Publication Date

4-29-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy in International Business Administration (Ph.D.-IB)

Committee Chair

Shankar, Siddharth

Abstract

The purpose of this dissertation is to contribute to the syndicated loan literature. More specifically, this dissertation is composed of three chapters and each chapter explores different aspects. First, we examine syndicated loan’s structure in the presence of regulatory bail-outs. The Troubled Asset Relief Program (TARP) was implemented during the 2009 economic downturn to stimulate the credit flow. However, the low cost of capital could have imprudently increased lenders’ credit risk-appetite. In three different measures, we find that TARP effectively prevented moral hazard by its participants as evidenced by the syndicated loans’ more diversified structures. Further study at lender level suggests that TARP’s impact was heterogeneous. In the case of TARP participants that are lead arrangers in the syndicate, average bank share has increased. This result is robust to propensity score matching and instrument variable approaches. Next, we explore syndicated loan’s terms. We look at how the reputation of lead arrangers or the auditors might signal the borrower’s credit quality, thus determine loan terms. We find that when the borrower has either reputable lead arrangers or Big 4 auditors, it benefits by receiving more favorable terms. Loan amount increases, maturity extends while interest spread narrows and the number of financial covenants decreases. If the borrower has both, it is even better. An average borrower who has a Top 10 lead arranger and a Big 4 auditor at the same time could reduce its loan price by about 37bps, the number of financial covenants by about 0.2 units and increase loan amount and maturity materially by about 121.3 million USD and a year respectively. Finally, we focus on lender’s role in the syndicate. The research question here is which banks have a higher chance of receiving lead mandates? The results based on logistic regression show that the past relationship with the borrower increases that bank's odds of winning the lead mandate by 34%. Moreover, while being a top 10 lender increases the odds of winning the lead mandate by 21%, specialization in the borrower’s industry increases it by even more, at 47%. When we handle the identification problem, results improved significantly.

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