Essays in Corporate Finance and Private Equity

This dissertation is a collection of essays in private equity and corporate finance. Detractors have warned that Private Equity (PE) funds tend to over-lever their portfolio companies because of an option-like payoff. In Chapter 1, I draw on standard trade-off theory, and argue PE-ownership leads to higher levels of \emph{optimal} (value-maximizing) leverage. I develop a dynamic trade-off model where a firm's capital structure and default decisions are made by the PE fund manager.Key model parameters are estimated using balance sheet data from a large sample of PE-sponsored leveraged buyouts (LBO). I find the estimated model is able to explain both the level and change in leverage ratios documented empirically following LBOs, driven primarily by changes in the portfolio company hypothesized above. In Chapter 2, I investigate the role of global push factors that may drive cross-border buyout investments. Till date, the extant literature has only documented pull factors that matter for international buyout investments. I show that changes in global risk aversion and uncertainty are particularly important and quantitatively matters as much as debt market conditions in driving cross border buyouts. In Chapter 3, we study the impact of the COVID-19 recession on capital structure of publicly listed U.S. firms. Our find leverage decreased by 5.3 percentage points from the pre-shock mean of 19.6 percent, while debt maturity increased moderately. This de-leveraging effect is stronger for firms exposed to significant rollover risk. We estimate a structural model that shows lower expected growth rate and higher volatility of cash flows following COVID-19 reduced optimal levels of corporate leverage.Doctor of Philosoph

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes