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;
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
An Analysis of Passive and Active Bond Mutual Fund Performance
By Michael J. Kiffel
The literature on the performance differential between passively and actively managed equity mutual funds is thorough: passively managed funds generally outperform their active counterparts except in the rare presence of highly-skilled managers. However, there exists limited academic research regarding fixed income mutual funds. This study utilizes the Fama-French bond risk factors, TERM and DEF, in a dual-step multivariate linear regression analysis to determine this performance differential between passively and actively managed bond mutual funds. The funds are comprised of either corporate or government bonds, spanning three categorizations of average maturities. Overall, it is determined that passively managed bond funds offer higher net returns than those offered by actively managed funds. Additionally, the regressions demonstrated that DEF possesses a high degree of predictive power and statistical
significance.
Advisor: Edward Tower | JEL Codes: C55, G10, G11
The Impact of Macroeconomic Surprises on Mergers & Acquisitions for Real Estate Investment Trusts
By John Reid
This paper examines the impact of various macroeconomics and real estate specific surprises on M&A transactions involving Real Estate Investment Trust. The 2008 financial crisis drastically affected merger & acquisitions activity, especially within the real estate market. The number of M&A transactions involving Real Estate Investment Trusts were very volatile during this period of economic turmoil and it appeared that several economic factors contributed to changing patterns in M&A activity. Our study uses time series data to draw a connection between REIT-related M&A activity and quantifiable factors. From or results we find there to be a relationship between the macroeconomic environment and REIT-related M&A activity.
JEL Codes: G10, G14, G34 | Tagged: Macroeconomic Surprises, Mergers & Acquisitions, Real Estate Investment Trust
Are Asset Allocation Funds Good at Market Timing?
By Yunze Chen
“Don’t forget that your incredible success in consistently making each move at the right time in the
market is but my pathetic failure in making each move at the wrong time. … … I don’t know anyone who can do it successfully, nor anyone who has done so in the past. Heck, I don’t even know anyone who knows anyone who has timed the market with consistent, successful, replicable results.” (John Bogle, quoted in The Finance Buff, 2011).
John C. Bogle, the founder of the Vanguard Group, has long insisted on the superiority of index funds over actively managed mutual funds and the foolishness of attempts to time the market. He published two articles in the Journal of Portfolio Management showing that in eight out of nine style categories, managed mutual funds had lower risk-adjusted returns than the corresponding indexes did. While this demonstrates the failure of stock picking by mutual funds to serve investors well, it says nothing about their ability to time the market by changing styles. Managers of asset allocation funds often use a flexible combination of stocks, bonds, and cash; some, but not all, shift assets frequently based on analysis of business-cycle trends. To test his view of market timing, we evaluated the returns of 82 major asset allocation funds by comparing them with the returns of corresponding baskets of Vanguard’s index funds over a 13-year time span. We find that on average the index funds have higher risk-adjusted returns. We conclude that “simplicity is the ultimate sophistication” applies to mutual fund investments.
Advisor: Edward Tower | JEL Codes: G10, G11, G20 | Tagged: Expense Ratio, Mutual fund families, Performance
Examination of Time-Variant Asset Correlations Using High- Frequency Data
By Mingwei Lei
Drawing motivation from the 2007-2009 global financial crises, this paper looks to further examine the potential time-variant nature of asset correlations. Specifically, high frequency price data and its accompanying tools are utilized to examine the relationship between asset correlations and market volatility. Through further analyses of this relationship using linear regressions, this paper presents some significant results that provide striking evidence for the time-variability of asset correlations. These findings have crucial implications for portfolio managers as well as risk management professionals alike, especially in the contest of diversification.
Advisor: George Tauchen | JEL Codes: G, G1, G10, G11, G14 | Tagged: Asset correlations, Diversification, Financial Crisis, High-Frequency Data, Market Volatility, Time-Variant Correlations, Time-Variant Volatility
Relative Contribution of Common Jumps in Realized Correlation
By Kyu Won Choi
This paper studies common intraday jumps and relative contribution of these common jumps
in realized correlation between individual stocks and market index, using high-frequency price
data. We find that the common jumps significantly contribute in realized correlation at different
threshold cut-offs and both common jumps and realized correlation are relatively consistent across
time period including financial crisis. We also find a weak, positive relationship between relative
contribution of common jumps and realized correlation, when we further sample high-frequency
data into a year. We also observe that the volatility index and market index reveal the strongest
relationship.
Advisor: Geourge Tauchen, Tim Bollerslev | JEL Codes: C40, C58, G10 | Tagged: Diffusive Covariation, Realized Correlation, Relative Contribution of Common Jumps
Motivation and Reasoning Behind Chinese Enterprises Overseas Listing
By Sjing Liang
Starting from the early 90s, the number of Chinese firms going public overseas has been increasing rapidly. By running a probit regression, this paper investigates the different factors that affect a Chinese firm’s choice of listing location, either a domestic or a foreign stock exchange. Our data consists of 286 foreign listed companies and 788 domestically listed ones that went public between 2005 and the first quarter of 2011. Our results reveal that, larger firms, in terms of their pre-IPO revenue values, are more likely to go public overseas. In addition, firms in high-tech and capital-intensive industries, namely technology, financials, and real estate, are better represented in overseas markets. We also find that stock markets with lower underpricing levels are more attractive to Chinese firms, who tend to avoid capital markets with high underpricing levels as they do not want to be undervalued at their IPOs.
Advisor: Alon Brav, Edward Tower | JEL Codes: G10, G15 | Tagged: Chinese Enterprises, Initial Public Offerings, Oversea Listing, Stock Markets
