Mecanismos psicológicos que median en la influencia del establecimiento de objetivos sobre el rendimiento de atletas de resistencia

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Forecasting the environmental, social and governance rating of firms by using corporate financial performance variables: A rough sets approach

[EN] The environmental, social, and governance (ESG) rating of firms is a useful tool for stakeholders and investment decision-makers. This paper develops a rough set model to relate ESG scores to popular corporate financial performance measures. This methodology permits handling with information in an uncertain, ambiguous, and imperfect context. A large database was gathered, including ESG scores, as well as industry sector and financial variables for publicly traded European companies during the period 2013-2018. We carried out 500 simulations of the rough set model for different values in the discretization parameter and different grouping scenarios of firms regarding ESG scores. The results suggest that the variables considered are useful in the prediction of ESG rank when firms are clustered in three or four equally balanced groups. However, the prediction power vanishes when a larger number of groups is computed. This would suggest that industry sector and financial variables serve to find big differences across firms regarding ESG, but the significance of the model drops when small differences in ESG performance are scrutinized.García García, F.; González-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J. (2020). Forecasting the environmental, social and governance rating of firms by using corporate financial performance variables: A rough sets approach. Sustainability. 12(8):1-18. https://doi.org/10.3390/su12083324S118128García-Rodríguez, F. J., García-Rodríguez, J. L., Castilla-Gutiérrez, C., & Major, S. A. (2013). Corporate Social Responsibility of Oil Companies in Developing Countries: From Altruism to Business Strategy. Corporate Social Responsibility and Environmental Management, 20(6), 371-384. doi:10.1002/csr.1320García, González-Bueno, Oliver, & Riley. (2019). Selecting Socially Responsible Portfolios: A Fuzzy Multicriteria Approach. Sustainability, 11(9), 2496. doi:10.3390/su11092496Arribas, I., Espinós-Vañó, M. D., García, F., & Tamošiūnienė, R. (2019). Negative screening and sustainable portfolio diversification. Entrepreneurship and Sustainability Issues, 6(4), 1566-1586. doi:10.9770/jesi.2019.6.4(2)Martínez-Ferrero, J., Gallego-Álvarez, I., & García-Sánchez, I. M. (2015). A Bidirectional Analysis of Earnings Management and Corporate Social Responsibility: The Moderating Effect of Stakeholder and Investor Protection. Australian Accounting Review, 25(4), 359-371. doi:10.1111/auar.12075Garriga, E., & Melé, D. (2004). Corporate Social Responsibility Theories: Mapping the Territory. Journal of Business Ethics, 53(1/2), 51-71. doi:10.1023/b:busi.0000039399.90587.34Jensen, M. C. (2002). Value Maximization, Stakeholder Theory, and the Corporate Objective Function. Business Ethics Quarterly, 12(2), 235-256. doi:10.2307/3857812Charlo, M. J., Moya, I., & Muñoz, A. M. (2017). Sustainable Development in Spanish Listed Companies: A Strategic Approach. Corporate Social Responsibility and Environmental Management, 24(3), 222-234. doi:10.1002/csr.1403Hillman, A. J., & Keim, G. D. (2001). Shareholder value, stakeholder management, and social issues: what’s the bottom line? Strategic Management Journal, 22(2), 125-139. doi:10.1002/1097-0266(200101)22:23.0.co;2-hMio, C., Venturelli, A., & Leopizzi, R. (2015). Management by objectives and corporate social responsibility disclosure. Accounting, Auditing & Accountability Journal, 28(3), 325-364. doi:10.1108/aaaj-09-2013-1480Venturelli, A., Caputo, F., Leopizzi, R., Mastroleo, G., & Mio, C. (2017). How can CSR identity be evaluated? A pilot study using a Fuzzy Expert System. Journal of Cleaner Production, 141, 1000-1010. doi:10.1016/j.jclepro.2016.09.172Lii, Y.-S., Wu, K.-W., & Ding, M.-C. (2011). Doing Good Does Good? Sustainable Marketing of CSR and Consumer Evaluations. Corporate Social Responsibility and Environmental Management, 20(1), 15-28. doi:10.1002/csr.294Sheikh, S., & Beise‐Zee, R. (2011). Corporate social responsibility or cause‐related marketing? The role of cause specificity of CSR. Journal of Consumer Marketing, 28(1), 27-39. doi:10.1108/07363761111101921Hermawan, A., & Gunardi, A. (2019). Motivation for disclosure of corporate social responsibility: evidence from banking industry in Indonesia. Entrepreneurship and Sustainability Issues, 6(3), 1297-1306. doi:10.9770/jesi.2019.6.3(17)Davis, K. (1967). Understanding the social responsibility puzzle. Business Horizons, 10(4), 45-50. doi:10.1016/0007-6813(67)90007-9Labib Eid, N., & Robert Sabella, A. (2014). A fresh approach to corporate social responsibility (CSR): partnerships between businesses and non-profit sectors. Corporate Governance, 14(3), 352-362. doi:10.1108/cg-01-2013-0011Donaldson, T., & Dunfee, T. W. (2002). Ties that bind in business ethics: Social contracts and why they matter. Journal of Banking & Finance, 26(9), 1853-1865. doi:10.1016/s0378-4266(02)00195-4Jahn, J., & Brühl, R. (2016). How Friedman’s View on Individual Freedom Relates to Stakeholder Theory and Social Contract Theory. Journal of Business Ethics, 153(1), 41-52. doi:10.1007/s10551-016-3353-xSison, A. J. G. (2009). From CSR to Corporate Citizenship: Anglo-American and Continental European Perspectives. Journal of Business Ethics, 89(S3), 235-246. doi:10.1007/s10551-010-0395-3Lee, J. W., & Tan, W. N. (2019). Global Corporate Citizenship: Cross-cultural Comparison of Best Practices in the Global Automotive Industry. The Journal of Asian Finance, Economics and Business, 6(1), 261-271. doi:10.13106/jafeb.2019.vol6.no1.261Wartick, S. L., & Rude, R. E. (1986). Issues Management: Corporate Fad or Corporate Function? California Management Review, 29(1), 124-140. doi:10.2307/41165231Preston, L. E., & Post, J. E. (1981). Private Management and Public Policy. California Management Review, 23(3), 56-62. doi:10.2307/41172602Shnayder, L., van Rijnsoever, F. J., & Hekkert, M. P. (2016). Motivations for Corporate Social Responsibility in the packaged food industry: an institutional and stakeholder management perspective. Journal of Cleaner Production, 122, 212-227. doi:10.1016/j.jclepro.2016.02.030Wartick, S. L., & Cochran, P. L. (1985). The