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
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
After The Mega-Buyout Era: Do Public-to-Private Transactions Still Outperform?
By Bryn Wilson
Abstract
This thesis contributes to existing knowledge of the private equity asset class by examining whether public-to-private leveraged buyouts outperform public peers before and after the mega-buyout era (2005 – 2007). This paper considers the impact of four groups of value drivers on both market- and peer-adjusted returns. These value drivers include operational improvements, leverage, multiple expansion and market timing, and management and corporate decision making. I analyze how these factors change over time, aiming to determine whether public-to-private target firms improve profitability, return on assets, and investment more than peers. I also examine how employment changes at target firms relative to peers. Multivariable regression analysis is used to quantify the impact of operating performance changes, leverage, multiple expansion, credit market conditions, GDP growth, and management and corporate decisions on market- and peer-adjusted returns. The paper constructs a sample of 227 public-to-private transactions from 1996 – 2013 and analyzes 74 transactions with post-buyout financial information available. Results suggest that private equity ownership post-buyout does not lead to significant operational improvements relative to peers, but that improving profitability and ROA are crucial to outperforming the market and peers.
Professor Connel Fullenkamp, Faculty Advisor
JEL Codes: G3; G34; G32; G11
Bang for Your (Green) Buck: The Effects of ESG Risk on US M&A Performance
by Richard Chen
Abstract
Mergers & Acquisitions (M&A) is a fundamental corporate activity that has not received much attention from an environmental, social, and governance (ESG) perspective. In this paper, I analyze how buyer and target ESG risks affect US M&A performance in both the short and long run as measured by deal valuations and changes in buyer operating metrics, respectively. I utilize a sample of 341 transactions from 2007-2020 with a cumulative value over $3 trillion from Capital IQ where both the buyer and target have available ESG data provided by RepRisk. Utilizing OLS, my results suggest that higher ESG risk causes buyers to pay more and targets to receive less. In the long run, buyer ESG risk is an important determinant of performance. When examining the components of ESG, governance is the most consistently significant, followed by social, then environmental – though it becomes more significant in the long run. Additionally, all three components appear to have some non-linear impacts on M&A performance.
Professor Connel Fullenkamp, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: G34, G14, M14
Informing the Investor: A Comparative Analysis of the Importance of Pre-Initial Public Offering (IPO) Information on Stock Performance
by Paul Snyder
Abstract
This paper answers which available information about the company, macroeconomic and market environment, regulatory constraints, and offering before an IPO is most impactful on year-long buy-and-hold abnormal returns and how that changes across time while analyzing the IPO markets of 1999 and 2019. Data was gathered from predominantly company prospectuses and proprietary datasets to select a total of 419 IPOs across two samples and regress abnormal geometric returns against the aforementioned information using multivariate OLS regressions. There are a number of interesting findings. First, certain information or factors that act as signals of stock performance before an IPO that correlate with stock performance change across time. Second, there is evidence that companies abiding by more regulation pre-IPO tend to perform better on the stock market after the fact, particularly with the Sarbanes-Oxley and JOBS Acts. While the direction of causality is unknown, there is now a clear and quantified relationship between IPO regulation requirements and stock performance. Third, there is evidence that the IPO market has become more strong-form efficient when comparing 1999 to 2019.
Professor Edward Tower, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: G1, G12, G14
The Elusive “Stock-Picker’s Market”: Dispersion and Mutual Fund Performance
By Jacob Epstein
This paper explores the relationship between active mutual fund performance and market dispersion from January 1990 to December 2018. I find a significant positive relationship between dispersion and 4-factor alpha overall, providing some evidence of managerial skill. There are large differences in this relationship by decade and fund selectivity. The results suggest active mutual funds were able to take advantage of stock-picking opportunities during the 1990s and 2000s, particularly the most active subset of funds. However, I find a significant negative relationship between dispersion and alpha for funds in the 2010s, indicating this relationship has changed over time. I discuss several possible explanations for this reversal, which could present interesting avenues for further research.
Advisors: Professor Emma Rasiel | JEL Codes: G1, G12, G23
Comparing the Performance of Active and Passive Mutual Funds in Developing and Developed Countries
By Nalini Gupta
This paper seeks to test the hypothesis that developing countries or informationally inefficient countries should see higher returns for active mutual funds on average than passive funds and the trend should be reversed in developed nations or informationally efficient economies. This analysis is done using a cross section of eight countries, four developed and four developing. Using a fund universe of 20 active and 20 passive funds per country and controls such as volatility, market return, financial market development and Human Development Index among others, we see that there is no clear systematically dominant strategy between active and passive investment universally. While developing countries are associated with lower returns, we do not find a significant difference between active and passive based on development classification. A key finding is that an increase in liquidity, acting as proxy for informational efficiency, leads to a co-movement of active and passive returns in each country. The paper also lends itself to further analysis regarding confounding factor such as noise trading and movement of foreign capital which impact the effect of increased liquidity on mutual fund returns.
Advisors: Professor Connel Fullenkamp, Professor Kent Kimbrough | JEL Codes: G1, G11, G14
Forecasting Corporate Bankruptcy: Applying Feature Selection Techniques to the Pre- and Post-Global Financial Crisis Environments
By Parker Levi
I investigate the use of feature selection techniques to forecast corporate bankruptcy in the years before, during and after the global financial crisis. Feature selection is the process of selecting a subset of relevant features for use in model construction. While other empirical bankruptcy studies apply similar techniques, I focus specifically on the effect of the 2007-2009 global financial crisis. I conclude that the set of bankruptcy predictors shifts from accounting variables before the financial crisis to market variables during and after the financial crisis for one-year-ahead forecasts. These findings provide insight into the development of stricter lending standards in the financial markets that occurred as a result of the crisis. My analysis applies the Least Absolute Shrinkage and Selection Operator (LASSO) method as a variable selection technique and Principal Components Analysis (PCA) as a dimensionality reduction technique. In comparing each of these methods, I conclude that LASSO outperforms PCA in terms of prediction accuracy and offers more interpretable results.
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Advisors: Professor Andrew Patton, Professor Michelle Connolly | JEL Codes: G1, G01, G33
