Person:
Segovia Vargas, María Jesús

First Name

María Jesús

Last Name

Segovia Vargas

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Económicas y Empresariales

Department

Economía Financiera, Actuarial y Estadística

Area

Economía Financiera y Contabilidad

Identifiers

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Search Results

Now showing 1 - 10 of 25

Curso de auto-aprendizaje de matemáticas financieras con excel
(2017-06-28) Segovia Vargas, María Jesús; Blanco Hernandez, Teresa; Ferreiro Pérez, Roberto; Gelashvili, Vera
El objetivo de este proyecto es diseñar un curso introductorio a Excel para la asignatura Financial Mathematics del grupo en inglés del Grado en Administración y Dirección de Empresas, al objeto de crear un material ad hoc de autoaprendizaje para dicho grupo.
Sheltered Employment Centres: Sustainability and Social Value
(MDPI, 2021-07-15) Segovia Vargas, María Jesús; Camacho Miñano, María del Mar; Pedrosa Alberto, Fernanda Cristina; Gelashvili, Vera
Sheltered employment centres are social enterprises where at least 70% of their workers have disabilities. They are a way of helping people with disabilities to work in good working conditions and of allowing disadvantaged people to live a full life. However, some people criticise these businesses for being ghettos where public subsidies are used inefficiently. Our paper aims to test if this criticism is valid by analysing whether these companies provide social and economic value to society in return for public funding and are also economically sustainable over time. Using a sample of 997 Spanish sheltered employment centres, a descriptive analysis of the main variables has been carried out. Additionally, the results of a PART algorithm show the relationship between these companies and economic sustainability. Our findings corroborate that these firms are economically sustainable and, at the same time, socially sustainable. These results highlight the great work that such companies perform for society and the country’s economy.
Predicción de crisis empresariales en seguros no vida mediante la metodología Rough Set
(Universidad Complutense de Madrid, Servicio de Publicaciones, 2004) Segovia Vargas, María Jesús; Gil Fana, José Antonio; Heras Martínez, Antonio
La insolvencia, su temprana detección o el conocimiento de las condiciones que pueden conducir a ella, en una compañía de seguros es una de las principales preocupaciones de legisladores, consumidores y directivos de este tipo de entidades. Esta preocupación surge como resultado de la necesidad de proteger al público de las consecuencias de las insolvencias de los aseguradores, por un lado, y la necesidad de minimizar la carga que supone para el estado hacer frente a las mismas a través de los fondos de garantía, por otro. Además una función importante de los gobiernos es regular el sector asegurador y a través de ella, controlar la solvencia del mismo. Se han aplicado numerosos métodos estadísticos para afrontar este problema utilizando como variables explicativas los ratios financieros. Estas variables no suelen cumplir las hipótesis estadísticas que requieren estos métodos. En consecuencia, hemos aplicado la metodología Rough Set para la predicción de la insolvencia sobre una muestra de empresas españolas de seguros no-vida. Esta metodología presenta, entre otras, estas ventajas: 1,- Es útil para analizar sistemas de información que representan el conocimiento adquirido por la experiencia. 2,- Elimina las variables redundantes reduciendo el coste, en tiempo y dinero, del proceso de decisión. 3,- Se obtienen unas reglas de decisión fácilmente comprensibles que no necesitan interpretación de ningún experto. 4,- Las reglas están bien justificadas por extraerse de ejemplos reales lo que justificaría las decisiones que en base a ellas se tome. Este trabajo completa otras investigaciones previas que aplican la Teoría Rough Set a la predicción de crisis empresariales desarrollando un modelo de predicción de insolvencias en empresas aseguradoras españolas del ramo no-vida. El modelo que hemos desarrollado utiliza tanto ratios financieros generales como específicos para evaluar la solvencia en este sector. Los resultados son muy satisfactorios en comparación con los que obtenemos aplicando el análisis discriminante y demuestran como la Teoría Rough Set puede resultar una herramienta muy útil y novedosa para todos aquellos interesados en evaluar la solvencia de una empresa aseguradora.
Risk-Return modelling in the P2P lending market: Trends, Gaps, Recommendations and future directions
(2020-12-24) Ariza Garzón, Miller Janny; Camacho Miñano, María del Mar; Segovia Vargas, María Jesús; Arroyo Gallardo, Javier
The proposal for new financial products has been accompanied by new tools for risk management and profit in the Peer-to-Peer (P2P) lending market, one market in evolution, as an alternative for traditional investment and financing. For understanding this development, a systematic literature review and a bibliometric analysis of 104 papers published in the Web of Science database in the last decade are carried out using Scimat software. Our aim is to identify methodological elements, modelling components, analysis of variables and business aspects that generate opportunities for deepening its development and application. Developments of algorithms of artificial intelligence (AI) and machine learning (ML) support most of new proposals. Regulators, supervisors and users tend to increasingly seek these new alternatives in a natural project of financial digitalization demanded by technological advances, innovation and market development. Based on this study, recommendations in future research directions are provided for researchers.
Securitization vs. subprime
(DICYT-USFX. Dirección de Investigación, Ciencia y Tecnología de la Universidad San Francisco Xavier de Chuquisaca, 2013-12) Blanco García, Susana; Ramos Escamilla, Maria; Miranda García, Marta; Segovia Vargas, María Jesús
