business Lending: The Role of Lender Specialization
ABSTRACT: I study the role of collateral on small business credit access in the aftermath of the 2008 financial crisis. I construct a novel, loan-level dataset covering all collateralized small business lending in Texas from 2002-2016 and link it to the U.S. Census of Establishments. Using textual analysis, I quantify whether a lender is specialized in a borrower's collateral by comparing the collateral pledged by the borrower to the lender's collateral portfolio. I show that post-2008, lenders reduced credit supply by focusing on borrowers that pledged collateral in which the lender specialized. This result holds when comparing lending to the same borrower from different lenders, and when comparing lending by the same lender to different borrowers. A 10% higher specialization in borrower collateral increases lending to the same firm by 1.2%. Abstracting from general equilibrium effects, if firms switched to lenders with the highest specialization in their collateral, aggregate lending would increase by 14.8%. Furthermore, firms borrowing from lenders with greater specialization in the borrower's collateral see a larger growth in employment after 2008. I identify the lender's informational advantage in the posted collateral to be the mechanism driving lender specialization. Finally, I show that firms with collateral more frequently accepted by lenders in the economy find it easier to switch lenders. In sum, my paper shows that borrowing from specialized lenders increases access to credit and employment during a financial crisis.
 
Joseph Kalmenovitz
Joseph Kalmenovitz
Dissertation Committee:  Holger Mueller (co-chair), Philipp Schnabl (co-chair), David Yermack, Andres Liberman
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            Incentivizing Financial Regulators
ABSTRACT: I study how promotion-based tournament incentives within the public sector affect financial regulation. I assemble individual data for all Securities and Exchange Commission enforcement attorneys between 2002 and 2017, including enforcement cases, salaries, and ranks. I find that stronger tournament incentives, reflected by expected salary upon promotion and likelihood of promotion, increase enforcement activity and facilitate the detection of severe financial misconduct. For identification, I rely on cross-sectional tests within offices and hierarchy ranks and on exogenous variation in salaries resulting from a rule-based conversion to a new pay system. The findings highlight a novel link between incentives and regulation and show that the regulator's organizational design affects securities markets.
 
Sung Lee
Sung Lee
Dissertation Committee:  Johannes Stroebel (Chair), Anthony Saunders, Andres Liberman, Theresa Kuchler, Arpit Gupta
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            Fintech Nudges: Overspending Messages and Personal Finance Management
ABSTRACT: Using large proprietary money management app data from a major commercial bank in Canada, I study how the app users manage their personal finance upon seeing an overspending message on the mobile app. First, I find that the message recipients reduced spending on the following day by C$8.15, which corresponds to 5.4% of their daily average spending, compared to the non-recipients. Second, these fintech nudges had temporary effect on flow spending and resulted in permanent reduction in cumulative spending. Third, the effects are especially pronounced for the users who are older, have higher liquid wealth, are more finance-savvy, are new to the app experience, or reside in a city with a higher fraction of educated population. Fourth, I find suggestive evidence that these effects could spill over from one app user to another in the same household. On the other hand, the message recipients were less likely to monitor their accounts via log-ins afterward, which is selective inattention known as the ostrich effect.
 
Heebum Lee
Heebum Lee
Dissertation Committee:  Robert F. Engle (Chair), Robert Whitelaw, Kose John, Jose Luis Montiel Olea
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            What Explains the Cross-Section of Stock Return Reaction to Macroeconomic News?: A Machine Learning Approach
ABSTRACT: Abstract : I examine which stock characteristics explain cross-sectional heterogeneity in stock return responses to macroeconomic news announcement (MNA) shocks using a machine-learning approach instead of testing pre-specified factors or characteristics. Specifically, I adopt the Generalized Random Forest algorithm (Athey, Tibshrani, and Wager (2019)), which allows me to identify stock returns’ conditional reactions to macroeconomic news jointly and flexibly, depending on a multitude of stock characteristics. I show that stocks whose prices are more positively correlated with MNA shocks earn higher average returns. A long-short trading strategy exploiting this effect generates 10.6% annualized risk-adjusted return with an annualized Sharpe Ratio of 0.59 from 1980 through 2018. I also find that not only market betas but also momentum characteristics play a key role in explaining the cross-section of stock returns on MNA days. Past losers command high premiums on MNA days, which I show is partly driven by the fact that momentum factor is more volatile and more negatively skewed on MNA days than on non-MNA days.