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AbstractOver the past decades the complexity of financial decisions has increased rapidly, thus highlighting the importance of developing and implementing sophisticated and efficient quantitative analysis techniques for supporting and aiding financial decision making. Multi‐criteria decision aid (MCDA), an advanced field of operations research, provides financial decision makers (DMs) and analysts a wide range of methodologies, which are well suited to the complexity of financial decision problems. The aim of this paper is to provide an in‐depth presentation of the contributions of MCDA in the field of finance, focusing on the methods used and their real‐world applications. Copyright © 2003 John Wiley & Sons, Ltd.

Intro -- Contents -- List of Figures -- List of Tables -- Introduction -- Reference -- 1 Shipping Industry and Induced Air Pollution -- Abstract -- 1.1 Shipping Industry and Induced Air Pollution -- 1.2 Potential Impacts of Maritime Transport Air Emissions -- 1.3 Growing Rates of Shipping Trade and Environmental Profile of Maritime Industry -- 1.4 The Role of Ports -- References -- 2 Mitigation of Air Emissions: Existing Policy Actions and Legislation -- Abstract -- 2.1 Environmental Legislation on Air Pollutants and Greenhouse Gases Related to the Maritime Sector -- 2.1.1 International Mechanisms for Reducing Maritime Transport Emissions: The Current Debate -- 2.2 Recent Developments in Regulating Greenhouse Gas Emissions from International Shipping -- 2.3 Regulating Ships' Emissions in Ports -- References -- 3 Current Methodologies for the Estimation of Maritime Emissions -- Abstract -- 3.1 Time of Maneuvering and Berthing Mode -- 3.2 Load Factors -- 3.3 Emission Factors -- 3.4 Air Emissions in Ports -- References -- 4 Economic and Social Cost of In-port Ships' Emissions -- Abstract -- 4.1 Review of External Cost of Maritime Emissions at Port -- 4.2 Impact Pathway Approach (IPA) Methodology -- 4.2.1 The Bottom-Up Approach -- 4.2.2 The Top-Down Approach -- 4.3 Assumptions of Research -- References -- 5 The Case of Greek Ports -- Abstract -- 5.1 The Sustainability of Cruise Industry -- 5.2 The Case of Greece -- 5.3 Estimation of Shipping Emissions in Greek Ports -- 5.4 The Social Cost of Shipping Emissions -- References -- Conclusions.

2. Literature Review2.1. Prior Research on Operating and Maintenance Costs; 2.2. Containerships and Their Costs; 3. Hypotheses; 3.1. Age; 3.2. Size; 3.3. Stay Days; 3.4. Market Conditions; 3.5. Owners' Negotiating Capacity; 4. Data and Methodology; 4.1. Data Collected; 4.2. Methodology; 5. Discussion of Results; Conclusion; References; Chapter 6: Discounted Cash Flows through Expertons in Business Valuation Process; Abstract; 1. Introduction; 2. Preliminaries; 2.1. Literature Review in Business Valuation; 2.2. Fuzzy Methodology in Business Valuation; 3. Fuzzy Methodology for Uncertainty.

AbstractAre cryptocurrencies useful minimum‐variance hedging instruments? This paper develops a two‐step analytical framework to explore this question across time. First, it estimates dynamic optimal weights, calibrated when investing between the aggregate market and a respective sampled cryptocurrency. This is performed separately for 11 major cryptocurrencies using the dynamic conditional correlation approach of Engle. Second, using a fractional regression approach, it uncovers linkages between optimal weights in cryptocurrencies and sources of economic uncertainty. Overall, this paper makes the following important findings. First, optimal weights in cryptocurrencies all rose rapidly during the COVID‐19 pandemic. In all, bitcoin showed to be the leading cryptocurrency in terms of hedging effectiveness during this recent time period. Second, most cryptocurrencies exhibit zero or negative betas consistently across time, thus making them natural hedging instruments for investors seeking to reduce their portfolio's comovement with the market. Finally, cryptocurrencies serve as better hedges for economic uncertainties arising from equity and commodity markets. They are relatively less effective for uncertainties arising from risks in the banking industry and firm default risk. This paper contributes broadly to the asset pricing literature since our two‐step approach herein can tractably be extended to other asset classes or other econometric measures of systematic risk.

AbstractIn this paper, we use a sample of 894 banks from 79 countries to develop a multicriteria decision aid model, for the classification of banks into three groups on the basis of their soundness. The model is developed with the UTilités Additives DIScriminantes (UTADIS) method, through a 10‐fold cross‐validation procedure using six financial and four non‐financial variables. The ratings of Fitch form the basis for assigning banks into the three groups. The results indicate that the asset quality (as measured by loan loss provisions), capitalization, and the market where banks operate are the most important criteria (in terms of weights) in classifying the banks. Profitability and efficiency in expenses management are also important attributes, whereas size and listing in a stock exchange are the least important ones. UTADIS achieves higher classification accuracies than discriminant analysis and ordinary logistic regression which are used for benchmarking purposes. Copyright © 2007 John Wiley & Sons, Ltd.

ABSTRACTThis paper presents a real application of a multicriteria decision aid (MCDA) approach to portfolio selection based on preference disaggregation, using ordinal regression and linear programming (UTADIS method; UTilités Additives DIScriminantes). The additive utility functions that are derived through this approach have the extrapolation ability that any new alternative (share) can be easily evaluated and classified into one of several user‐predefined groups. The procedure is illustrated with a case study of 98 stocks from the Athens stock exchange, using 15 criteria. The results are encouraging, indicating that the proposed methodology could be used as a tool for the analysis of the portfolio managers' preferences and choices. Furthermore, the comparison with multiple discriminant analysis (either using a stepwise procedure or not) illustrates the superiority of the proposed methodology over a well‐known multivariate statistical technique that has been extensively used to study financial decision‐making problems.