Elder Financial Fraud: The Economic and Ethical Case for Instituting Mandatory Reporting Laws in Financial Institutions
by Lauren Tse
Abstract
This study examines the effectiveness of the 2016 NASAA Model Act, specifically if states that implemented its provisions see greater levels of elder fraud reporting. This legal reform introduces reporting requirements for broker-dealers and investment advisers to report suspected elder fraud to government authorities, granting explicit immunity to those who comply. To analyze both the immediate and longer-term effects of the Model Act’s staggered passage across states, I use a dynamic Difference-in-Difference model to analyze institutionally reported elder fraud cases from the U.S. Department of Treasury’s Financial Crimes Enforcement Network. Regression findings suggest that the Model Act has a positive enabling effect, increasing the number of elder fraud reports filed by financial professionals. Further, I quantify the monetary losses associated with these fraud cases using self-reported data from the Federal Trade Commission’s Consumer Sentinel Network. In line with this ‘placebo’ dataset, I find that the passage of the Model Act — targeted at financial professionals — has inconclusive impacts on the number of self-reported elder fraud and no effect on the financial losses incurred.
Professor Kate Bundorf, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: G28; K42; J14
Keywords: Elder Financial Fraud; NASAA Model Act; Mandatory Reporting Requirements
Investing in Rural Healthcare: Impact of Private Equity Acquisition on Financial and Utilization Outcomes of Rural Hospitals
by Amanda He
Abstract
Private equity investment in the healthcare sector has risen considerably in recent decades, yet the impact of private equity ownership in rural hospital markets is largely unknown. Existing research points to a correlation between private equity acquisition and increased hospital incomes and charges. Rural hospitals, however, are structurally and operationally different from their urban counterparts, with lower occupancy rates and higher susceptibility to financial distress. This paper seeks to (1) characterize the types of rural hospitals acquired by private equity firms and (2) examine the changes in rural hospital financial, utilization, and survivability outcomes following private equity ownership. Using a 15-year panel of Medicare data, I estimate the impact of 352 private equity deal-hospitals across nine financial and utilization outcomes. Additionally, I estimate the impact of private equity on hospital closures. I find that private equity acquisition improves profitability for both urban and rural hospitals, but the magnitude is smaller for rural hospitals. My results suggest that private equity-owned hospitals increase profits by reducing operating expenses. Among rural hospitals, private equity ownership is associated with fewer discharges and lower occupancy rates, which may be a concern for long-term viability. I find a statistically significant negative correlation between private equity acquisition of rural hospitals and an increased likelihood of closure. PE-acquired hospitals have a negative spillover effect on other hospitals within the same hospital referral region, leading to a higher probability of closing.
Professor Ryan McDevitt, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL classification: G23, G33, G34, I10, I11
Last Second Comebacks: Examining Influencers of Bankruptcy Success
by Eric Junzhe Zhang
Abstract
The American bankruptcy system allows for companies to file for Chapter 11 bankruptcy to protect their assets from creditors and reorganize their business operations to continue operating after going through bankruptcy court. While the process is meant to help improve the financial health and business operations of companies after they exit the bankruptcy process, supposedly remedied firms will often find themselves filing again for bankruptcy despite the drastic changes they underwent to avoid such a fate. As such, it is difficult to determine what exactly makes a bankruptcy successful, as oftentimes a company with one metric that deems the bankruptcy successful may have another conflicting metric that deems it unsuccessful. This thesis seeks to contribute to prior knowledge on bankruptcy analysis by examining what in-court factors and company metrics drive bankruptcy success, with the change in debt-to-asset ratio and refiling likelihood post emergence being used as measures of bankruptcy success. Probit regression is used to analyze the change in the debt-to-asset ratio from bankruptcy filing to emergence while multivariable regression analysis is used to analyze the likelihood of refiling post-bankruptcy emergence. Explanatory variables which will be examined across these two variables will be the time spent in bankruptcy court, whether there was forum shopping to Delaware or New York, size of assets / EBIT of the firm, hedge fund presence, CEO turnover, whether a case was prepackaged, unionization rate, prime rate at filing and emergence, whether there was a 363 asset sale, whether a firm remained public following emergence, and debtor in possession financing. Results suggest that likelihood of refiling is a better measure of bankruptcy success than relative change in debt-to-asset ratio, which faces issues with the significance of its variables and their explanatory power.