El mecanismo financiero conocido como titulización ha sido considerado como una de las causas más importantes de la crisis financiera actual.Este procedimiento que ha sido aplicado ampliamente en el mercado americano plantea varias preguntas: ¿Ha sucedido en España también? ¿Causa los mismos riesgos a nuestro sistema financiero que al sistema americano? ¿Es necesario renunciar a su uso con el fin de contar con un sistema financiero sólido, estable y saludable?. En esta investigación expresamos la necesidad de implementar medidas de control estrictas y supervisar los mecanismos para administrar el mercado de titulización por todos los participantes involucrados. El objetivo es evitar la falta de uso de este tipo de préstamos, por lo que creemos que puede ser una herramienta útil para su uso.
Social Entrepreneurship in Sheltered Employment Centres: A Case Study of Business Success
(Business Science Reference, 2017) Gelashvili, Vera; Aguilar Pastor, Eva; Segovia Vargas, María Jesús; Camacho Miñano, María del Mar; Blanco Hernández, Mª Teresa
Sheltered Employment centres (CEEs) are part of the social economy companies, based on the primacy of people over capital, social benefits and solidarity. Its aim is to carry out productive work and they are a means of integration of the greatest possible number of disabled people. There is a growing interest in this type of business because its number has increased considerably. The objective of this chapter is to give academic visibility to CEEs due to its great contribution to the social corporate responsibility and to encourage the so-called social entrepreneurship. The reasons for creating social firms are analysed and the characteristics that can contribute to the success of this type of companies are studied. Using the case study methodology, a CEE is analysed in depth showing the main features of social economy business by means of a specific case and the key variables that has conducted to its success.
Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies
(Fundação Escola de Comércio Alvares Penteado, 2015) Segovia Vargas, María Jesús; Camacho Miñano, María del Mar; Pascual Ezama, David
The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.
Rough Sets in insurance sector
(Nova Science Publishers, Inc., 2017) Segovia Vargas, María Jesús; Díaz Martínez, Zuleyka
Rough Set theory methodology belongs to the domain of Artificial Intelligence (AI) and has demonstrated a very high performance in financial issues, especially in classifying problems. Yet, there is little AI research devoted to the insurance industry, although it plays a growing and crucial role in modern economies. The present chapter shows three relevant rough sets researches in insurance sector concluding that this method is an effective tool for supporting managerial decision making in general, and for insurance sector in particular.
Using rough sets to predict insolvency of Spanish non-life insurance companies
(Facultad de Ciencias Económicas y Empresariales. Decanato, 2003) Segovia Vargas, María Jesús; Gil Fana, José Antonio; Heras Martínez, Antonio José; Vilar Zanón, José Luis; Sanchís Arellano, Alicia
Insolvency of insurance companies has been a concern of several parties stemmed from the perceived need to protect the general public and to try to minimize the costs associated to this problem such as the effects on state insurance guaranty funds or the responsibilities for management and auditors. Most methods applied in the past to predict business failure in insurance companies are techniques of statistical nature and use financial ratios as explicative variables. These variables do not normally satisfy statistical assumptions so we propose an approach to predict insolvency of insurance companies based on Rough Set Theory. Some of the advantages of this approach are: first, it is a useful tool to analyse information systems representing knowledge gained by experience; second, elimination of the redundant variables is got, so we can focus on minimal subsets of variables to evaluate insolvency and the cost of the decision making process and time employed by the decision maker are reduced; third, a model consisted of a set of easily understandable decision rules is produced and it is not necessary the interpretation of an expert and, fourth, these rules based on the experience are well supported by a set of real examples so this allows the argumentation of the decisions we make. This study completes previous researches for bankruptcy prediction based on Rough Set Theory developing a prediction model for Spanish non-life insurance companies and using general financial ratios as well as those that are specifically proposed for evaluating insolvency of insurance sector. The results are very encouraging in comparison with discriminant analysis and show that Rough Set Theory can be a useful tool for parties interested in evaluating insolvency of an insurance firm.
Application of Support Vector Machines in Evaluating the Internationalization Success of Companies
(Institute of Physics Publishing, 2018) Rustam, Z; Yaurita, F; Segovia Vargas, María Jesús
The internationalization started to be seen as an opportunity for many companies. This is one of the most crucial growth strategies for companies. Internationalization can be defined as a corporative strategy for growing through foreign markets. It can enhance the product lifetime and improve productivity and business efficiency. However, there is no general model for a successful international company. Therefore, the success of an internationalization procedure must be estimated based on different variables such as the status, strategy, and market characteristics of the company. In this paper, we try to build a model in evaluating the internationalization success of a company based on existing past data by using Support Vector Machines. The results are very encouraging and show that Support Vector Machines can be a useful tool in this sector. We found that Support Vector Machines achieved 81.36% accuracy rate with RBF Kernel, 80% training set, and