Multi-criteria analysis of decision-making by international commercial banks for providing shipping loans

Lee, Koung Rae; Pak, Myong-Sop

doi:10.1080/03088839.2018.1448476

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…The fifth independent variable is financial distress. We use companies' free cash flow as a proxy of financial distress because cash liquidity has been found to be an important determinant of financial distress in literature (see Baños‐Caballero et al, 2013; Bonfim, 2009) including shipping‐related studies (see Drobetz et al, 2016; Lee & Pak, 2018). To avoid collinearity issues, we construct a binary variable taking the value of 1 when the company reports negative free cash flow, and the value of 0 when the company reports positive cash flow (see Shamsudin & Kamaluddin, 2015).…”

Section: Methodsmentioning

confidence: 99%

Determinants and effects of trade credit financing: Evidence from the maritime shipping industry

Mantzari,

Merika,

Sigalas

2023

Euro Fin Management

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This paper investigates the factors and effects of trade credit, as an alternative source of capital, by employing a generalized method of moments instrumenting for endogeneity based on a panel data set of public maritime shipping companies and compatible companies in other industries. Our study shows that the magnitude of trade credit is affected by profitability, financial leverage, company size, cost of capital, financial distress, institutional ownership, corporate power, corporate liquidity, asset intensity, and corporate growth. It also suggests that trade credit affects financial performance, equity value, and risk. These empirical findings yield important implications for principal financial officers, as discussed herein.

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Section: Methodsmentioning

confidence: 99%

Determinants and effects of trade credit financing: Evidence from the maritime shipping industry

Mantzari,

Merika,

Sigalas

2023

Euro Fin Management

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“…Gong et al (2013) conducted a questionnaire-based survey of 12 ship-lending banks in Hong Kong over the period [2008][2009] and show that the loan quality and collateral used are the most important factors in assessing default risk. Finally, in a similar study, Lee and Pak (2018) examine bankers' underwriting attitude towards shipping syndicated loans in the post-2008 financial crisis period. Based on a survey of 41 shipping bankers in 11 countries, they find that corporate recourse related criteria (e.g.…”

Section: Shipping Bank Loans and Credit Risk Analysismentioning

confidence: 99%

A survey of shipping finance research: Setting the future research agenda

Alexandridis

Kavussanos

Kim

et al. 2018

Transportation Research Part E: Logistics and Transportation Re

112

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Financing shipping related investment projects has always been a focal area of debate and research within the international maritime industry since access to funding can determine the competitiveness of a capitalintensive business as well as its success or failure under adverse market conditions. This paper provides, for the first time, a comprehensive and structured survey of all published research in the area of shipping finance and investment. The review spans approximately four decades (1979-2018) of empirical evidence, including 162 studies published in 48 scholarly journals, complemented with select books and book chapters. The study provides a bibliometric analysis and comprehensive synthesis of existing research offering an invaluable source of information for both the academic community and business practice, shaping the future research agenda in shipping finance and investment. JEL Code: G10, G20, G30.

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“…Despite the fact that investment banking, equity investment, cash management services, and other banking businesses have grown rapidly in recent years, public shares and real bonds remain relatively common [12]. The asset business of absorbing deposits, issuing on balance sheet and off-balance sheet financing, and earning interest difference by expanding its own assets has the most weight in terms of profit distribution indicators and risk appetite [13]. Due to the high uncertainty and complexity of credit risk, a new technology is urgently needed in current credit risk evaluation work to compensate for the flaws and shortcomings of the old methods [14].…”

Section: Introductionmentioning

confidence: 99%

A Self-Learning BP Neural Network Assessment Algorithm for Credit Risk of Commercial Bank

Liu

2022

Wireless Communications and Mobile Computing

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Commercial banks play an important role in the financial system, and their role in the processes of capital circulation, capital integration, capital resource allocation, and total social demand and supply adjustment is irreplaceable. This paper proposes the idea of constructing a credit risk evaluation system for commercial banks based on BPNN by combining qualitative and quantitative analysis, based on computer technology and management theory. Its main goal is to use a neural network’s self-learning ability to perform complex loan risk assessment. BPNN is capable of self-learning, self-adaptation, and knowledge acquisition, as well as dealing well with uncertainty. It is a nonlinear method that eliminates obvious subjective and artificial factors, resulting in a more objective and effective evaluation result. Experiments show that the established model is effective in regulating personal credit management and reducing investment risks for commercial banks, as well as providing a new decision-making framework for banks’ personal credit business.

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Multi-criteria analysis of decision-making by international commercial banks for providing shipping loans

Cited by 9 publications

References 44 publications

Determinants and effects of trade credit financing: Evidence from the maritime shipping industry

Determinants and effects of trade credit financing: Evidence from the maritime shipping industry

A survey of shipping finance research: Setting the future research agenda

A Self-Learning BP Neural Network Assessment Algorithm for Credit Risk of Commercial Bank

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