A Model for Screening Vulnerability in the Loan Market in the Context of Credit Rationing

A Model for Screening Vulnerability in the Loan Market in the Context of Credit Rationing

Saeed Asadi Bagloee (PARSONS, Dubai, United Arab Emirates), Mohsen Asadi (Kharazmi University, Tehran, Iran) and Cyrus Mohebbi (Department of Information, Operation and Management Science, Kaufman Management Center, New York University, Columbia University, New York, NY, USA; Morgan Stanley, Purchase, USA)
Copyright: © 2014 |Pages: 17
DOI: 10.4018/ijsds.2014010104
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Abstract

The loan market has contributed to the success and failure of economies. Examples of such failures are the US subprime mortgage crisis as well as the global economic meltdown that followed. Many factors influence the loan market, making it volatile and vulnerable. As such, it is important to understand the extent of its vulnerability. Such uncertainties emerge from asymmetric information in the loan market that may lead to credit rationing. Many studies have been devoted to exploring theoretical aspects of the credit market. However, before delving into the theory, it is important to understand and analyze empirical data. Having said that, the literature has yet to provide reliable methodologies for analyzing the empirical data of the loan market. Therefore, given an empirical survey, this study provides a model describing borrowers' behavior in the loan markets. Borrowers are faced with a variety of loan contracts with different terms and conditions from different banks. Logit models can be used to capture the borrowers' choice of bank. Credit is not easily available rather it is rationed and borrowers compete to obtain their required credit via best suited banks offers. The competition is guaranteed by developing a mathematical programming formulation (an objective function subject to constraints) integrated with the logit models for which a solution algorithm using Successive Coordinate Descent was developed. Numerical results of the methodology are presented. Loan terms and conditions as well the borrowers characteristics and preferences are captured in the logit models as explanatory variables. The methodology allows sensitivity analysis on the explanatory variables demonstrating the fluctuation and vulnerability of credit flow.
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1. Introduction

Credit flow is the blood of a vibrant economy, hence carefully watching and ensuring its smoothness is essential. Banks sit at cross points on the path of credit flow. Money is deposited at banks and it is then lent to borrowers. Many factors affecting the credit market have made it highly volatile and vulnerable. Such that the current global economic crisis stemmed from the US subprime mortgage crisis of 2008. Therefore it is important to deepen our understanding of the bank-borrower relationship as well as the factors contributing in the vulnerability and uncertainty of the credit market.

On one hand borrowers need credit to fund their projects seeking loans with convenient terms and conditions such as low interest rate, low collateral requirements, low application fee etc. On the other hand banks seek responsible borrowers who pay back installments on time with no risk of default. From the borrowers perspective the terms and conditions of the loans are clear for each bank. As such, borrowers can compare different banks and choose the best suited one(s). However, from the banks’ view there is no absolute mechanism to assess credit riskiness of the borrowers. Compared to the borrowers, lenders have less information about the projects making information asymmetric or imperfect.

Asymmetric information may rise from imperfect conditions such as risk, uncertainty and vulnerability and is not limited to the credit market. In fact, it is expected to appear wherever there are flow, human decision and competition involved such as transportation (Bagloee et al., 2013), Supply Chain (Ajmal & Kristianto, 2010), Stock Market (Joseph & Mazouz, 2010), negotiation (Neves & Nakhai 2011), insurance market (Hanafizadeh et al., 2013), search algorithms on internet (Nagurney et al., 2007), information technology (Di Caprio & Santos-Arteaga 2011), manufacturing (Stecke et al., 2012), Consumer theory (Boone, 2012) and management science (Davies 2010).

Traditionally, banks have had two major tools to screen borrowers: interest rate and collateral. Those tools are used to compensate the asymmetric information. Interest rate and collateral are aimed to hedge banks against unknown risks associated with the borrowers’ projects, while the risks are better known to the borrowers (and the borrowers are not willing to disclose the extent of the risks to the banks).

Interest rate is the price of credit flow in the credit market. In general when demand increases, price will increase to balance supply and demand. In the credit market, not every borrower gets its credit demand met, even when there is no credit supply shortage and the borrowers is willing to pay higher interest rates. On one hand default by riskier borrowers brings down banks’ revenue, on the other hand riskier borrowers are willing pay higher interest rates. Therefore the interest rate stabilizes at a certain level, below which banks are not lending causing credit to become rationed. Credit rationing implies some (good or bad) borrowers may not get their credit demand fully met. Credit rationing is a stigma of the market that is supposed to be perfect and competitive but it is not.

In addition to imperfect (or asymmetric) information, there are other potential causes to the imperfect market such as bankruptcy costs, agency conflict, transaction costs and taxes asymmetry. The recent financial crisis has shown how far from perfect financial markets can be (Ćorić, 2010).

Stiglitz and Weiss (1981) analyzed several models of credit rationing in markets with imperfect information that won the Nobel Prize for Stiglitz in 2001. Since then, many studies have been dedicated to this topic, some with conflicting results due to lack of reliable methods to analyze empirical data (Bester, 1985; Riley, 1987; De Meza & Webb, 1987; Coco, 1997; Lensink & Sterken, 2002; De Meza & Webb, 2006; Arnold & Riley, 2009; Arnold 2012).

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