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

Algunos resultados recientes en didáctica pitagórica

Se muestran algunos resultados obtenidos con niños entre 9 y 12 años que pertenecen al Proyecto Semicírculo de la Universidad Sergio Arboleda y que estuvieron en el primer semestre de 2007 en el Programa Pre talentos. Se toma como figura principal el cuadrado y se definen algunas reglas para construir figuras a partir de ´este, en donde los niños (as) generan nuevas figuras y se empieza a estudiar algunas características de estas figuras como lo son, las relaciones entre los nodos, los lados y las regiones que hay en cada una de ellas, con el fin de inducirlos hacia el Teorema de Euler. Durante este proceso se generan nuevos resultados, entre ellos el Teorema de Cristian, el cual se muestra a continuación en el desarrollo del presente trabajo

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

Variación de la captura por unidad de esfuerzo (CPUE) de la pesca artesanal y su sustentabilidad en relación con las variables ambientales en el Pacífico colombiano

Contextualization: artisanal fishing in the Colombian Pacific is positioned as one of the main sources of income for the communities settled on the coasts, therefore, there is strong pressure on fishing resources, with non-selective fishing gear such as the trammel netting and the hand linehook, capturing individuals that have not presented reproductive events and belonging to incidental catches and discards. Knowledge gap:  in the Colombian Pacific the spatio temporal relationships have not been explored between the target catch and discard according to the environmental variables. Purpose: Evaluate the spatio temporal dynamics of catch per unit of effort and the sustainability of artisanal fishing associated with environmental variables in Tumaco Bay, Colombian Pacific. Methodology: four samplings were conducted throughout the year 2020 and 2021, two in the dry season and two in the rainy season with the trammel and hand hook line fishing gears, in the internal and external zone of the Tumaco bay. Samples of physical-chemical variables were carried out. Results and conclusions: Within the target catch, the families Ariidae and Sciaenidae were the most representative, Pristigasteridae and Gerreidae in the bycatch and Engraulidae, Cynoglosidae in the discard. In hand hook line, 94.60% was target catch, 3.90% bycatch and 1.48% belonged to discard, while in trammel netting, 49.07% was discard, 41.78% was target catch and 9.15% was bycatch. The trammel net caught smaller individuals (17.49 ± 4.26 cm), which may indicate that it is a little selective gear as it does not respect the minimum catch sizes. The total catch per unit of effort was higher with the hand hook fishing gear (8.84 ± 5.42 kg h-1). The variables salinity, total dissolved solids and transparency presented a negative correlation with the total catch per unit of effort in the trammel net, while with the hook they presented a positive association. It is recommended to regulate the use of trammel nets in the internal area and in the rainy season due to with this fishing gear captures juvenile commercial species that are discarded.Contextualización: la pesca artesanal en el Pacífico colombiano se posiciona como una de las principales fuentes de ingresos de las comunidades asentadas en las costas. Existe una fuerte presión en los recursos pesqueros, con artes de pesca poco selectivos como el trasmallo y la línea de anzuelo de mano, capturando individuos que no han presentado eventos reproductivos y pertenecientes a capturas incidentales y de descarte. Vacío de investigación: en el Pacífico colombiano no se ha explorado las relaciones espacio-temporales entre las capturas objetivo y descarte según las variables ambientales. Propósito del estudio: Evaluar la dinámica espacio-temporal de la captura por unidad de esfuerzo y la sustentabilidad de la pesca artesanal asociada a las variables ambientales en la bahía de Tumaco, Pacífico colombiano. Metodología: se realizaron cuatro muestreos a lo largo del año 2020 y 2021, dos en época seca y dos en época de lluvia con los artes de pesca trasmallo y línea de anzuelo de mano, en las áreas interna y externa, asimismo se realizaron muestras de variables físico-químicas. Resultados y conclusiones: dentro de la captura objetivo, las familias Ariidae y Sciaenidae fueron las más representativas, Pristigasteridae y Gerreidae en la captura incidental y Engraulidae y Cynoglosidae en el descarte. En anzuelo, 94.60% fue captura objetivo, 3.90% incidental y 1.48% perteneció al descarte, mientras que, en trasmallo, 49.07% fue descarte, 41.78% fue captura objetivo y 9.15% fue captura incidental. El trasmallo capturó individuos de menor tamaño (17.49 ± 4.26 cm), lo cual puede indicar que es un arte poco selectivo al no respetar las tallas mínimas de captura. La CPUE total fue mayor con el arte de pesca de anzuelo de mano (8.84 ± 5.42 kg h-1). Las variables salinidad, sólidos totales disueltos y transparencia presentaron una correlación negativa con la captura por unidad de esfuerzo total en el trasmallo, mientras con anzuelo presentaron una asociación positiva. Se recomienda regular el uso de trasmallo en el área interna de la bahía y en época de lluvias por capturar especies comerciales juveniles que se descartan