Evolution of the Corporate Social Performance Model. Academy of Management Review, 10(4), 758-769. doi:10.5465/amr.1985.4279099Schwartz, M. S., & Carroll, A. B. (2003). Corporate Social Responsibility: A Three-Domain Approach. Business Ethics Quarterly, 13(4), 503-530. doi:10.5840/beq200313435Cassel, D. (2001). Human Rights and Business Responsibilities in the Global Marketplace. Business Ethics Quarterly, 11(2), 261-274. doi:10.2307/3857749Hart, S. L., & Ahuja, G. (1996). DOES IT PAY TO BE GREEN? AN EMPIRICAL EXAMINATION OF THE RELATIONSHIP BETWEEN EMISSION REDUCTION AND FIRM PERFORMANCE. Business Strategy and the Environment, 5(1), 30-37. doi:10.1002/(sici)1099-0836(199603)5:13.0.co;2-qHang, M., Geyer-Klingeberg, J., & Rathgeber, A. W. (2018). It is merely a matter of time: A meta-analysis of the causality between environmental performance and financial performance. Business Strategy and the Environment, 28(2), 257-273. doi:10.1002/bse.2215McWilliams, A., & Siegel, D. (2001). Corporate Social Responsibility: a Theory of the Firm Perspective. Academy of Management Review, 26(1), 117-127. doi:10.5465/amr.2001.4011987Luo, X., & Bhattacharya, C. B. (2006). Corporate Social Responsibility, Customer Satisfaction, and Market Value. Journal of Marketing, 70(4), 1-18. doi:10.1509/jmkg.70.4.001Seifert, B., Morris, S. A., & Bartkus, B. R. (2004). Having, Giving, and Getting: Slack Resources, Corporate Philanthropy, and Firm Financial Performance. Business & Society, 43(2), 135-161. doi:10.1177/0007650304263919Brammer, S., Brooks, C., & Pavelin, S. (2006). Corporate Social Performance and Stock Returns: UK Evidence from Disaggregate Measures. Financial Management, 35(3), 97-116. doi:10.1111/j.1755-053x.2006.tb00149.xArribas, I., Espinós-Vañó, M. D., García, F., & Oliver, J. (2019). Defining socially responsible companies according to retail investors’ preferences. Entrepreneurship and Sustainability Issues, 7(2), 1641-1653. doi:10.9770/jesi.2019.7.2(59)Arribas, I., Espinós-Vañó, M., García, F., & Morales-Bañuelos, P. (2019). The Inclusion of Socially Irresponsible Companies in Sustainable Stock Indices. Sustainability, 11(7), 2047. doi:10.3390/su11072047BACCARO, L., & MELE, V. (2011). FOR LACK OF ANYTHING BETTER? INTERNATIONAL ORGANIZATIONS AND GLOBAL CORPORATE CODES. Public Administration, 89(2), 451-470. doi:10.1111/j.1467-9299.2011.01918.xChatterji, A. K., Levine, D. I., & Toffel, M. W. (2009). How Well Do Social Ratings Actually Measure Corporate Social Responsibility? Journal of Economics & Management Strategy, 18(1), 125-169. doi:10.1111/j.1530-9134.2009.00210.xGangi, F., & Varrone, N. (2018). Screening activities by socially responsible funds: A matter of agency? Journal of Cleaner Production, 197, 842-855. doi:10.1016/j.jclepro.2018.06.228Utz, S., & Wimmer, M. (2014). Are they any good at all? A financial and ethical analysis of socially responsible mutual funds. Journal of Asset Management, 15(1), 72-82. doi:10.1057/jam.2014.8Roberts, P. W., & Dowling, G. R. (2002). Corporate reputation and sustained superior financial performance. Strategic Management Journal, 23(12), 1077-1093. doi:10.1002/smj.274Julian, S. D., & Ofori-dankwa, J. C. (2013). Financial resource availability and corporate social responsibility expenditures in a sub-Saharan economy: The institutional difference hypothesis. Strategic Management Journal, 34(11), 1314-1330. doi:10.1002/smj.2070Garcia-Castro, R., Ariño, M. A., & Canela, M. A. (2009). Does Social Performance Really Lead to Financial Performance? Accounting for Endogeneity. Journal of Business Ethics, 92(1), 107-126. doi:10.1007/s10551-009-0143-8Dupire, M., & M’Zali, B. (2016). CSR Strategies in Response to Competitive Pressures. Journal of Business Ethics, 148(3), 603-623. doi:10.1007/s10551-015-2981-xLin, W. L., Law, S. H., Ho, J. A., & Sambasivan, M. (2019). The causality direction of the corporate social responsibility – Corporate financial performance Nexus: Application of Panel Vector Autoregression approach. The North American Journal of Economics and Finance, 48, 401-418. doi:10.1016/j.najef.2019.03.004Van Beurden, P., & Gössling, T. (2008). The Worth of Values – A Literature Review on the Relation Between Corporate Social and Financial Performance. Journal of Business Ethics, 82(2), 407-424. doi:10.1007/s10551-008-9894-xJankalová, M., & Jankal, R. (2017). The assessment of corporate social responsibility: approaches analysis. Entrepreneurship and Sustainability Issues, 4(4), 441-459. doi:10.9770/jesi.2017.4.4(4)Marom, I. Y. (2006). Toward a Unified Theory of the CSP–CFP Link. Journal of Business Ethics, 67(2), 191-200. doi:10.1007/s10551-006-9023-7Chen, C.-M., & Delmas, M. (2010). Measuring Corporate Social Performance: An Efficiency Perspective. Production and Operations Management, 20(6), 789-804. doi:10.1111/j.1937-5956.2010.01202.xMuñoz-Torres, M. J., Fernández-Izquierdo, M. Á., Rivera-Lirio, J. M., & Escrig-Olmedo, E. (2018). Can environmental, social, and governance rating agencies favor business models that promote a more sustainable development? Corporate Social Responsibility and Environmental Management, 26(2), 439-452. doi:10.1002/csr.1695Guijarro, F., & Poyatos, J. (2018). Designing a Sustainable Development Goal Index through a Goal Programming Model: The Case of EU-28 Countries. Sustainability, 10(9), 3167. doi:10.3390/su10093167Guijarro, F. (2019). A Multicriteria Model for the Assessment of Countries’ Environmental Performance. International Journal of Environmental Research and Public Health, 16(16), 2868. doi:10.3390/ijerph16162868Escrig-Olmedo, E., Fernández-Izquierdo, M., Ferrero-Ferrero, I., Rivera-Lirio, J., & Muñoz-Torres, M. (2019). Rating the Raters: Evaluating how ESG Rating Agencies Integrate Sustainability Principles. Sustainability, 11(3), 915. doi:10.3390/su11030915Mattingly, J. E. (2015). Corporate Social Performance: A Review of Empirical Research Examining the Corporation–Society Relationship Using Kinder, Lydenberg, Domini Social Ratings Data. Business & Society, 56(6), 796-839. doi:10.1177/0007650315585761Landi, G., & Sciarelli, M. (2019). Towards a more ethical market: the impact of ESG rating on corporate financial performance. Social Responsibility Journal, 15(1), 11-27. doi:10.1108/srj-11-2017-0254Martínez-Ferrero, J., & Frías-Aceituno, J. V. (2013). Relationship Between Sustainable Development and Financial Performance: International Empirical Research. Business Strategy and the Environment, 24(1), 20-39. doi:10.1002/bse.1803Grewatsch, S., & Kleindienst, I. (2015). When Does It Pay to be Good? Moderators and Mediators in the Corporate Sustainability–Corporate Financial Performance Relationship: A Critical Review. Journal of Business Ethics, 145(2), 383-416. doi:10.1007/s10551-015-2852-5López, M. V., Garcia, A., & Rodriguez, L. (2007). Sustainable Development and Corporate Performance: A Study Based on the Dow Jones Sustainability Index. Journal of Business Ethics, 75(3), 285-300. doi:10.1007/s10551-006-9253-8Kang, C., Germann, F., & Grewal, R. (2016). Washing Away Your Sins? Corporate Social Responsibility, Corporate Social Irresponsibility, and Firm Performance. Journal of Marketing, 80(2), 59-79. doi:10.1509/jm.15.0324WADDOCK, S. A., & GRAVES, S. B. (1997). THE CORPORATE SOCIAL PERFORMANCE-FINANCIAL PERFORMANCE LINK. Strategic Management Journal, 18(4), 303-319. doi:10.1002/(sici)1097-0266(199704)18:43.0.co;2-gChin, M. K., Hambrick, D. C., & Treviño, L. K. (2013). Political Ideologies of CEOs. Administrative Science Quarterly, 58(2), 197-232. doi:10.1177/0001839213486984Chung, S. (Andy), Pyo, H., & Guiral, A. (2019). Who Is the Beneficiary of Slack on Corporate Financial Performance and Corporate Philanthropy? Evidence from South Korea. Sustainability, 11(1), 252. doi:10.3390/su11010252El Ghoul, S., Guedhami, O., Kwok, C. C. Y., & Mishra, D. R. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance, 35(9), 2388-2406. doi:10.1016/j.jbankfin.2011.02.007Mishra, S., & Modi, S. B. (2012). Positive and Negative Corporate Social Responsibility, Financial Leverage, and Idiosyncratic Risk. Journal of Business Ethics, 117(2), 431-448. doi:10.1007/s10551-012-1526-9Cheng, B., Ioannou, I., & Serafeim, G. (2013). Corporate social responsibility and access to finance. Strategic Management Journal, 35(1), 1-23. doi:10.1002/smj.2131Bhuiyan, M. B. U., & Nguyen, T. H. N. (2019). Impact of CSR on cost of debt and cost of capital: Australian evidence. Social Responsibility Journal, 16(3), 419-430. doi:10.1108/srj-08-2018-0208Brammer, S., & Millington, A. (2005). Corporate Reputation and Philanthropy: An Empirical Analysis. Journal of Business Ethics, 61(1), 29-44. doi:10.1007/s10551-005-7443-4Sparkes, R., & Cowton, C. J. (2004). The Maturing of Socially Responsible Investment: A Review of the Developing Link with Corporate Social Responsibility. Journal of Business Ethics, 52(1), 45-57. doi:10.1023/b:busi.0000033106.43260.99Hudson, R. (2005). Ethical Investing: Ethical Investors and Managers. Business Ethics Quarterly, 15(4), 641-657. doi:10.5840/beq200515445Oikonomou, I., Brooks, C., & Pavelin, S. (2014). The Effects of Corporate Social Performance on the Cost of Corporate Debt and Credit Ratings. Financial Review, 49(1), 49-75. doi:10.1111/fire.12025Kotchen, M., & Moon, J. J. (2012). Corporate Social Responsibility for Irresponsibility. The B.E. Journal of Economic Analysis & Policy, 12(1). doi:10.1515/1935-1682.3308Flammer, C. (2013). Corporate Social Responsibility and Shareholder Reaction: The Environmental Awareness of Investors. Academy of Management Journal, 56(3), 758-781. doi:10.5465/amj.2011.0744Godfrey, P. C., Merrill, C. B., & Hansen, J. M. (2009). The relationship between corporate social responsibility and shareholder value: an empirical test of the risk management hypothesis. Strategic Management Journal, 30(4), 425-445. doi:10.1002/smj.750LINS, K. V., SERVAES, H., & TAMAYO, A. (2017). Social Capital, Trust, and Firm Performance: The Value of Corporate Social Responsibility during the Financial Crisis. The Journal of Finance, 72(4), 1785-1824. doi:10.1111/jofi.12505Bouslah, K., Kryzanowski, L., & M’Zali, B. (2016). Social Performance and Firm Risk: Impact of the Financial Crisis. Journal of Business Ethics, 149(3), 643-669. doi:10.1007/s10551-016-3017-xKoh, P.-S., Qian, C., & Wang, H. (2013). Firm litigation risk and the insurance value of corporate social performance. Strategic Management Journal, 35(10), 1464-1482. doi:10.1002/smj.2171Drempetic, S., Klein, C., & Zwergel, B. (2019). The Influence of Firm Size on the ESG Score: Corporate Sustainability Ratings Under Review. Journal of Business Ethics, 167(2), 333-360. doi:10.1007/s10551-019-04164-1ADAMS, C. A., HILL, W.-Y., & ROBERTS, C. B. (1998). CORPORATE SOCIAL REPORTING PRACTICES IN WESTERN EUROPE: LEGITIMATING CORPORATE BEHAVIOUR? The British Accounting Review, 30(1), 1-21. doi:10.1006/bare.1997.0060Neu, D., Warsame, H., & Pedwell, K. (1998). Managing Public Impressions: Environmental Disclosures in Annual Reports. Accounting, Organizations and Society, 23(3), 265-282. doi:10.1016/s0361-3682(97)00008-1Brammer, S., & Pavelin, S. (2004). Voluntary social disclosures by large UK companies. Business Ethics: A European Review, 13(2-3), 86-99. doi:10.1111/j.1467-8608.2004.00356.xHaniffa, R. M., & Cooke, T. E. (2005). The impact of culture and governance on corporate social reporting. Journal of Accounting and Public Policy, 24(5), 391-430. doi:10.1016/j.jaccpubpol.2005.06.001Surroca, J., Tribó, J. A., & Waddock, S. (2009). Corporate responsibility and financial performance: the role of intangible resources. Strategic Management Journal, 31(5), 463-490. doi:10.1002/smj.820Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356. doi:10.1007/bf01001956Kim, Y., & Enke, D. (2016). Developing a rule change trading system for the futures market using rough set analysis. Expert Systems with Applications, 59, 165-173. doi:10.1016/j.eswa.2016.04.031Podsiadlo, M., & Rybinski, H. (2016). Financial time series forecasting using rough sets with time-weighted rule voting. Expert Systems with Applications, 66, 219-233. doi:10.1016/j.eswa.2016.08.066Chen, Y.-S. (2012). Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems, 26, 259-270. doi:10.1016/j.knosys.2011.08.021Chen, X., Zhang, X., Zhou, J., & Zhou, K. (2019). Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets. Applied Sciences, 9(6), 1161. doi:10.3390/app9061161Liang, F., Xu, Y., Li, W., Ning, X., Liu, X., & Liu, A. (2017). Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology. Applied Sciences, 7(2), 192. doi:10.3390/app7020192Roy, B. (1993). Decision science or decision-aid science? European Journal of Operational Research, 66(2), 184-203. doi:10.1016/0377-2217(93)90312-bSun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56. doi:10.1016/j.knosys.2013.12.006https://cran.r-project.org/web/packages/RoughSets/index.htm