Professor Connel Fullenkamp, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: G33, K22, G34
Do Green Stocks Get You the Green? Differential Impacts of S&P 500 ESG Index Labels on Firm Stock Prices
by Heera Rajavel
Abstract
On January 28, 2019, the S&P Dow Jones Indices launched the ESG S&P 500 Index, aiming to create a sustainable index fund with a similar risk/return profile to the S&P 500 Index. This study assesses the causal mechanisms behind the performance of the S&P 500 ESG Index by running two difference-in-differences estimations using a panel data set of 698 companies. The first difference-in-differences estimation compares the stock prices of companies on the S&P 500 ESG Index to the stock prices of companies S&P 500 Index, determining if companies on the S&P 500 ESG Index received an “ESG label” price premium. Results show that in the short-term and the long-term, companies on the S&P ESG 500 Index experienced statistically significant negative stock price growth relative to companies only on the general S&P 500 Index; the “ESG label” appears to slow stock growth for companies on the S&P 500 ESG Index by $48.24 in the short-term and $65.29 in the long-term. The second difference-in-differences estimation compares the stock prices of companies on the S&P 500 ESG Index to the stock prices of companies with similar ESG qualifications that are not on an S&P Index, determining if companies in the S&P 500 ESG Index received an “S&P label” price premium. These results found that in both the short and the long run, companies on the S&P 500 ESG Index faced statistically significant positive stock price growth relative to companies with similar ESG qualifications; the “S&P label” seems to increase stock price growth for companies on the S&P 500 ESG Index by $2.19 in the short-term and $7.63 in the long term.
Professor Lawrence Kreicher, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: G2, G23, Q56
The Cost of Delay: Evidence from the Ethereum Transaction Fee Market
by Yinhong “William” Zhao
Abstract
Delaying a financial transaction can be costly, but the cost of delay is difficult to estimate in traditional
finance. I exploit the unique data offering and market design of the Ethereum blockchain to estimate the
cost of delaying financial transactions in decentralized finance (DeFi). I construct a dynamic auction
model for the Ethereum transaction fee market that relates users’ optimal transaction fee bids to their delay
cost functions and network conditions, and I structurally estimate the delay cost functions for different
users and transaction types. The average cost of delaying a transaction by one minute is 8.78 US dollars,
but the distribution of delay costs is highly skewed to the right. Delay costs are higher for complex
transactions and users who trade more frequently. I estimate that welfare loss due to network delay on
Ethereum was 14.03 million US dollars per day in July 2021, and I apply the delay cost estimates to
evaluate the welfare losses under alternative transaction fee mechanisms.
Campbell Harvey, Faculty Advisor
Michelle Connolly, Faculty Advisor
JEL Codes: D44; G10; L17;
Short Term Effectiveness of Chinese Stock Connect Program — a Study of the Pricing Dynamics of Cross-listed Stocks
by Kaiyu Ren
Abstract
This thesis examines the pricing dynamics of cross-listed stocks in the Chinese A-share and
Hong Kong H-share markets. By identifying an announcement-implementation window, I offer a
fresh perspective on the short-term price adjustment of cross-listed stocks around the launch of
the first Stock Connect program. My findings reveal a significant increase of the A-H price ratio,
but this price discrepancy appears to have been mitigated by the implementation of the Stock
Connect program.Additionally, my observations suggest the existence of market inefficiencies,
particularly among the groups of A-share stocks that are excluded from the Stock Connect
program.
Professor Ronald Leven, Faculty Advisor
JEL Codes: G14; G18
What Affects Post-Merger Innovation Outcomes? An Empirical Study of R&D Intensity in High Technology Transactions Among U.S. Firms
by Neha Karna
Abstract
High levels of global M&A activity have characterized the past decade, making the policy debate over the impact of mergers on innovation even more pertinent. Innovation is a significant driver of economic growth and therefore a negative effect of mergers on innovation outcomes may have detrimental consequences. Nevertheless, the existing literature demonstrates mixed results leaving it unclear whether the overall effect is positive or negative. This paper contributes to existing literature on the relationship between mergers and innovation and examines the effects of M&A on the subsequent innovative activity of acquiring firms that operate in high technology (high-tech) industries. I construct a sample of U.S.-based public-to-public deals from 2010-2019 involving high-tech acquiring firms. Using multivariable regression with robust considerations, I analyze factors that may explain post-merger R&D intensity defined as the merged entity’s R&D expenditure divided by its total assets one year after deal completion. I consider firm characteristics of the target and acquirer, including size, industry, and age, and industry competition. I find potential positive impact of relative target size on post-merger R&D intensity and significant interaction effects between relative target size and firm age, relative target size and industry relatedness, and target industry competition and industry relatedness. My results suggests that beyond the occurrence of a merger, specific deal characteristics may affect postmerger innovation outcomes.
