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.
Grace Kim, Faculty Advisor
JEL Classification: G3; G34; L40; O31; O32;
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.
Dr. Connel Fullenkamp, Faculty Advisor
JEL classification: G3; G34; G32; G11
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.
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…
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.
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.
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.
Advisor: Edward Tower, Marjorie McElroy | JEL Codes: G12, G24, G32, G34 | Tagged: Finance, Initial Public Offering, Reverse Takeover
