Determining the Drivers of Acquisition Premiums in Leveraged Buyouts
By Peter Noonan
This thesis analyzes factors that determine acquisition premiums paid by private equity firms in public to private leveraged buyouts. Building off of established literature that models the acquisition premiums paid in corporate mergers and acquisitions (M&A), this paper considers factors that influence a private equity firm’s willingness to pay (referred to as reservation price) and the bargaining power dynamic between a target company and acquirer in leveraged buyouts. Specifically, multivariable regression analysis is used to quantify the impact of a target company’s trading multiple, profitability, stock price as a percentage of its annual high, and number of competitors, a private equity firm’s deal approach and payment method, and the financial market’s 10-year US Treasury yield and high-yield interest rates at the time a transaction was announced. A sample of 320 public to private leveraged buyout transactions completed from 2000 to 2020 is constructed to perform this paper’s regression analysis. Using 2008 as an inflection point, this thesis then applies the same regression model to the subperiods from 2000–2008 and from 2009–2020 to examine how these drivers have changed as a result of industry trends—increased competition, low interest rates, and new value creation investment strategies—as well as the 2008 financial crisis and US presidential election—two crucial events that caused tremendous change in the financial system and intense scrutiny of the private equity industry. From the same original transaction screen, a second sample of 659 transactions is used to perform a difference of acquisition premium means t-test to analyze how the absolute magnitude of leverage buyout acquisition premiums have changed across these two subperiods. The second sample consists of more transactions due the t-tests less data-demanding nature as a result of its fewer variables. Results of this paper’s baseline model suggest that acquisition premiums are driven by a target company’s…
Advisors: Professor Ronald Leven, Professor Michelle Connolly | JEL Codes: G3, G11, G34
The Impact of Post-IPO Private Equity Ownership on Long-Term Company Performance
By Maria Suhail and Cipriano Echavarría
This thesis contributes to existing knowledge of private equity (PE) by analyzing the
impact of PE ownership post-IPO upon the long-term performance of companies. It considers whether companies perform better when PE funds maintain their ownership stakes post-IPO and whether this performance is also impacted by the degree of ownership that is maintained after IPO. This study uses stock performance (measured by cumulative excess stock returns) as a proxy for long-run company performance. The paper constructs and analyzes a sample of 487 companies that underwent an IPO between 2004 and 2012 to determine the implications of the maintenance and level of PE ownership by analyzing the performance of these companies for six years post-IPO. Results suggest that PE ownership post-IPO positively impacts long-term stock performance of companies. Duration and degree of PE ownership post-IPO are also important determinants of long-run performance likely due to the positive signal that continued PE ownership sends to outside investors about the quality of the company, the information asymmetry that exists between public and private markets and that PE firms are experienced managers that add value to companies.
Advisors: Professor David Robinson, Professor Michelle Connolly | JEL Codes: G11, G14, G24
Wrangling the Herd: A Cross-Cultural and Cross-Industry Approach to Herding Market Behavior
By Tyler Fenton and Jarred Kotzin
The traditional efficient market hypothesis serves as the foundation of modern economic theory, governing the investigation of financial markets. While this premise assumes all investors are rational and all information is immediately incorporated into markets, this paper explores herding behavior – a central tenet of behavioral finance that explains the apparent inefficiencies of financial markets. Utilizing return data from the past 10 years from eight exchanges around the world, segmented into 10 industry classes as well as a broad market index, we compare levels of herd behavior using return dispersion proxies. We find significant evidence of herding in nearly all exchanges and all industries included in the study and the degree of this herd behavior varies across industries in different countries. Overall, we find support for the behavioral finance principle of herding and conclude that certain cultural or non cultural factors affect this activity differently in various countries and industries.
Advisors: Professor Connel Fullenkamp | JEL Codes: G4, G14, G15
Is Smart Money Smart? The Costs of Hedge Funds Trading Market Anomalies
By Matthew J. Farrell
Do hedge funds earn statistically significant premia on common factor trading strategies after trading costs are accounted for? Furthermore, what is the gap between what a hedge fund would earn and the paper portfolios that they hold? I answer this question by using the latest cutting-edge methodology to estimate trading costs for major financial market anomalies. This methodology uses the familiar asset-pricing Fama-MacBeth procedure to compare the on-paper compensation to factor exposures with those earned by hedge funds. I find that the typical hedge fund does not earn profits to value or momentum, and and low returns to size.