Professor Grace Kim, Faculty Advisor
Professor Kent Kimbrough, Faculty Advisor
JEL Codes: G3; G34; L40; O31; O32;
Private Equity IPOs: Long-term Performance and Drivers of Success
by Ignacio Hidalgo Perea
Abstract
In this paper, I explore the impact Private Equity ownership has on portfolio companies post-exit. This thesis aims to add to the discussion of whether the proliferation of Private Equity in the United States is a positive development for the country. Using a proprietary dataset that compiles thousands of IPOs between the years 2000 and 2016, I look at whether there are significant differences in performance between IPOs that come from Private Equity firms and those that go public on their own. Specifically, I use empirical analysis with robust regression to estimate the effects of Private Equity ownership on four key measures of financial success: MCAP growth, Revenue Growth, EBITDA Margin, and EV / EBITDA multiple. By looking at the changes in these measures of performance across three different time windows: 3 years post-IPO, 6 years post-IPO, and 9 years post-IPO, this paper determines how Private Equity ownership affects company performance post-exit and whether those effects persist over time.
Professor Grace Kim, Faculty Advisor
JEL Codes: G23, G24
Shades of Green: An Examination Into Second Party ESG Ratings In The Municipal Green Bond Market
by Harrison Zane Cole
Abstract
Since the end of the pandemic the market capitalization of green bonds and investor interest in sustainable investments has grown massively. The tidal wave of ESG funds has accompanied many claims of greenwashing and extreme variation in investment quality. While many investors focus on doing their own due diligence, second party ratings are an important source of information for capturing overall risk and characteristics of a security. This paper aims to take a deeper look at how HIP Investor’s (a popular ESG rating firm) ratings correlate to real-world yield and bond characteristics. Yield refers to the annualized return that investors receive from a bond, and lower yields at issuance reduce borrowing costs for the issuer. It is generally established in popular and academic literature that green bond designation does not directly lead to a benefit for issuers in terms of their cost of capital expressed through interest rates. This paper examines the yield at issuance effects for degrees of “actual greenness” and other inputs that may lead to a security to fit well in an ESG focused or impact fund. Within a sample of green bonds, the estimated yield to worst spread premium for a best-in-class (environmentally) ESG issuer is -23.9 basis points compared to a worst-in-class issuer holding all else equal. When considering non-environmental factors such as human health, the effect is larger at -71.4 basis points. For social wealth considerations, the directionality is reversed at 17.9 basis points. More research is needed to better understand and apply these results. This decrease in interest rates can result in millions of dollars of savings for larger issuances.
Professor Lawrence Kreicher, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: G1, G12, G14
Heterogeneity in Mortgage Refinancing
By Julia Wu
Abstract
Many households who would benefit from and are eligible to refinance their mortgages fail to do so. A recent literature has demonstrated a significant degree of heterogeneity in the propensity to refinance across various dimensions, yet much heterogeneity is left unexplained. In this paper, I use a clustering regression to characterize heterogeneity in mortgage refinancing by estimating the distribution of propensities to refinance. A key novelty to my approach is that I do so without relying on borrower characteristics, allowing me to recover the full degree of heterogeneity, rather than simply the extent to which the propensity to refinance varies with a given observable. I then explore the role of both observed and unobserved heterogeneity in group placement by regressing group estimates on a set of demographic characteristics. As a complement to my analysis, I provide evidence from a novel dataset of detailed information on borrower perspectives on mortgage refinancing to paint a more nuanced picture of how household characteristics and behavioral mechanisms play into the decision to refinance. I find a significant degree of heterogeneity in both the average and marginal propensity to refinance across households. While observables such as education, race and income do significantly correlate with group heterogeneity, it is clear that much heterogeneity may still be attributed to the presence of unobservable characteristics.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL codes: D9, E52, G21
