Comments on: Multicriteria Decision Systems for Financial Problems

The final publication is available at Springer via http://dx.doi.org/10.1007/s11750-013-0280-1Pla Santamaría, D.; García Bernabeu, AM. (2013). Comments on: Multicriteria Decision Systems for Financial Problems. TOP. 21(2):275-278. doi:10.1007/s11750-013-0280-1S275278212Arrow KJ (1965) Aspects of the theory of risk-bearingBallestero E (2001) Stochastic goal programming: a mean-variance approach. Eur J Oper Res 131(3):476–481Copeland TE, Weston JF (1988) Financial theory and corporate policy. Addison-Wesley, ReadingDoumpos M, Zopounidis C (2010) A multicriteria decision support system for bank rating. Decis Support Syst 50(1):55–63Doumpos M, Zopounidis C (2011) A multicriteria outranking modeling approach for credit rating. Decis Sci 42(3):721–742Geanakoplos J (2001) Three brief proofs of arrow’s impossibility theorem. Yale Cowles Foundation discussion paper (1123RRR)Konno H, Yamazaki H (1991) Mean-absolute deviation portfolio optimization model and its applications to Tokyo Stock Market. Manag Sci 37(5):519–531Saaty TL, Ozdemir MS (2003) Why the magic number seven plus or minus two. Math Comput Model 38(3):233–244Sun S, Lu WM et al. (2005) A cross-efficiency profiling for increasing discrimination in data envelopment analysis. Inf Syst Oper Res 43(1):5

Financial crises and bank failures: a review of prediction methods

In this article we analyze financial and economic circumstances associated with the U.S. subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. We suggest that the level of cross-border holdings of long-term securities between the United States and the rest of the world may indicate a direct link between the turmoil in the securitized market originated in the United States and that in other countries. We provide a summary of empirical results obtained in several Economics and Operations Research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults; we also extensively outline the methodologies used in them. The intent of this article is to promote future empirical research for preventing financial crises.Subprime mortgage ; Financial crises

Risk Assessment of Transitional Economies by Multivariate and Multicriteria Approaches

This article assesses country-risk of sixteen Central, Baltic and South-East European transition countries, for 2005 and 2007, using multivariate cluster analysis. It was aided by the appropriate ANOVA (analysis of variance) testing and the multicriteria PROMETHEE method. The combination of methods makes for more accurate and efficient country-risk assessment.Country risk classifications and ratings involve evaluating the performance of countries while considering their economic and socio-political characteristics. The purpose of the article is to classify, and then find the comparative position of each individual country in the group of analyzed countries, in order to find out to which extent development of market economy and democratic society has been achieved.Country-risk, Transition countries, Multivariate cluster analysis, PROMETHEE method.

Financial crises and bank failures: a review of prediction methods

In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets

ASSESSING SUSTAINABILITY IN AGRICULTURE: A MULTICRITERIA APPROACH

Environmental Economics and Policy,

COMPARATIVE ANALYSIS OF SOME PROMINENT MCDM METHODS: A CASE OF RANKING SERBIAN BANKS

In the literature, many multiple criteria decision making methods have been proposed. There are also a number of papers, which are devoted to comparison of their characteristics and performances. However, a definitive answer to questions: which method is most suitable and which method is most effective is still actual. Therefore, in this paper, the use of some prominent multiple criteria decision making methods is considered on the example of ranking Serbian banks. The objective of this paper is not to determine which method is most appropriate for ranking banks. The objective of this paper is to emphasize that the use of various multiple criteria decision making methods sometimes can produce different ranking orders of alternatives, highlighted some reasons which lead to different results, and indicate that different results obtained by different MCDM methods are not just a random event, but rather reality

