Credit risk determinants

: the role of foreign direct investment, taxes and fintech investment in the level of nonperforming loans of US commercial banks

Abstract

Identifying the determinants of nonperforming loans and understanding their impact is of crucial importance given that it is proven that financial institutions report a large proportion of nonperforming loans prior to insolvency, increasing the risk of a banking crisis. Nonperforming loans also constitute the main metric of credit risk measurement, with this risk being the most significant for financial institutions. Using data of 408 US commercial banks to derive bank specific variables, while also considering macroeconomic variables, over the period of 2008 to 2021, this Dissertation provides evidence of the importance and impact of both sets of variables in the level of nonperforming loans. Through the use of a dynamic panel data estimation model, known as the Generalized Method of Moments, introduced by Arellano and Bover (1995) and Blundell and Bond (1998), this research project tests the impact of a set of variables, specifically, of new or little explored variables in the US banking industry, e.g. foreign direct investment, fintech investment, and personal taxes. Model estimates derive the significance of these variables for the determination of nonperforming loans of commercial banks, showing a negative relationship for the foreign direct investment variable and a positive relationship for the fintech investment and personal taxes variables. Moreover, results also show that the lagged value of nonperforming loans is significant to determine their future value, thus concluding on the past literature finding of time persistence of nonperforming loans.

Date of Award	25 Jan 2023
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Diptes Chandrakante Prabhudas Bhimjee (Supervisor)