Comparativa de los models clásicos de series temporales con la red neuronal recurrente LSTM: Una aplicación a las acciones del S&P 500

[EN] In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of di erent investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.[ES] En la literatura financiera existe un gran interés por la predicción de precios bursátiles que es necesario para la creación de diferentes estrategias de inversion, tanto especulativas como de cobertura. La aplicación de las redes neuronales ha supuesto un cambio en la creación de modelos de predicción. En este trabajo se analiza la capacidad que tienen las redes neuronales recurrentes, en concreto la long shortterm recurrent neural network (LSTM) frente a modelos de series temporales clásicos como el Exponential Smooth Time Series (ETS) y el modelo Arima (ARIMA). Para ello se ha estimado dichos modelos para 284 acciones pertenecientes al índice bursátil S&P 500, comparando el MAE obtenido de sus predicciones, con el modelo LSTM. Los resultados obtenidos confirman una reducción importante de los errores de predicción. Estos resultados son coincidentes con otros estudios similares aplicados a acciones de otros índices bursátiles así como a otros activos financieros como los tipos de cambio.Oliver-Muncharaz, J. (2020). Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks. Finance, Markets and Valuation. 6(2):137-148. https://doi.org/10.46503/ZVBS2781S1371486

Red neuronal fuzzy híbrida versus red neuronal backpropagation: Aplicación a la predicción del índice bursátil Ibex-35

[ES] El uso de las redes neuronales se ha extendido en todas las áreas de conocimiento por los buenos resultados que se están obteniendo en la resolución de los diferentes problemas planteados. La predicción sobre los precios en general, y los precios bursátiles en particular, representa uno de los principales objetivos del uso de las redes neuronales en finanzas. En este trabajo se presenta el análisis de la eficiencia de la hybrid fuzzy neural network frente a una red neuronal de tipo backpropagation en la predicción del precio del índice bursátil Español (IBEX-35). El trabajo se divide en dos partes. En la primera se expone las principales características de las redes neuronales como la hybrid fuzzy y la Backpropagation, sus estructuras y sus reglas de aprendizaje. En la segunda parte se analiza la predicción del índice bursátil IBEX-35 con estas redes midiendo la eficiencia de ambas en función de los errores de predicción cometidos. Para ello se han construido ambas redes con los mismos inputs y para el mismo periodo muestral. Los resultados obtenidos sugieren que la Hybrid fuzzy neuronal network es mucho más eficiente que la, tan extendida, red neuronal backpropagation para la muestra analizada.[EN] The use of neural networks has been extended in all areas of knowledge due to the good results being obtained in the resolution of the different problems posed. The prediction of prices in general, and stock market prices in particular, represents one of the main objectives of the use of neural networks in finance. This paper presents the analysis of the efficiency of the hybrid fuzzy neural network against a backpropagation type neural network in the price prediction of the Spanish stock exchange index (IBEX-35). The paper is divided into two parts. In the first part, the main characteristics of neural networks such as hybrid fuzzy and backpropagation, their structures and learning rules are presented. In the second part, the prediction of the IBEX-35 stock exchange index with these networks is analyzed, measuring the efficiency of both as a function of the prediction errors committed. For this purpose, both networks have been constructed with the same inputs and for the same sample period. The results obtained suggest that the Hybrid fuzzy neuronal network is much more efficient than the widespread backpropagation neuronal network for the sample analysed.Oliver-Muncharaz, J. (2020). Hybrid fuzzy neural network versus backpropagation neural network: An application to predict the Ibex-35 index stock. Finance, Markets and Valuation. 6(1):85-98. https://doi.org/10.46503/ALEP9985S85986

Analysis and classification of technical analysis indicators by support vector machines

