Pilar Campoy-Muñoz

A guided data projection technique for classification of sovereign ratings: The case of European Union 27

Sovereign rating has had an increasing importance since the beginning of the financial crisis. However, credit rating agencies opacity has been criticised by several authors highlighting the suitability of designing more objective alternative methods. This paper tackles the sovereign credit rating classification problem within an ordinal classification perspective by employing a pairwise class distances projection to build a classification model based on standard regression techniques. In this work the @e-SVR is selected as the regressor tool. The quality of the projection is validated through the classification results obtained for four performance metrics when applied to Standard & Poors, Moodys and Fitch sovereign rating data of U27 countries during the period 2007-2010. This validated projection is later used for ranking visualization which might be suitable to build a decision support system.

A Multi-Objective Evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling

Bayesian Networks are increasingly being used to model complex socio-economic systems by expert knowledge elicitation even when data is scarce or does not exist. In this paper, a Multi-Objective Evolutionary Algorithm (MOEA) is presented for assessing the parameters (input relevance/weights) of fuzzy dependence relationships in a Bayesian Network (BN). The MOEA was designed to include a hybrid model that combines Monte-Carlo simulation and fuzzy inference. The MOEA-based prototype assesses the input weights of fuzzy dependence relationships by learning from available output data. In socio-economic systems, the determination of how a specific input variable affects the expected results can be critical and it is still one of the most important challenges in Bayesian modeling. The MOEA was checked by estimating the migrant stock as a relevant variable in a BN model for forecasting remittances. For a specific year, results showed similar input weights than those given by economists but it is very computationally demanding. The proposed hybrid-approach is an efficient procedure to estimate output values in BN.

Economic impact assessment of small-scale sporting events using Social Accounting Matrices: an application to the Spanish Football League

ABSTRACT Small-scale sporting events contribute to hosting economies through the expenditures of both sports team activities and sports tourism. Among these sports, football has an increasing importance worldwide, with outstanding competition, such as the Spanish Football League. The goal of this paper was to assess the impact of a team’s promotion on the hosting economy by using linear models based on regionalized Social Accounting Matrices (SAMs). The proposed methodology is applied to the real case of the promotion of the Spanish team Cordoba F.C. over the 2014–2015 season. Estimates are made under two scenarios, conservative and progressive, encompassing different rates of both attendance and average expenditure per spectator and per match. The results highlight that the net expenditures associated with small-scale sporting events benefit the hosting economy, spurring the production of goods and services directly demanded as well as the production of supplier activities.

Remittance flows estimation hybridizing a multilevel fuzzy system and a simulation model

This paper estimates the remittances recorded in Ecuador throughout the time span 2000-2010, when they were the second source of foreign currency of the Andean country. The relationships between covariates, which have effects on these financial flows, are modeled by fuzzy dependence relationships (DR) instead of algebra-based relationships due to the absence or bad quality of data available. A procedure to male fuzzy rules explicit and to evaluate them automatically was designed and tested in a multilevel fuzzy inference engine, which is integrated in a Monte-Carlo simulation model. Firstly, the primary covariates (inputs in a DR) are determined by the simulation model according to expert-based selection of its statistical distributions. Thus, the inference engine evaluated DR outputs once the input values were known, following a hierarchical structure. The results show that this methodology allows us to include expert knowledge in a simulation model.

Assessing the economic impact of a cultural heritage site using social accounting matrices: The case of the Mosque-Cathedral of Cordoba

Heritage sites attract cultural tourists and represent a major source of revenue and employment for the destination’s economy. This article assesses a cultural heritage site’s economic impact on its environment via linear models based on regionalized social accounting matrices. The proposed methodology is applied to data gathered on the Mosque-Cathedral of Cordoba in the year 2013 to determine the heritage site’s contribution to the host economy in terms of production and employment.

Addressing remitting behavior using an ordinal classification approach

Abstract The remittance market represents a great business opportunity for financial institutions given the increasing volume of these capital flows throughout the world. However, the corresponding business strategy could be costly and time consuming because immigrants do not respond to general media campaigns. In this paper, the remitting behavior of immigrants have been addressed by a classification approach that predicts the remittance levels sent by immigrants according to their individual characteristics, thereby identifying the most profitable customers within this group. To do so, five nominal and two ordinal classifiers were applied to an immigrant sample and their resulting performances were compared. The ordinal classifiers achieved the best results; the Support Vector Machine with Ordered Partitions (SVMOP) yielded the best model, providing information needed to draw remitting profiles that are useful for financial institutions. The Support Vector Machine with Explicit Constraints (SVOREX), however, achieved the second best results, and these results are presented graphically to study misclassified patterns in a natural and simple way. Thus, financial institutions can use this ordinal SVM-based approach as a tool to generate valuable information to develop their remittance business strategy.