A multicriteria approach to manage credit risk under strict uncertainty

[EN] Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there is no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms.Pla Santamaría, D.; Bravo Selles, M.; Reig-Mullor, J.; Salas-Molina, F. (2021). A multicriteria approach to manage credit risk under strict uncertainty. Top. 29(2):494-523. https://doi.org/10.1007/s11750-020-00571-0S494523292Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Reviews Comput Stat 2(4):433–459Adams W, Einav L, Levin J (2009) Liquidity constraints and imperfect information in subprime lending. Am Econ Rev 99(1):49–84Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23(4):589–609Altman EI, Iwanicz-Drozdowska M, Laitinen EK, Suvas A (2016) Financial and non-financial variables as long-horizon predictors of bankruptcy. J Credit Risk 12(4):49–78Angilella S, Mazzù S (2015) The financing of innovative smes: a multicriteria credit rating model. Eur J Oper Res 244(2):540–554Armor DJ (1973) Theta reliability and factor scaling. Sociol Methodol 5:17–50Avery RB, Bostic RW, Calem PS, Canner GB (2000) Credit scoring: statistical issues and evidence from credit-bureau files. Real Estate Econ 28(3):523–547Ballestero E (2002) Strict uncertainty: a criterion for moderately pessimistic decision makers. Decis Sci 33(1):87–108Ballestero E (2006) Ranking alternatives from the decision maker’s preferences: an approach based on utility and the notion of marginal action. J Oper Res Soc Jpn 49(1):49–65Ballestero E, Pla-Santamaria D (2004) Selecting portfolios for mutual funds. Omega 32(5):385–394Ballestero E, Romero C (1998) Multiple criteria decision making and its applications to economic problems. Kluwer Academic Publishers, DordrechtBallestero E, Günther M, Pla-Santamaria D, Stummer C (2007) Portfolio selection under strict uncertainty: a multi-criteria methodology and its application to the frankfurt and vienna stock exchanges. Eur J Oper Res 181(3):1476–1487Bartlett MS (1937) Properties of sufficiency and statistical tests. Proc R Soc Lond Ser A Math Phys Sci 160(901):268–282Bellman R (1957) Dynamic programming. Princeton University Press, New JerseyBravo M, Pla-Santamaria D (2012) Evaluating loan performance for bank offices: a multicriteria decision-making approach. Inf Syst Oper Res 50(3):127–133Breeden JL (2016) Incorporating lifecycle and environment in loan-level forecasts and stress tests. Eur J Oper Res 255(2):649–658Carmines EG, Zeller RA (1979) Reliability and validity assessment. Quantitative applications in the social sciences. SAGE, Thousand OaksCerny BA, Kaiser HF (1977) A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivar Behav Res 12(1):43–47Chen M-Y (2014) Using a hybrid evolution approach to forecast financial failures for taiwan-listed companies. Quant Financ 14(6):1047–1058Conway JM, Huffcutt AI (2003) A review and evaluation of exploratory factor analysis practices in organizational research. Organ Res Methods 6(2):147–168Doumpos M, Zopounidis C (2011) A multicriteria outranking modeling approach for credit rating. Decis Sci 42(3):721–742Edelberg W (2006) Risk-based pricing of interest rates for consumer loans. J Monet Econ 53(8):2283–2298Einav L, Jenkins M, Levin J (2012) Contract pricing in consumer credit markets. Econometrica 80(4):1387–1432Einav L, Jenkins M, Levin J (2013) The impact of credit scoring on consumer lending. Rand J Econ 44(2):249–274Elisabetsky R (1976) Um modelo matemático para decisões de crédito no banco comercial. PhD thesis, Dissertação (Mestrado)–Escola Politécnica, Universidade de São Paulo, São PauloEriksson K, Jonsson S, Lindbergh J, Lindstrand A (2014) Modeling firm specific internationalization risk: an application to banks’ risk assessment in lending to firms that do international business. Int Bus Rev 23(6):1074–1085Fabrigar LR, Wegener DT, MacCallum RC, Strahan EJ (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4(3):272Falangis