[EN] The search for models which can accurately forecast the market trend has developed over the past decades. Technical indicators and oscillators are the most usually employed inputs in the prediction models. These inputs basically rely on prices and the evolution of the index itself, which may cause some problems like multicolinearity and autocorrelation, in the case of linear models, or overoptimization and noise, in the case of neural networks. This paper proposes filtering the inputs to be employed in the models. To this end, their impact on the forecast will be analysed. A support vector machine will be used to this end, in order to characterize both inputs (indicators and oscillators) and output (market trend). Doing this, it can be assessed whether the relationship between the different inputs and the market trend offers relevant information regarding the contribution of the inputs in the prediction process and whether this contribution remains constant over time. Those inputs will be selected, which obtain more stable forecasts in order to obtain more consistent predictions.[ES] La búsqueda de modelos para la predicción de la tendencia de los índices bursátiles se ha desarrollado en las últimas décadas. Los indicadores y osciladores técnicos son los inputs más utilizados en todos los modelos. Éstos se basan fundamentalmente en los precios y dirección del propio índice. Esto puede provocar ciertos problemas en las estimaciones y procesos de aprendizajes de los diferentes modelos, como multicolinealidad y autocorrelación para el caso de modelos lineales y problemas de sobreoptimización y ruido en otros casos como en las redes neuronales. Se plantea filtrar los diferentes indicadores y osciladores técnicos a utilizar en los diferentes modelos. Para ellos, se va a analizar el impacto que tienen éstos en el proceso de predicción de la tendencia de un índice bursátil. El modelo utilizado es la support vector machine que permite encontrar las características tanto de los inputs (indicadores y osciladores) como del output (la tendencia del índice). Este mapeo de la relación de los indicadores y la tendencia ofrece información relevante sobre si dicha contribución a su predicción es estable en el tiempo. Por tanto, se seleccionarán aquellos inputs cuyas características estabilicen las predicciones en los modelos. Así pues, se deben descartar aquellos indicadores irregulares, aunque puntualmente puedan alcanzar ratios de acierto algo más elevadas que los más estables. Este proceso provocará obtener predicciones de la tendencia más consistentes.Oliver-Muncharaz, J. (2018). Análisis y clasificación de indicadores técnicos mediante support vector machine. Finance, Markets and Valuation. 4(1):81-93. http://hdl.handle.net/10251/122883S81934

Tendencias líderes de investigación sobre estrategias de trading

[EN] Trading strategies have attracted the attention of academic researchers and practitioners for a long time, but most specially in recent years due to the explosion of high-quality databases and computation capacity. Numerous studies are devoted to the analysis and proposal of trading strategies which cover aspects such as trend prediction, variables selection, technical analysis, pattern recognition etc. and apply many di erent methodologies. This paper conducts a meta-literature review which covers 1187 research articles from 1984 to 2020. The aim of this paper is to show the increasing importance of the topic and present a systematic study of the leading research areas, countries, institutions and authors contributing to this field. Moreover, a network analysis to identify the main research streams and future research opportunities is conducted.[ES] La creación de estrategias de inversión siempre ha atraído la atención de los académicos y de los inversores profesionales, pero, indudablemente, esta popularidad ha aumentado en los últimos años, con la aparición de bases de datos más completas y mayor potencia de cálculo de las computadoras. Son numerosos los estudios que analizan y proponen estrategias de inversión y que tratan aspectos como la predicción de la tendencia, la selección de variables, el análisis técnico, el reconocimiento de patrones etc. aplicando diferentes metodologías. En este trabajo se realiza un estudio bibliográfico que abarca 1187 artículos de investigación desde 1984 hasta 2020. El objetivo es mostrar la creciente importancia de este campo de investigación y presentar un análisis sistemático de los países, instituciones y autores que más están contribuyendo al avance del conocimiento. Además, se realiza un análisis de redes para identificar las principales áreas de investigación y las tendencias futuras.Oliver-Muncharaz, J.; García García, F. (2020). Leading research trends on trading strategies. Finance, Markets and Valuation. 6(2):27-54. https://doi.org/10.46503/LHTP1113S27546

A Multicriteria Goal Programming Model for Ranking Universities

[EN] This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.García García, F.; Guijarro, F.; Oliver-Muncharaz, J. (2021). A Multicriteria Goal Programming Model for Ranking Universities. Mathematics. 9(5):1-17. https://doi.org/10.3390/math90504591179

El uso del software estadístico R en la asignatura de finanzas cuantitativas: desarrollo de habilidades y competencias

