Is the Ownership Structure Model a Decisive Determinant of Co-Operatives' Financial Success? A Financial Assessment

In this paper, the financial/ownership structures of agribusiness co-operatives (co-ops) are analyzed in order to examine whether new co-op models perform better than the more traditional ones. The assessment procedure introduces a new financial decision-aid approach, which is based on data-analysis techniques in combination with a Preference Ranking Organization Method of Enrichment Evaluations (PROMETHEE II). The application of this multi-criteria decision-aid approach allows the rank ordering of the co-ops on the basis of the most prominent financial ratios. The financial ratios were selected using principal component analysis. This analytical procedure reduces the dimensionality of large numbers of interrelated financial performance measures. We assess the financial success of selected EU agribusiness co-ops for the period 1999-2007. Results show that there is no clear-cut evidence that co-op models used to attract outside equity perform better than the more traditional models. This suggests that ownership structure of co-ops is not a decisive factor for their financial success.Agribusiness cooperatives, financial success, multicriteria decision-aid analysis, Agribusiness,

A multicriteria hierarchical discrimination approach for credit risk problems

Recently, banks and credit institutions have shown an increased interest in developing and implementing credit-scoring systems for taking corporate and consumer credit granting decisions. The objective of such systems is to analyze the characteristics of each applicant (firm or individual) and support the decision making process regarding the acceptance or the rejection of the credit application. This paper addresses this problem through the use of a multicriteria classi - fication technique, the M.H.DIS method (Multi-group Hierarchical DIScrimination). M.H.DIS is applied to real-world case studies regarding the assessment of corporate credit risk and the evaluation of credit card applications. The results obtained through the M.H.DIS method are compared to the results of three wellknown statistical techniques, namely linear and quadratic discriminant analysis, as well as logit analysis.peer-reviewe

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

APPROXIMATION OF LIMIT STATE SURFACES IN MONOTONIC MONTE CARLO SETTINGS

International audienceThis article investigates the theoretical convergence properties of the estimators produced by a numerical exploration of a monotonic function with multivariate random inputs in a structural reliability framework.The quantity to be estimated is a probability typically associated to an undesirable (unsafe) event and the function is usually implemented as a computer model. The estimators produced by a Monte Carlo numerical design are two subsets of inputs leading to safe and unsafe situations, the measures of which can be traduced as deterministic bounds for the probability. Several situations are considered, when the design is independent, identically distributed or not, or sequential. As a major consequence, a consistent estimator of the (limit state) surface separating the subsets under isotonicity and regularity arguments can be built, and its convergence speed can be exhibited. This estimator is built by aggregating semi-supervized binary classifiers chosen as constrained Support Vector Machines. Numerical experiments conducted on toy examples highlight that they work faster than recently developed monotonic neural networks with comparable predictable power. They are therefore more adapted when the computational time is a key issue

Binary choice models for external auditors decisions in Asian banks

Summarization: The present study investigates the efficiency of four classification techniques, namely discriminant analysis, logit analysis, UTADIS multicriteria decision aid, and nearest neighbours, in the development of classification models that could assist auditors during the examination of Asian commercial banks. To develop the auditing models and examine their classification ability, the dataset is split into two distinct samples. The training sample consists of 1,701 unqualified financial statements and 146 ones that received a qualified opinion over the period 1996–2001. The models are tested in a holdout sample of 527 unqualified financial statements and 52 ones that received a qualified opinion over the period 2002–2004. The results show that the developed auditing models can discriminate between financial statements that should receive qualified opinions from the ones that should receive unqualified opinions with an out-of-sample accuracy around 60%. The highest classification accuracy is achieved by UTADIS, followed by logit analysis, nearest neighbours and discriminant analysis. Both financial variables and the environment in which banks operate appear to be important factors.Presented on: Operational Research, An International Journa

Mutual funds performance appraisal using stochastic multicriteria acceptability analysis

Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000–2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables,we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios

Mutual funds performance appraisal using stochastic multicriteria acceptability analysis

Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000–2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables,we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios

Mutual funds performance appraisal using stochastic multicriteria acceptability analysis

Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000–2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables,we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios