Measuring the Technical Efficiency for the Shipping Banks—An Approach Using Data Envelopment Analysis

Abstract

The international transportation industry involves various sectors, shipping being one with particular characteristics which differentiates it from others especially as relevant capital risk is concerned. Within this scope, shipping banks are required to assess a number of factors in order to limit the risk from loans, considering the investment capital required. The efficiency of shipping banks is particularly important as it may affect the borrowing level and consequently the financial situation and investment activity in shipping market. This paper examines the Technical Efficiency (TE) of 71 banks operating worldwide in the maritime sector from 2005 to 2010, which is the period that the shipping industry reached its peak and one of its lowest point, making extremely difficult to secure debt finance in shipping, by using Data Envelopment Analysis (DEA) and presents the factors which may affect their technical efficiency, through the application of Regression Analysis. Based on the paper results, most banks during the study period are technically inefficient, whereas TE is proved to be higher under the assumption of variable returns to scale (VRS DEA model) when comparing to constant returns (CRS DEA model). Statistically significant variables are total deposits and total assets for both TE-CRS and TE-VRS and ROE (Return On Equity) for TE-VRS, providing significant information regarding factors on which management should further focus, in order to maintain and reinforce technical efficiency with respect to their strategy for financing shipping sector.

APPLIED Research on Banking Production Planning and Control in the Housing Investment Process

“…Contemporary literature on performance analysis was researched Coelli et al (2005), Tabatabaei and Bazrkar (2019), Fethi and Pasiouras (2010), Maniati and Sambracos (2017), Ray (2004) and Camioto et al (2017). On this way, the WG concludes that to the specific objectives of this article, the Data Envelopment Analysis (DEA) is a superior method of performance assessment than the other usual methods (Economic Method of Least Squares (LSQ), Total Factor Productivity (TFP) and Stochastic Frontiers (SF).…”

“…This principle was adopted in the modeling procedures of this paper. Maniati and Sambracos (2017) applied DEA method, selected as the most suitable for the measurement of technical efficiency of a group of banks, as can process models with many inputs and outputs in different measures, that enables comparisons, allows the use of input and output vectors and requires lesser degrees of freedom.…”

Bank Specific Factors Affecting the Efficiency of Regional Development Bank in Indonesia

“…The results of research related to Garza-García (2012) show that ROA has a positive effect on bank efficiency. Banks with high profitability ratios can attract customers so that Banks can increase the collection and distribution of funds (Ally and Patel, 2014;Maniati and Sambracos, 2017). Besides, banks with high profitability can manage assets more effectively and efficiently to reduce credit risk.…”

“…Large banks have a high level of efficiency because they have more ability to manage their resources. Besides, banks also tend to be able to pay lower input costs than their competing banks, thereby affecting efficiency levels by considering the services provided (Hauner, 2005;Sufian, 2009;Hassan et al, 2004;Maniati and Sambracos, 2017;Buğan and Ata, 2016). The NIM has a positive but not significant effect on the efficiency of RDB.…”

Dividend Dynamics in the Shipping Industry: A Panel Data Analysis of Partial Adjustment Models

“…Relevant 1 empirical studies utilize national and inter-1 See the meta-analysis by Fernau & Hirsch (2019). menos et al, 2008;Drobetz et al, 2013;Maniati & Sambracos, 2017). The maritime sector's high-risk profile has shaped certain aspects of firm's financial decision-making such as a conservative cash-holding policy (Ahrends et al, 2018) and a higher capital structure speed of adjustment (Drobetz et al, 2013).…”