Default risk drivers of shipping bank loans

Bank loans are historically the most popular source of capital in the shipping industry to finance a newbuilding or second-hand ship acquisition (see Albertijn et ah, 2011). They are based on relationship banking and provide faster access to the required capital in comparison to other sources of finance. This is especially important in a market where speed of decision-making can make the difference when striving to achieve the best deal within a short time window. Moreover, it leaves the ownership structure of the company unchanged, which is not the case for Initial Public Offerings (IPOs). In addition, bank loans do not require the shipping company to disclose in the general public any of its business information, which again is not the case for IPOs and bond issues. For a thorough discussion on this, see Kavussanos and Tsouknidis (2014).

In the general corporate finance literature, a system of credit risk assessment, namely the “5Cs of credit”, standing for Capacity, Capital, Collateral, Conditions and Character, has emerged from the 1960s (for details see Smith, 1964), and is widely used for the systematic classification of the default risk drivers in loan facilities (see for instance Chen et ah, 2009). Later, a sixth “C” is added to account for the Company effect. When assessing credit risk, several explanatory variables could be used in a credit scoring (logit) model to assess the PD of a shipping company, a shipping loan or a shipping bond. For example, Kavussanos and Tsouknidis (2011; 2016), using proprietary data, developed for the first time a credit scoring model for the assessment of credit risk in shipping bank loans. A wide set of variables is included in their credit scoring model. This is to ensure that the credit risk assessment takes into account not only the specific characteristics of the loan under consideration but also the state of the shipping industry and the macroeconomic climate in general.

Table 13.7 presents the set of factors examined by Kavussanos and Tsouknidis (2016) as possible explanatory variables when constructing a logit (credit score) model for shipping

Table 13.7 Default risk drivers used as explanatory variables of shipping bank loan defaults

Croup of explanatory variables

Panel A: Financial, company and loan related variables

6C’s mapping

Description

Expected

sign

Dependent

variable

Default Indicator: Takes value 0 if the loan is still active and follows the repayment schedule and value 1 if a missed payment for a loan is recorded for a period longer than 90 days.

Financial-specific

variables

Capital/Company

Leverage = Total Liabilities (TL)/Total Assets (ТА): Leverage measures the total debt burden of the company compared with its total assets.

+

Capital/Company

Debt Coverage Ratio I = Current Liabilities (CL)/EBITDA: The debt coverage ratio measures the current liabilities of the company in comparison with its current EBITDA.

+

Capital/Company

Debt Coverage Ratio 2 = Long Term Debt (LTD)/EBITDA: This debt coverage ratio measures the long term liabilities as a percentage of EBITDA.

+

Capital/Company

Debt Coverage Ratio 3 = TL/EBITDA: This ratio measures the total liabilities of the company in comparison to its EBITDA.

+

Capital/Company

Debt Coverage Ratio 4 = Interest Expenses/ EBITDA: This ratio measures the interest expenses of the company as a percentage of its EBITDA.

+

Capital/Company

Liquidity Ratio 1 = Current Ratio = Current Assets (CAJ/Current Liabilities (CL): The current ratio measures how many times the current assets of the company cover its current liabilities.

Capital/Company

Liquidity Ratio 2 = Cash Reserves Ratio =

Cash and Cash Equivalents/Total Assets (ТА): This ratio measures the proportion of the total assets of a company which is kept as cash and cash equivalents.

Capital/Company

Profit Margin Ratio = EBITDA /Revenue: The profit margin measures the percentage of profit for each unit of revenue.

Character/Capacity/

Company

Age: The time period the shipping company exists.

Company characteristics’- specific variables

Character/Capacity/

Company

Years of cooperation with the bank (YOCB): The number of years of cooperation between the bank and the shipping company since the first loan agreement made between them.

Character/Capacity/

Company

TC Policy: A dummy variable taking the value of 1 for time chartered vessels and 0 for voyage chartered ships.

Loan-specific

variables

Arrangement Fee/Amount of Loan: The arrangement fee is the main sources of profit for the bank along with the margin (spread) charged. These fees are computed as a percentage of the whole amount of the loan. Life to Final Maturity (tenor of the loan): The number of years from the initiation of the agreement until the final maturity of the loan and the balloon payment.

Margin: The spread over LIBOR charged. Internal Bank Rating: The variable internal bank rating, refers to the internal risk rating assigned by the bank, based on the overall assessment of each credit loan application (1 = Completely Safe, 7 = Acceptable risk, >7 Non-acceptable risk).

+

+ /-

+

+

Collateral

Asset Coverage Ratio (ACR) Contractual: The market value of the ship over the amount of loan. This ratio is defined by the bank as a threshold which should be maintained at lower levels than the ACR actual.

Collateral/Conditions

Asset Coverage Ratio (ACR) Actual: The market value of the ship as estimated by ship- brokers or internal sources of the bank over the amount of the loan. This ratio changes over time since the price of the vessel may fluctuate and the debt outstanding is also been reducing during the repayment schedule of the loan.

Source: Kavussanos and Tsouknidis (2016)

bank loans. Financial-specific, company-specific and loan-specific variables have been examined as potential default risk drivers of shipping bank loans. Also, each variable has been mapped to the 6Cs. The results reveal that the important factors in predicting the probability of default are those that measure current and expected future conditions in the shipping market, the risk appetite of the obligor as captured from their choice of the chartering policy of the vessel and the relationship banking effect as captured in the pricing of the loan through the “arrangement fee” variable. These results can be generalised to risky international industries operating under conditions of perfect competition. Specifically, factors mediating the volatile cash-flow risks are considered to be the most important, while more traditional types of information such as that carried in financial ratios seem to come second in line, without being irrelevant. Typically, such credit scoring models are used by the credit analysis departments of banks specialising in transportation and ship finance or by shipping departments of commercial and investment banks lending to the industry.

Shipping bank loans can be classified into syndicated and non-syndicated ones. The term syndicated means that more than one bank is co-financing a shipping project. Syndicated loans carry the benefit of diversifying the multiple risks faced by a bank financing a shipping project and this is the reason that this type of loan has grown in popularity. Specifically, syndicated loans are always organised in principle by an “arrangement” bank which plays the leading role in the whole process. If a default occurs, typically there is negotiation between the two parties where the arrangement bank may agree to provide a refinancing of the loan by extending its maturity and/or charge a higher spread. In this case, the borrower is downgraded in the internal credit rating scale of the bank and the whole amount of the loan is added to Non-Performing Loans (NPLs) which eventually harms the financial statements of the bank. Thus, it is extremely useful for banks to mitigate the credit risk originating from NPLs by identifying from the outset the correct risk level entailed in a bank loan application and as a consequence the potential defaults. Even if a bank does not eventually lose the whole amount of a defaulted loan, the process of refinancing and amending the terms of the loan is a major issue, i.e. extending its maturity, charge higher spreads, transfer the missed payment to the remaining payments. This is because such arrangements create substantial operational cost and may significantly alter a bank’s asset value and operational strategy. For these reasons it is a priority for a financial institution to develop a model that can detect such cases from the outset.

 
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