Marine EngineeringAs the financing institution of the EU, the European Investment Bank (EIB) has a long history of investments in the maritime sector. The EIB's support for the sector is guided by EU maritime policy which is increasingly influenced by nonfinancial criteria such as safety, environment and employment. The increasing inclusion of non-financial criteria into financial decisions adds to the degree of subjectivity involved in project investment decisions, especially ones involving public funds. This subjectivity is present in individual decision maker's thought processes when assessing the relative importance of each criterion. Within this context, this paper examines a methodology which combines established financial analysis methods with multi criteria decision analysis (MCDA) in an effort to address this complex issue. The aim is to develop a model, which incorporates financial and non-financial criteria whilst accounting for the inherent subjectivity in investment decision making, in a transparent and auditable manner.The paper examines the application of the model to a fleet expansion project which has been financed by the EIB. Further research is proposed including ways in which the model could also be utilised as a performance indictor to track the degree to which EIB financing meets the goals outlined in EU maritime policy.

Corporate financial distress (FD) prediction models are of great importance to all stakeholders, including regulators and banks, who rely on acceptable estimates of default risk, for both individual borrowers and bank loan portfolios. Whilst this subject has been covered extensively in finance research, its application to international shipping companies has been limited while the focus has mainly been on the application of traditional linear modelling, using sparse, cross-sectional financial statement data. Insufficient attention has been paid to the noisy and incomplete nature of shipping company financial statement information. This study contributes to the literature through the design, development and testing of a novel holistic machine learning methodology which integrates predictor evaluation and missing data analysis into the distress prediction process. The model was validated using a longitudinal dataset of over 5,000 company year-end financial statements combined with macroeconomic and market predictors. We applied this methodology first for individual company level distress prediction before testing the models' ability to provide accurate confidence intervals by backtesting conditional value-at-risk estimations of the distress rates for bank portfolios. We conclude that, by adopting a holistic approach, our methodology can enhance financial monitoring of company loans and bank loan portfolios thereby providing a practical "early warning system" for financial distress.

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