A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American Integrated Market

[EN] This paper extends the stochastic mean-semivariance model to a fuzzy multiobjective model, where apart from return and risk, also liquidity is considered to measure the performance of a portfolio. Uncertainty of future return and liquidity of each asset are modeled using L-R type fuzzy numbers that belong to the power reference function family. The decision process of this novel approach takes into account not only the multidimensional nature of the portfolio selection problem but also realistic constraints by investors. Particularly, it optimizes the expected return, the semivariance and the expected liquidity of a given portfolio, considering cardinality constraint and upper and lower bound constraints. The constrained portfolio optimization problem resulting is solved using the algorithm NSGA-II. As a novelty, in order to select the optimal portfolio, this study defines the credibilistic Sortino ratio as the ratio between the credibilistic risk premium and the credibilistic semivariance. An empirical study is included to show the effectiveness and efficiency of the model in practical applications using a data set of assets from the Latin American Integrated Market.García García, F.; Gonzalez-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J. (2020). A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American Integrated Market. Enterpreneurship and Sustainability Issues. 8(2):1027-1046. https://doi.org/10.9770/jesi.2020.8.2(62)S102710468

Sistema de autoevaluación y evaluación continuada del grado de aprendizaje del alumnado, mediante metodologias de portafolio electrónico del/la estudiante.

Diseño e implantación de un Campus Virtual y de un portafolio electrónico de auto-evaluación continuada del aprendizaje del/la alumno/a, mediante la plataforma informática Moodle, para la asignatura de Proyectos de la titulación de Enginyeria en Organització Industrial. En este Campus Virtual el/la alumno/a tendrá a su disposición toda la información necesaria para el seguimiento de la asignatura, podrá acceder directamente al actual entorno virtual donde se realizan los trabajos de grupo, en un portafolio electrónico de grupo, y tendrá una zona de auto-evaluación a través de test creados por los profesores/as y a los que el/la alumno/a sólo podrá acceder en determinados momentos del horario de clase. Por medio de los resultados proporcionados tras la realización de cada test, tanto el/la alumno/a como el profesor/a, podrán saber el nivel de los conocimientos aprendidos individualmente con el estudio de cada tema y la realización en grupo del ejercicio correspondiente. Esta batería de test servirá para ayudar al/la alumno/a a consolidar los conocimientos adquiridos, prepararse para la realización del siguiente ejercicio, prepararse para el examen final y para subir la nota final de la asignatura

