Investing in Rural Healthcare: Impact of Private Equity Acquisition on Financial and Utilization Outcomes of Rural Hospitals
by Amanda He
Abstract
Private equity investment in the healthcare sector has risen considerably in recent decades, yet the impact of private equity ownership in rural hospital markets is largely unknown. Existing research points to a correlation between private equity acquisition and increased hospital incomes and charges. Rural hospitals, however, are structurally and operationally different from their urban counterparts, with lower occupancy rates and higher susceptibility to financial distress. This paper seeks to (1) characterize the types of rural hospitals acquired by private equity firms and (2) examine the changes in rural hospital financial, utilization, and survivability outcomes following private equity ownership. Using a 15-year panel of Medicare data, I estimate the impact of 352 private equity deal-hospitals across nine financial and utilization outcomes. Additionally, I estimate the impact of private equity on hospital closures. I find that private equity acquisition improves profitability for both urban and rural hospitals, but the magnitude is smaller for rural hospitals. My results suggest that private equity-owned hospitals increase profits by reducing operating expenses. Among rural hospitals, private equity ownership is associated with fewer discharges and lower occupancy rates, which may be a concern for long-term viability. I find a statistically significant negative correlation between private equity acquisition of rural hospitals and an increased likelihood of closure. PE-acquired hospitals have a negative spillover effect on other hospitals within the same hospital referral region, leading to a higher probability of closing.
Professor Ryan McDevitt, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL classification: G23, G33, G34, I10, I11
Last Second Comebacks: Examining Influencers of Bankruptcy Success
by Eric Junzhe Zhang
Abstract
The American bankruptcy system allows for companies to file for Chapter 11 bankruptcy to protect their assets from creditors and reorganize their business operations to continue operating after going through bankruptcy court. While the process is meant to help improve the financial health and business operations of companies after they exit the bankruptcy process, supposedly remedied firms will often find themselves filing again for bankruptcy despite the drastic changes they underwent to avoid such a fate. As such, it is difficult to determine what exactly makes a bankruptcy successful, as oftentimes a company with one metric that deems the bankruptcy successful may have another conflicting metric that deems it unsuccessful. This thesis seeks to contribute to prior knowledge on bankruptcy analysis by examining what in-court factors and company metrics drive bankruptcy success, with the change in debt-to-asset ratio and refiling likelihood post emergence being used as measures of bankruptcy success. Probit regression is used to analyze the change in the debt-to-asset ratio from bankruptcy filing to emergence while multivariable regression analysis is used to analyze the likelihood of refiling post-bankruptcy emergence. Explanatory variables which will be examined across these two variables will be the time spent in bankruptcy court, whether there was forum shopping to Delaware or New York, size of assets / EBIT of the firm, hedge fund presence, CEO turnover, whether a case was prepackaged, unionization rate, prime rate at filing and emergence, whether there was a 363 asset sale, whether a firm remained public following emergence, and debtor in possession financing. Results suggest that likelihood of refiling is a better measure of bankruptcy success than relative change in debt-to-asset ratio, which faces issues with the significance of its variables and their explanatory power.
Professor Connel Fullenkamp, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: G33, K22, G34
Investigating Underpricing in Venture-Backed IPOs Using Statistical Techniques
By Michael Tan
This paper concerns applying statistical methods to investigate under-pricing in VC-backed technology Initial Public Offerings (IPOs) since the great recession. In particular, firm, market, and IPO-specific variables were explored to determine if there were any significant relationships to under-pricing. The paper focused on the Bank Preference theory of under-pricing, where under-pricing is said to occur because investment banks running IPO processes are incentivized to under-price to decrease the risk that they will not be able to allocate all the issuance to price-sensitive public markets investors.
Advisors: Professor Daniel Xu, Professor Shawn Santo, Professor Grace Kim| JEL Codes: G3, G33, G24
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