K (2007) The use of MSD model in credit scoring. Oper Res Int J 7(3):481–503Ford JK, MacCallum RC, Tait M (1986) The application of exploratory factor analysis in applied psychology: a critical review and analysis. Pers Psychol 39(2):291–314Freeman L, Hamilton D (2002) A dream deferred or realized: the impact of public policy on fostering black homeownership in new york city throughout the 1990’s. Am Econ Rev 92(2):320–324George W, Snedecor CWG (1989) Statistical methods. Iowa State University Press, IowaJory SR, Ngo TN, Wang D (2016) Credit ratings and the premiums paid in mergers and acquisitions. J Empir Financ 39:93–104Kaiser HF (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23(3):187–200Kaiser HF (1974) An index of factorial simplicity. Psychometrika 39(1):31–36Kanitz S (1974) Como prever falencias de empresas. Revista de Negocios Em Exame, Dezembro, pp 95–102Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms. Wiley, New YorkKealhofer S (1993) Portfolio management of default risk. KMV Corporation, San FranciscoLaplace P-S (1825) Essai philosophique sur les probabilités. H. Remy, ParisLegault, J. and Score, A. (1987). Ca score. warning system for small business failures. Bilanas, June, pages 29–31Li H, Hong L-Y, Zhou Q, Yu H-J (2015) The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation. Int J Syst Sci 46(11):2072–2086Magee S (2013) The effect of foreign currency hedging on the probability of financial distress. Account Financ 53(4):1107–1127McQuown J (1993) Market versus accounting-based measures of default risk. KMV Corporation, San FranciscoMencía J (2012) Assessing the risk-return trade-off in loan portfolios. J Bank Financ 36(6):1665–1677Merton RC (1974) On the pricing of corporate debt: the risk structure of interest rates. J Financ 29(2):449–470Mester LJ (1997) What’s the point of credit scoring? Bus Rev 3(Sep/Oct):3–16Nunnally JC, Bernstein I (1994) Psychometric theory (McGraw-Hill Series in Psychology), vol 3. McGraw-Hill, New YorkSaunders D, Xiouros C, Zenios SA (2007) Credit risk optimization using factor models. Ann Oper Res 152(1):49–77Sengupta R, Bhardwaj G (2015) Credit scoring and loan default. Int Rev Financ 15(2):139–167Shi J, Xu B (2016) Credit scoring by fuzzy support vector machines with a novel membership function. J Risk Financ Manag 9(4):13Sirignano J, Giesecke K (2019) Risk analysis for large pools of loans. Manag Sci 65(1):107–121Sirignano JA, Tsoukalas G, Giesecke K (2016) Large-scale loan portfolio selection. Oper Res 64(6):1239–1255Spector PE (1992) Summated rating scale construction: An introduction, vol 82. Sage, Newbury ParkSpringate GL (1978) Predicting the possibility of failure in a Canadian firm: a discriminant analysis. PhD thesis, Simon Fraser UniversityThomas LC (2009) Consumer credit models: pricing, profit and portfolios: pricing, profit and portfolios. Oxford University Press, OxfordVasicek O (1984) The philosophy of credit valuation: the credit valuation model. KMV Corporation, San FranciscoWald A (1950) Statistical decision functions. Wiley, New YorkWang J, Wang J (2015) Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing 156:68–78Weber O (2012) Environmental credit risk management in banks and financial service institutions. Bus Strate Environ 21(4):248–263Woerheide W, Persson D (1992) An index of portfolio diversification. Financ Serv Rev 2(2):73–85Yu P-L (1985) Multiple-criteria decision making: concepts, techniques, and extensions. Plenum Press, New YorkZeleny M (1973) Compromise programming. In: Cochrane JL, Zeleny M (eds) Multiple criteria decision making. University of South Carolina Press, Columbia, pp 262–301Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New Yor

A multicriteria approach to manage credit risk under strict uncertainty

Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there is no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms