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Category Archives: G34

Private Equity Buyouts and Strategic Acquisitions: An Analysis of Capital Investment and the Timing of Takeovers in the United States

By Anthony Melita   

This paper investigates how motivational differences between agents who execute private equity buyouts and those who execute strategic (corporate) acquisitions may influence the timing of capital investment via takeovers. This paper synthesizes prominent merger theories to inform macroeconomic variables that may drive acquisitions. I find a significant negative expected effect of volatility on capital investment via takeover for each buyer type, a negative expected effect from valuation multiples on capital investment from PE buyouts, and a positive expected effect from debt capacity (EBITDA-CAPEX) on capital investment from PE buyouts.

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Advisors: Professor Grace Kim | JEL Codes: G3, G34, G29

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…

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Advisors: Professor Ronald Leven, Professor Michelle Connolly | JEL Codes: G3, G11, G34

Corporate Financial Distress and Bankruptcy Prediction in North American Construction Industry

By Gang Li

This paper seeks to explore the application of Altman’s bankruptcy prediction model in the construction industry by measuring its percentage accuracy on a dataset consisting of 108 bankrupt & non-bankrupt firms selected across the timeline of 1985-2013. Another main goal this paper is to explore the predictive power of an expanded variable set tailored to the construction industry and compare the results. Specifically, this measuring process is done using machine learning algorithm based on scikit-learn library that transforms a raw .csv file into clean vectorized dataset. The algorithm provides various classifiers to cross-validate the training set, which produces mixed statistics that favors neither variable set but provides insight into the reliability of the non-linear classifiers.

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Advisor: Connel Fullenkamp | JEL Codes: C38, C5, G33, G34 | Tagged: Bankruptcy, Corporate, Discriminant Analysis, Distress, Machine Learning

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.

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JEL Codes: G10, G14, G34 | Tagged: Macroeconomic Surprises, Mergers & Acquisitions, Real Estate Investment Trust

A Further Exploration of Reverse Takeovers as an Alternative to Initial Public Offerings

By Matt LoSardo

In theory a reverse takeover (RTO) should be a viable alternative to initial public offerings (IPO) for private companies looking to access the public capital markets.  Since the IPO process can be very timely and include significant costs, both direct and indirect, we analyze reverse takeovers as an alternative method.  Recent papers have posed some similar questions, evaluating underpricing and market-timing, which we look to confirm.  However, our paper seeks to build on these analyses, with a particular focus on long-term returns for RTO stocks.  Overall we find that reverse takeovers can be successfully used instead of IPOs and should be sustainable long-term investments.

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Advisor: Edward Tower, Marjorie McElroy | JEL Codes: G12, G24, G32, G34 | Tagged: Finance, Initial Public Offering, Reverse Takeover

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