Sufrimiento competitivo y rendimiento en deportes de resistencia

La literatura que trata de relacionar aspectos precompetitivos como la ansiedad o el estado de ánimo con el rendimiento, en deportes de resistencia, es muy extensa. Sin embargo, las medidas precompetitivas han mostrado importantes inconsistencias en los resultados debido a que no tienen en cuenta las fluctuaciones en la respuesta emocional y en los procesos psicológicos del atleta una vez comenzada la competición (Hammersmeister y Burton, 1995). En esta investigación se estudia la relación de aspectos precompetitivos, como la ansiedad, el estado de ánimo y la autoeficacia, y aspectos acaecidos durante la competición, como las percepciones de amenaza y los recursos de afrontamiento,con el rendimiento. Mientras los aspectos precompetitivos se relacionan pobremente con el rendimiento, las percepciones de amenaza y los recursos de afrontamiento tienen un mayor poder explicativo de éste. Los resultados dan validez al concepto de sufrimiento competitivo que aparece cuando el atleta obtiene la certeza, mientras está compitiendo, de que no alcanzará el objetivo por el que está luchando.Research which attempts to relate precompetitive factors like anxiety or mood state with performance in endurance sports is very extensive. However, precompetitive measurements have showed important incongruence in their results because they don't bear in mind the changes in the emotional response and in the psychological processes of the athlete when the competition has begun (Hammersmeister y Burton, 1995). In this research, we study the relationship between precompetitive aspects -like anxiety, mood and self-efficacy-, aspects which arise when the competition has begun - like treath perceptions and coping resources-, and performance. While precompetitive aspects are poorly related with performance, treath perceptions and coping resources have a bigger explicative power over performance. Results give validity to the competitive suffering concept which arises when the athlete discovers, while competing, that he will not reach the goal which he is fighting for

Evaluación del nivel de actividad física a través de la rigidez musculo-articular en adultos jóvenes

The purpose of the study is to evaluate the level of physical activity of young adults by means of the Musculo-articular stiffness and to analyse its correlation with the physical performance measured in jump capacity. The proposed protocol includes a Muscle-articular test of both legs, a test of maximum voluntary contraction in isometric conditions (MVCi), a countermovement jump test (CMJ), and a drop jump (DJ) protocol from different heights (20, 40 and 60 cm). 21 healthy young adult subjects (12 males and 9 females). The mechanical variables are: force (f), Muscle-articular stiffness (k) and Muscle-articular Unitary stiffness (ku). Physical variables: Jump flight height (h) and force generated (f). An Anova of repeated measurements was performed to analyse the influence of gender and laterality and a Pearson correlation to analyse the relationship between mechanical and physical parameters. The results obtained show a clear symmetry in physical and mechanical parameters. There were significant differences between men and women (f and k) (p smaller than 0.05) being in absolute terms higher in men than in women but not in relative terms (ku). A clear correlation was obtained between mechanical parameters and MVCi in absolute terms (p smaller than 0.05). Ku allows comparisons between different subjects but its interpretation is not as intuitive as in absolute terms due to the application of the Hill’s model on the mechanical response of muscle-tendon complexes that establishes a nonlinear relationship between f and k.El propósito del estudio consiste en evaluar el nivel de actividad física de adultos jóvenes mediante la obtención de la rigidez Musculo-articular y analizar su correlación con el rendimiento físico medido en capacidad de salto. El protocolo propuesto engloba un test Músculo-articular de ambas piernas, un test de Máxima contracción isométrica (MCIV) voluntaria en las mismas condiciones, un test de salto de contramovimiento, y un protocolo de salto de drop jump desde diferentes alturas (20, 40 y 60 cm). 21 sujetos adultos jóvenes sanos (12 hombre y 9 mujeres) conforman la muestra. Las variables mecánicas son: fuerza (f), Rigidez Músculo-articular (k) y rigidez Músculoarticular Unitaria (ku). Variables físicas: Altura de vuelo de salto (h) y fuerza generada (f). Se llevó a cabo un Anova de mediciones repetidas para analizar la influencia del género y lateralidad y una correlación de Pearson para analizar la relación entre parámetros mecánicos y parámetros físicos. Los resultados obtenidos muestran una simetría clara tanto en parámetros físicos como en parámetros mecánicos. Se obtuvieron diferencias significativas entre hombres y mujeres (f y k) (p menor que 0.05) siendo en términos absolutos mayores en hombres que en mujeres pero no en términos relativos (ku). Se obtuvo una clara correlación entre parámetros mecánicos y MCIV términos absolutos (p menor que 0.05). Ku permite comparar entre diferentes sujetos pero su interpretación no es tan intuitiva como en términos absolutos debido a la aplicación del modelo de Hill sobre la respuesta mecánica de los complejos músculo-tendón que establece una relación no lineal entre f y k

Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory

[EN] The present research proposes a novel methodology to solve the problems faced by investors who take into consideration different investment criteria in a fuzzy context. The approach extends the stochastic mean-variance model to a fuzzy multiobjective model where liquidity is considered to quantify portfolio's performance, apart from the usual metrics like return and risk. The uncertainty of the future returns and the future liquidity of the potential assets are modelled employing trapezoidal fuzzy numbers. The decision process of the proposed approach considers that portfolio selection is a multidimensional issue and also some realistic constraints applied by investors. Particularly, this approach optimizes the expected return, the risk and the expected liquidity of the portfolio, considering bound constraints and cardinality restrictions. As a result, an optimization problem for the constraint portfolio appears, which is solved by means of the NSGA-II algorithm. This study defines the credibilistic Sortino ratio and the credibilistic STARR ratio for selecting the optimal portfolio. An empirical study on the S&P100 index is included to show the performance of the model in practical applications. The results obtained demonstrate that the novel approach can beat the index in terms of return and risk in the analyzed period, from 2008 until 2018.García García, F.; González-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2020). Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory. Technological and Economic Development of Economy (Online). 26(6):1165-1186. https://doi.org/10.3846/tede.2020.13189S11651186266Acerbi, C., & Tasche, D. (2002). On the coherence of expected shortfall. Journal of Banking & Finance, 26(7), 1487-1503. doi:10.1016/s0378-4266(02)00283-2Ahmed, A., Ali, R., Ejaz, A., & Ahmad, I. (2018). Sectoral integration and investment diversification opportunities: evidence from Colombo Stock Exchange. Entrepreneurship and Sustainability Issues, 5(3), 514-527. doi:10.9770/jesi.2018.5.3(8)Arenas Parra, M., Bilbao Terol, A., & Rodrı́guez Urı́a, M. V. (2001). A fuzzy goal programming approach to portfolio selection. European Journal of Operational Research, 133(2), 287-297. doi:10.1016/s0377-2217(00)00298-8Arribas, I., Espinós-Vañó, M. D., García, F., & Tamošiūnienė, R. (2019). Negative screening and sustainable portfolio diversification. Entrepreneurship and Sustainability Issues, 6(4), 1566-1586. doi:10.9770/jesi.2019.6.4(2)Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203-228. doi:10.1111/1467-9965.00068Bawa, V. S. (1975). Optimal rules for ordering uncertain prospects. Journal of Financial Economics, 2(1), 95-121. doi:10.1016/0304-405x(75)90025-2Bermúdez, J. D., Segura, J. V., & Vercher, E. (2012). A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection. Fuzzy Sets and Systems, 188(1), 16-26. doi:10.1016/j.fss.2011.05.013Bezoui, M., Moulaï, M., Bounceur, A., & Euler, R. (2018). An iterative method for solving a bi-objective constrained portfolio optimization problem. Computational Optimization and Applications, 72(2), 479-498. doi:10.1007/s10589-018-0052-9Bi, T., Zhang, B., & Wu, H. (2013). Measuring Downside Risk Using High-Frequency Data: Realized Downside Risk Measure. Communications in Statistics - Simulation and Computation, 42(4), 741-754. doi:10.1080/03610918.2012.655826Carlsson, C., Fullér, R., & Majlender, P. (2002). A possibilistic approach to selecting portfolios with highest utility score. Fuzzy Sets and Systems, 131(1), 13-21. doi:10.1016/s0165-0114(01)00251-2Chen, W., & Xu, W. (2018). A Hybrid Multiobjective Bat Algorithm for Fuzzy Portfolio Optimization with Real-World Constraints. International Journal of Fuzzy Systems, 21(1), 291-307. doi:10.1007/s40815-018-0533-0Choobineh, F., & Branting, D. (1986). A simple approximation for semivariance. European Journal of Operational Research, 27(3), 364-370. doi:10.1016/0377-2217(86)90332-2Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Fang, Y., Lai, K. K., & Wang, S.-Y. (2006). Portfolio rebalancing model with transaction costs based on fuzzy decision theory. European Journal of Operational Research, 175(2), 879-893. doi:10.1016/j.ejor.2005.05.020Favre, L., & Galeano, J.-A. (2002). Mean-Modified Value-at-Risk Optimization with Hedge Funds. The Journal of Alternative Investments, 5(2), 21-25. doi:10.3905/jai.2002.319052García, F., González-Bueno, J., Guijarro, F., & Oliver, J. (2020). Forecasting the Environmental, Social, and Governance Rating of Firms by Using Corporate Financial Performance Variables: A Rough Set Approach. Sustainability, 12(8), 3324. doi:10.3390/su12083324García, González-Bueno, Oliver, & Riley. (2019). Selecting Socially Responsible Portfolios: A Fuzzy Multicriteria Approach. Sustainability, 11(9), 2496. doi:10.3390/su11092496García, F., González-Bueno, J., Oliver, J., & Tamošiūnienė, R. (2019). A CREDIBILISTIC MEAN-SEMIVARIANCE-PER PORTFOLIO SELECTION MODEL FOR LATIN AMERICA. Journal of Business Economics and Management, 20(2), 225-243. doi:10.3846/jbem.2019.8317García, F., Guijarro, F., & Moya, I. (2013). A MULTIOBJECTIVE MODEL FOR PASSIVE PORTFOLIO MANAGEMENT: AN APPLICATION ON THE S&P 100 INDEX. Journal of Business Economics and Management, 14(4), 758-775. doi:10.3846/16111699.2012.668859García, F., Guijarro, F., & Oliver, J. (2017). Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing and Applications, 30(8), 2625-2641. doi:10.1007/s00521-017-2882-2García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX. Technological and Economic Development of Economy, 24(6), 2161-2178. doi:10.3846/tede.2018.6394Goel, A., Sharma, A., & Mehra, A. (2018). Index tracking and enhanced indexing using mixed conditional value-at-risk. Journal of Computational and Applied Mathematics, 335, 361-380. doi:10.1016/j.cam.2017.12.015González-Bueno, J. (2019). Optimización multiobjetivo para la selección de carteras a la luz de la teoría de la credibilidad. Una aplicación en el mercado integrado latinoamericano. Editorial Universidad Pontificia Bolivariana.Gupta, P., Inuiguchi, M., & Mehlawat, M. K. (2011). A hybrid approach for constructing suitable and optimal portfolios. Expert Systems with Applications, 38(5), 5620-5632. doi:10.1016/j.eswa.2010.10.073Gupta, P., Inuiguchi, M., Mehlawat, M. K., & Mittal, G. (2013). Multiobjective credibilistic portfolio selection model with fuzzy chance-constraints. Information Sciences, 229, 1-17. doi:10.1016/j.ins.2012.12.011Gupta, P., Mehlawat, M. K., Inuiguchi, M., & Chandra, S. (2014). Portfolio Optimization Using Credibility Theory. Studies in Fuzziness and Soft Computing, 127-160. doi:10.1007/978-3-642-54652-5_5Gupta, P., Mehlawat, M. K., Inuiguchi, M., & Chandra, S. (2014). Portfolio Optimization with Interval Coefficients. Studies in Fuzziness and Soft Computing, 33-59. doi:10.1007/978-3-642-54652-5_2Gupta, P., Mehlawat, M. K., Kumar, A., Yadav, S., & Aggarwal, A. (2020). A Credibilistic Fuzzy DEA Approach for Portfolio Efficiency Evaluation and Rebalancing Toward Benchmark Portfolios Using Positive and Negative Returns. International Journal of Fuzzy Systems, 22(3), 824-843. doi:10.1007/s40815-020-00801-4Gupta, P., Mehlawat, M. K., & Saxena, A. (2010). A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality. Information Sciences, 180(11), 2264-2285. doi:10.1016/j.ins.2010.02.007Gupta, P., Mehlawat, M. K., Yadav, S., & Kumar, A. (2020). Intuitionistic fuzzy optimistic and pessimistic multi-period portfolio optimization models. Soft Computing, 24(16), 11931-11956. doi:10.1007/s00500-019-04639-3Gupta, P., Mittal, G., & Mehlawat, M. K. (2013). Expected value multiobjective portfolio rebalancing model with fuzzy parameters. Insurance: Mathematics and Economics, 52(2), 190-203. doi:10.1016/j.insmatheco.2012.12.002Heidari-Fathian, H., & Davari-Ardakani, H. (2019). Bi-objective optimization of a project selection and adjustment problem under risk controls. Journal of Modelling in Management, 15(1), 89-111. doi:10.1108/jm2-07-2018-0106Hilkevics, S., & Semakina, V. (2019). The classification and comparison of business ratios analysis methods. Insights into Regional Development, 1(1), 48-57. doi:10.9770/ird.2019.1.1(4)Huang, X. (2006). Fuzzy chance-constrained portfolio selection. Applied Mathematics and Computation, 177(2), 500-507. doi:10.1016/j.amc.2005.11.027Huang, X. (2008). Mean-semivariance models for fuzzy portfolio selection. Journal of Computational and Applied Mathematics, 217(1), 1-8. doi:10.1016/j.cam.2007.06.009Huang, X. (2009). A review of credibilistic portfolio selection. Fuzzy Optimization and Decision Making, 8(3), 263-281. doi:10.1007/s10700-009-9064-3Huang, X. (2010). Portfolio Analysis. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-642-11214-0Huang, X. (2017). A review of uncertain portfolio selection. Journal of Intelligent & Fuzzy Systems, 32(6), 4453-4465. doi:10.3233/jifs-169211Huang, X., & Di, H. (2016). Uncertain portfolio selection with background risk. Applied Mathematics and Computation, 276, 284-296. doi:10.1016/j.amc.2015.12.018Huang, X., & Wang, X. (2019). International portfolio optimization based on uncertainty theory. Optimization, 70(2), 225-249. doi:10.1080/02331934.2019.1705821Huang, X., & Yang, T. (2020). How does background risk affect portfolio choice: An analysis based on uncertain mean-variance model with background risk. Journal of Banking & Finance, 111, 105726. doi:10.1016/j.jbankfin.2019.105726Jalota, H., Thakur, M., & Mittal, G. (2017). Modelling and constructing membership function for uncertain portfolio parameters: A credibilistic framework. Expert Systems with Applications, 71, 40-56. doi:10.1016/j.eswa.2016.11.014Jalota, H., Thakur, M., & Mittal, G. (2017). A credibilistic decision support system for portfolio optimization. Applied Soft Computing, 59, 512-528. doi:10.1016/j.asoc.2017.05.054Kaplan, P. D., & Alldredge, R. H. (1997). Semivariance in Risk-Based Index Construction. The Journal of Investing, 6(2), 82-87. doi:10.3905/joi.1997.408419Konno, H., & Yamazaki, H. (1991). Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market. Management Science, 37(5), 519-531. doi:10.1287/mnsc.37.5.519Li, B., Zhu, Y., Sun, Y., Aw, G., & Teo, K. L. (2018). Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint. Applied Mathematical Modelling, 56, 539-550. doi:10.1016/j.apm.2017.12.016Li, H.-Q., & Yi, Z.-H. (2019). Portfolio selection with coherent Investor’s expectations under uncertainty. Expert Systems with Applications, 133, 49-58. doi:10.1016/j.eswa.2019.05.008Li, X., & Qin, Z. (2014). Interval portfolio selection models within the framework of uncertainty theory. Economic Modelling, 41, 338-344. doi:10.1016/j.econmod.2014.05.036Liagkouras, K., & Metaxiotis, K. (2015). Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics. International Journal of Information Technology & Decision Making, 14(03), 535-564. doi:10.1142/s0219622015300013Liu, B. (2004). Uncertainty Theory. Studies in Fuzziness and Soft Computing. doi:10.1007/978-3-540-39987-2Baoding Liu, & Yian-Kui Liu. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10(4), 445-450. doi:10.1109/tfuzz.2002.800692Liu, N., Chen, Y., & Liu, Y. (2018). Optimizing portfolio selection problems under credibilistic CVaR criterion. Journal of Intelligent & Fuzzy Systems, 34(1), 335-347. doi:10.3233/jifs-171298Liu, Y.-J., & Zhang, W.-G. (2018). Multiperiod Fuzzy Portfolio Selection Optimization Model Based on Possibility Theory. International Journal of Information Technology & Decision Making, 17(03), 941-968. doi:10.1142/s0219622018500190Mansour, N., Cherif, M. S., & Abdelfattah, W. (2019). Multi-objective imprecise programming for financial portfolio selection with fuzzy returns. Expert Systems with Applications, 138, 112810. doi:10.1016/j.eswa.2019.07.027Markowitz, H. (1952). PORTFOLIO SELECTION*. The Journal of Finance, 7(1), 77-91. doi:10.1111/j.1540-6261.1952.tb01525.xMarkowitz, H., Todd, P., Xu, G., & Yamane, Y. (1993). Computation of mean-semivariance efficient sets by the Critical Line Algorithm. Annals of Operations Research, 45(1), 307-317. doi:10.1007/bf02282055Martin, R. D., Rachev, S. (Zari), & Siboulet, F. (2003). Phi-alpha optimal portfolios and extreme risk management. Wilmott, 2003(6), 70-83. doi:10.1002/wilm.42820030619Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345, 9-26. doi:10.1016/j.ins.2016.01.042Mehlawat, M. K., Gupta, P., Kumar, A., Yadav, S., & Aggarwal, A. (2020). Multiobjective Fuzzy Portfolio Performance Evaluation Using Data Envelopment Analysis Under Credibilistic Framework. IEEE Transactions on Fuzzy Systems, 28(11), 2726-2737. doi:10.1109/tfuzz.2020.2969406Mehralizade, R., Amini, M., Sadeghpour Gildeh, B., & Ahmadzade, H. (2020). Uncertain random portfolio selection based on risk curve. Soft Computing, 24(17), 13331-13345. doi:10.1007/s00500-020-04751-9Moeini, M. (2019). Solving the index tracking problem: a continuous optimization approach. Central European Journal of Operations Research. doi:10.1007/s10100-019-00633-0Narkunienė, J., & Ulbinaitė, A. (2018). Comparative analysis of company performance evaluation methods. Entrepreneurship and Sustainability Issues, 6(1), 125-138. doi:10.9770/jesi.2018.6.1(10)Palanikumar, K., Latha, B., Senthilkumar, V. S., & Karthikeyan, R. (2009). Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II). Metals and Materials International, 15(2), 249-258. doi:10.1007/s12540-009-0249-7Pflug, G. C. (2000). Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk. Probabilistic Constrained Optimization, 272-281. doi:10.1007/978-1-4757-3150-7_15Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. The Journal of Risk, 2(3), 21-41. doi:10.21314/jor.2000.038Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443-1471. doi:10.1016/s0378-4266(02)00271-6Rubio, A., Bermúdez, J. D., & Vercher, E. (2016). Forecasting portfolio returns using weighted fuzzy time series methods. International Journal of Approximate Reasoning, 75, 1-12. doi:10.1016/j.ijar.2016.03.007Saborido, R., Ruiz, A. B., Bermúdez, J. D., Vercher, E., & Luque, M. (2016). Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection. Applied Soft Computing, 39, 48-63. doi:10.1016/j.asoc.2015.11.005Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business, 39(S1), 119. doi:10.1086/294846Sharpe, W. F. (1994). The Sharpe Ratio. The Journal of Portfolio Management, 21(1), 49-58. doi:10.3905/jpm.1994.409501Sortino, F. A., & Price, L. N. (1994). Performance Measurement in a Downside Risk Framework. The Journal of Investing, 3(3), 59-64. doi:10.3905/joi.3.3.59Srinivas, N., & Deb, K. (1994). Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3), 221-248. doi:10.1162/evco.1994.2.3.221Vercher, E., & Bermúdez, J. D. (2012). Fuzzy Portfolio Selection Models: A Numerical Study. Financial Decision Making Using Computational Intelligence, 253-280. doi:10.1007/978-1-4614-3773-4_10Vercher, E., & Bermudez, J. D. (2013). A Possibilistic Mean-Downside Risk-Skewness Model for Efficient Portfolio Selection. IEEE Transactions on Fuzzy Systems, 21(3), 585-595. doi:10.1109/tfuzz.2012.2227487Vercher, E., & Bermúdez, J. D. (2015). Portfolio optimization using a credibility mean-absolute semi-deviation model. Expert Systems with Applications, 42(20), 7121-7131. doi:10.1016/j.eswa.2015.05.020Vercher, E., Bermúdez, J. D., & Segura, J. V. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158(7), 769-782. doi:10.1016/j.fss.2006.10.026Wang, S., & Zhu, S. (2002). Fuzzy Optimization and Decision Making, 1(4), 361-377. doi:10.1023/a:1020907229361Yue, W., & Wang, Y. (2017). A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios. Physica A: Statistical Mechanics and its Applications, 465, 124-140. doi:10.1016/j.physa.2016.08.009Yue, W., Wang, Y., & Xuan, H. (2018). Fuzzy multi-objective portfolio model based on semi-variance–semi-absolute deviation risk measures. Soft Computing, 23(17), 8159-8179. doi:10.1007/s00500-018-3452-yZadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZhai, J., & Bai, M. (2018). Mean-risk model for uncertain portfolio selection with background risk. Journal of Computational and Applied Mathematics, 330, 59-69. doi:10.1016/j.cam.2017.07.038Zhao, Z., Wang, H., Yang, X., & Xu, F. (2020). CVaR-cardinality enhanced indexation optimization with tunable short-selling constraints. Applied Economics Letters, 28(3), 201-207. doi:10.1080/13504851.2020.174015