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

Every Fish Counts: Challenging Length–Weight Relationship Bias in Discards

Bycatch is a significant issue in global fisheries and understanding the length–weight relationships (LWR) of fish species can provide valuable insights for stock assessment and management efforts. In this study, we estimated the LWR of 74 fish species in trawl fleet discards from the Gulf of Cadiz, including 24 species for which LWR data had not been previously reported in this region. LWR was calculated from the formula W = aLb where parameter a is the intercept of the equation, related to body shape, and parameter b is the slope, which indicates the type of growth of the species. A total of 20,007 individuals from 40 families were measured and weighed. The most abundant species were Engraulis encrasicolus, Trachurus trachurus, Serranus hepatus, Sardina pilchardus, Capros aper, and Diplodus bellottii, and the Sparidae family was the most represented with ten species. The parameter b, which represents the type of growth, ranged from 2.1607 to 3.7040. A positive allometric growth trend was observed in 64% of the species. The inclusion of individuals with a low sample size proved useful, particularly for first reports in a new study area. However, caution should be taken when using these data, as the estimates of the length–weight relationship for these species may be less precise. Further studies with larger sample sizes are needed to confirm the results and improve the accuracy of the estimates. Overall, our findings contribute to the understanding of the LWR of fish species in the Gulf of Cadiz, informing future research and management efforts in the region.This research has been carried out within the framework of the ECOFISH project: ecoinnovative strategies for sustainable fishing in the Gulf of Cadiz SPA. This initiative has been supported by the Biodiversity Foundation, the Ministry for Ecological Transition and Demographic Challenge, through the Pleamar Program, co-financed by the European Maritime and Fisheries Fund (EMFF) [grant number: 2019-016/PV/PLEAMAR18/PT; 2020-013/PV/PLEAMAR19/PT; 2020- 055/PV/PLEAMAR20/PT; 2021/PV/PLEAMAR20-21/PT; 2021-060/PV/PLEAMAR21/PT]

Perspectiva de factores de riesgo y factores preventivos, en el desarrollo de la práctica sexual en las estudiantes de 1° año General del Instituto Nacional Joaquín Ernesto Cárdenas de San Miguel durante el año 2017

La sexualidad engloba una serie de condiciones culturales, sociales, anatómicas, fisiológicas, emocionales, afectivas y de conducta, relacionadas con el sexo que caracterizan de manera decisiva al ser humano en todas las fases de su desarrollo. Se presenta un estudio mediante el cual se expone la perspectiva sobre factores de riesgo y factores preventivos en el desarrollo de la práctica sexual en las estudiantes de primer año General del Instituto Nacional Joaquín Ernesto Cárdenas. La metodología fue de carácter cualitativa, de tipo exploratorio-descriptivo. Los procedimientos de muestreo fueron no probabilísticos, de naturaleza consecutiva. Los datos fueron analizados a través de la técnica de Análisis de Contenido. Se discutieron los resultados en función de las temáticas de adolescencia y factores de riesgo y factores preventivo

Desarrollo de un sistema E-Salud Ocupacional mediante Mean Stack Javascript en la Empresa Cemento Chimborazo.

El presente trabajo de investigación contiene el desarrollo de un sistema E-Salud para la empresa Unión Cementera Nacional (UCEM) que se ubica en el cantón Riobamba, provincia de Chimborazo, cuenta con un personal de 187 empleados entre operarios y administrativos; el centro médico cuenta con un médico ocupacional y 2 enfermeras de apoyo que son las personas encargadas de la historia clínica ocupacional. Mediante tecnologías web; Mean Stack JavaScript que es una plataforma conformada por MongoDB, Express.js, Node.js y Angular.js, en conjunto con estándares como el CIE10, que utilizan los médicos ocupacionales, para la gestión de la historia clínica ocupacional. Se utilizó el IDE WebStorm para gestionar lenguajes de programación JavaScript, en conjunto con la Metodología Scrum para desarrollo de software y con el Método Deductivo se plasmó los requerimientos del departamento, estableciendo 7 requerimientos funcionales que se desarrollaron en el lapso de 21 días. La velocidad del proyecto de investigación según Scrum se la realiza mediante tarjetas conocidas como historias de usuario donde se define el tiempo estimado para la actividad y el tiempo real en la que se realizó, cada una de ellas tiene una prueba de aceptación en donde se valida o se refactorizan errores. Al final de la metodología se plasma dicha velocidad en un gráfico conocido como BurnDown Chart, en donde se observa si el proyecto de investigación está acorde a lo planificado, se realizó una prueba de rendimiento, dando como resultado la carga de 206000 datos en 1 hora con 35 min, lo cual es aceptable. Se recomienda continuar con el desarrollo de más módulos al proyecto, como lo son el de morbilidad y ausentismo laboral; y aplicar criterios de seguridad más amplios como tokens OAUTH para los servicios web.This research develops an E-Health system for the Union Cementera Nacional (UCEM) enterprise which is located in Riobamba city, Chimborazo Province, the enterprise has 187 employees among workers and administrators; the health center has an occupational doctor and two nurses who are in charge of the occupational medical record and use the Mean Stack JavaScript web technology which is a platform composed by MongoDB, Express.js, Node.js and Angular.js in a set with standards such as CIE10, which are used by the occupational doctors for the occupational medical record management. The IDE WebStorm was used to process the JavaScript programming language together to the Scrum Methodology to develop the software and with the Deductive Method the department requirements were fulfilled by establishing 7 functional requirements which were develop in 21 days. The research project speed according to Scrum is made through cards known as user records where the estimated time and real time are defined, each card has an acceptance test in order to validate or re-factorize errors. At the end of methodology, the speed is expressed in a graph known as BurnDown Chart. Which shows if the research project presents the planned results, a performance test was applied, and the results were a load of 206000 data in 1 hour and 35 minutes, which is acceptable. It is recommended to continue with elaboration of more modules for the project, such us sickness rate and absenteeism, and apply broader safety criterian as tokens OAUTH for web services

Mean-variance investment strategy applied in emerging financial markets: evidence from the Colombian stock market

In any investment, an analysis of the expected return and the assumed risk constitutes a fundamental step. Investing in financial assets is no exception. Since the portfolio selection theory was proposed by Markowitz in 1952, this methodology has become the benchmark in portfolio management. However, it is not always possible to apply it, especially when investing in emerging financial markets, which are characterised by a scant variety of available stocks and very lowliquidity. In this paper, using the Colombian case, we will examine the challenges found by investors who want to create a portfolio using only stocks listed on a scarcely developed stock market