Advisor: Professor Brian Weller | JEL Codes: G12; G14; G23;
Prediction in Economics: a Case Study of Economists’ Views on the 2008 Financial Crisis
By Weiran Zeng
Prediction in economics is the focal point of debate for the future of economics, ever since economists were burdened with the failure to “predict” the 2008 Financial Crisis. This paper discusses positions held by philosophers and economic methodologists regarding what kinds of predictions there are and creates a taxonomy of prediction. Through evaluation of those positions, this paper presents different senses of prediction that can be expected of economics, and assess economists’ reflections according to those senses.
Advisor: Kevin Hoover | JEL Codes: B41, N1, G17
Evaluating Asset Bubbles within Cryptocurrencies using the LPPL Model
By Rafal Rokosz
The advent of blockchain technology has created a new asset class named cryptocurrencies that have experienced tremendous price appreciation leading to speculation that the asset class is experiencing an asset bubble. This paper examines the novelty and functionality of cryptocurrencies and potential factors that may lead to conclude the existence of an asset bubble. To empirically evaluate whether the asset class is experiencing an asset bubble the LPPL model is used. The LPPL model was able to successfully identify two of the four crashes within the data set signifying that cryptocurrencies are within an asset bubble.
Advisors: Ed Tiryakian and Grace Kim | JEL Codes: G12, Z00, C60
Effect of Sentiment on Bitcoin Price Formation
By Brian Perry-Carrera
With the recent growth in the investment of cryptocurrencies, such as bitcoin, it has become increasingly relevant to understand what drives price formation. Given that investment in bitcoin is greatly determined by speculation, this paper seeks to find the econometric relationship between public sentiment and the price of bitcoin. After scraping over 500,000 tweets related to bitcoin, sentiment analysis was performed for each tweet and then aggregated for each day between December 1st, 2017 and December 31st, 2017. This study found that both gold futures and market volatility are negatively related to the price of bitcoin, while sentiment demonstrates a positive relationship.
Advisor: Grace Kim | JEL Codes: G12, G41, Z00
Multi-Horizon Forecast Optimality Based on Related Forecast Errors
By Christopher G. MacGibbon
This thesis develops a new Multi-Horizon Moment Conditions test for evaluating multi-horizon forecast optimality. The test is based on the variances, covariances and autocovariances of optimal forecast errors that should have a non-zero relationship for multi-horizon forecasts. A simulation study is conducted to determine the test’s size and power properties. Also, the effects of combining the Multi-Horizon Moment Conditions test and the well-known Mincer-Zarnowitz and zero autocorrelation tests into one forecast optimality test are examined. Lastly, an empirical study evaluating forecast optimality for four multi-horizon forecasts made by the Survey of Professional Forecasters is included.
Advisors: Andrew Patton, Grace Kim and Kent Kimbrough | JEL Codes: G1, G17, G00
An Analysis of Passive and Active Bond Mutual Fund Performance
By Michael J. Kiffel and Surya Prabhakar
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: Grace Kim, Edward Tower | JEL Codes: C55, G10, G11
Vanguard’s Index Funds vs. Vanguard’s Managed Funds: a Nine Style Box and Fama-French Multi-Variable Regression Approach
By Susheel Nalla
Many investors struggle to determine whether they want to invest in managed funds or indexed funds when they build portfolio. Vanguard, founded by John Bogle, a strong advocate for indexed investing has seen his company grow to over $3.9 trillion in funds. In the last three years $1 trillion of new money has come into their passive funds as investors are moving towards saving on cheaper expense ratios. However, many people like Vanguard’s former CIO Gus Sauter believe that managed funds can deliver additional value to their investors by keeping expense ratios low and hiring the world’s best managers. This study looks at Vanguard indexed and managed funds in three different market capitalizations organized nine style boxes to see whether Vanguard’s strategy of low management expense ratios provides additional value to their own benchmarks. This study uses two comparison methods to analyze market returns of these funds from 2006-2016. First, indexed and managed Vanguard funds will be compared using a Morningstar nine-style box to directly see differences in return rates and estimate riskiness of these assets through standard deviations. Second, the Fama-French three-factor model will be used to create a regression explaining where the fund returns may be coming from. This method will determine the SMB and HML of the funds telling us the size of the equities in the fund along with their value premium over book value. Also, a market coefficient will be determined to see how close these funds are relative to a market benchmark. Overall, it is determined that Vanguard indexed funds in small-cap and mid-caps are slightly better investments based on returns and exposure to risk along with their equity composition. Based on the same criteria, large-cap funds perform slightly better than their indexed counterparts.
Advisor: Edward Tower | JEL Codes: C55, G10, G11, G12