What is the cost of maximizing ESG performance in the portfolio selection strategy? The case of The Dow Jones Index average stocks

[EN] Portfolio selection is one of the main financial topics. The original portfolio selection problem dealt with the trade-off between return and risk, measured as the mean returns and the variance, respectively. For investors more variables other than return and risk are considered to select the stocks to be included in the portfolio. Nowadays, many investors include corporate social responsibility as one eligibility criterion. Additionally, other return and risk measures are being employed. All of this, together with further constraints such as portfolio cardinality, which mirror real-world demands by investors, have made the multicriteria portfolio selection problem to be NP-hard. To solve this problem, heuristics such as the non-dominated sorting genetic algorithm II have been developed. The aim of this paper is to analyse the trade-off between return, risk and corporate social responsibility. To this end, we construct pareto efficient portfolios using a fuzzy multicriteria portfolio selection model with real-world constraints. The model is applied on a set of 28 stocks which are constituents of the Dow Jones Industrial Average stock index. The analysis shows that portfolios scoring higher in corporate social responsibility obtain lower returns. As of the risk, the riskier portfolios are those with extreme (high or low) corporate social responsibility scores. Finally, applying the proposed portfolio selection methodology, it is possible to build investment portfolios that dominate the benchmark. That is, socially responsible portfolios, measured by ESG scores, must not necessarily be penalized in terms of return or risk.García García, F.; Gankova-Ivanova, T.; González-Bueno, J.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2022). What is the cost of maximizing ESG performance in the portfolio selection strategy? The case of The Dow Jones Index average stocks. Enterpreneurship and Sustainability Issues. 9(4):178-192. https://doi.org/10.9770/jesi.2022.9.3(9)1781929

Quality of Life Questionnaire: Psychometric Properties and Relationships to Healthy Behavioural Patterns

The main purpose of this study is to evaluate the quality of life in a healthy population from Universitat Oberta de Catalunya (UOC) using the 'Quality of life Questionnaire' (QoLQ), which was developed in our cultural context and to examine its psychometric properties. A secondary goal is to explore the relationship between quality of life perception and healthy behavior profiles. Data were obtained from 264 participants with access to the online Campus who answered a web version of the questionnaire (QoLQ). Our results indicate that the psychometric properties of the instrument are satisfactory and the original factorial structure is confirmed: Social Support, General Satisfaction, Physical/Psychological well-being and Absence of work overload/Free time. The Cronbach's alpha coefficients for internal consistency ranged from 0.82 to 0.89 for the subscales and was 0.93 for the total items. A new variable called healthy behavioral pattern was generated from the answers of a chronogram of daily activities. The statistical significant differences (95% CI and t-values) across more healthy and less healthy behavior profiles reveal that the former perceive a higher quality of life