[ES] Los cambios en la orientación de la docencia como consecuencia de la necesaria adaptación al Espacio Europeo de Educación Superior incluyen la adopción de un nuevo paradigma, modificando la orientación docente. Así, se ha pasado de una enseñanza por contenidos a otra orientada a los resultados de aprendizaje y el desarrollo de competencias. La introducción del software estadístico R en las clases de laboratorio de la asignatura de Finanzas Cuantitativas supone un cambio en ese sentido. Entre otras ventajas, refuerza las competencias relacionadas con el aprendizaje de los contenidos del curso. Además, esta asignatura sirve ahora de punto de control de dos competencias transversales: uso crítico de la información y empleo de instrumentos específicos. En las clase prácticas de laboratorio, se emplea el software R para resolver problemas reales, evitando plantear simples problemas teóricos.[EN] The change in the teaching orientation due to the adaptation to the new European Higher Education Area (EHEA), involved a remarking new education paradigm, moving from a learning content to an orientation based on learning results and development of competences. The introduction of the R statistical software in the Quantitative Finance subject lab-sessions implied a change in this vein. Among others, it strengthened the competences related with the learning of the contents of the subject. Moreover, among the different transversal competences (TC) and skills (a total of nine TCs), the following competences were chosen as checkpoints: Critical synthesis of information and the use of specific tools. Under the methodological vision of action- research and the critical observation of teaching in the subject we use the lab-cases and sessions in order to propose actual cases, therefore, we avoid the use of simple practical exercises. By means of the R statistical software the main goal was to reach the objectives of the proposed three lab-sessions.Cervelló Royo, RE.; Guijarro, F.; Oliver-Muncharaz, J. (2017). The use of the R statistical software in the quantitative finance subject: development of skills and competences. Finance, Markets and Valuation. 3(2):109-116. http://hdl.handle.net/10251/102303S1091163

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

Ranking the Performance of Universities: The Role of Sustainability

[EN] University rankings assess the performance of universities in various fields and aggregate that performance into a single value. In this way, the aggregate performance of universities can be easily compared. The importance of rankings is evident, as they often guide the policy of Higher Education Institutions. The most prestigious multi-criteria rankings use indicators related to teaching and research. However, many stakeholders are now demanding a greater commitment to sustainable development from universities, and it is therefore necessary to include sustainability criteria in university rankings. The development of multi-criteria rankings is subject to numerous criticisms, including the subjectivity of the decision makers when assigning weights to the criteria. In this paper we propose a methodology based on goal programming that allows objective, transparent and reproducible weighting of the criteria. Moreover, it avoids the problems associated with the existence of correlated criteria. The methodology is applied to a sample of 718 universities, using 11 criteria obtained from two prestigious university rankings covering sustainability, teaching and research. A sensitivity analysis is carried out to assess the robustness of the results obtained. This analysis shows how the weights of the criteria and the universities' rank change depending on the lambda parameter of the goal programming model, which is the only parameter set by the decision maker.Burmann, C.; García García, F.; Guijarro, F.; Oliver-Muncharaz, J. (2021). Ranking the Performance of Universities: The Role of Sustainability. Sustainability. 13(23):1-17. https://doi.org/10.3390/su132313286S117132

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

Defining socially responsible companies according to retail investors' preferences

[EN] The impressive growth of the funds managed following socially responsible investment strategies is a phenomenon that has been analysed from different perspectives. One of the main factors determining such investment strategies, maybe the most important one, is the selection of socially responsable companies, that is, the differentiation between socially responsible and irresponsible companies. Generally, the selection process is performed applying negative screening or positive screening strategies. Negative screening considers irresponsible companies those involved in the production of weapons or alcoholic beverages, following religious criteria. The positive screening approach is much more complex and less transparent. Both methodologies have been critizied as they do not prevent companies performing a clearly irresponsible behaviour to be included in the socially responsable portfolio. Moreover, it is important to stress that the opinion of retail investors is not considered when defining the concept of "socially responsible company", that is, the opinion of the potential clients of the socially responsible financial products. In this paper we are interested in the opinion of these potential clients regarding negative screening criteria, because we exclude the possibility of retail investors applying complex positive screening approaches. Our results show that compliance with the legislation is a main criterion for potential retail investors. This is an important outcome, as legal compliance is actually not a necessary requisite and non-complying companies are usually included in socially responsible financial products. Regarding negative screening based on the activity sector of the companies, results are more controversial.Arribas, I.; Espinós-Vañó, MD.; García García, F.; Oliver-Muncharaz, J. (2019). Defining socially responsible companies according to retail investors' preferences. Enterpreneurship and Sustainability Issues. 7(2):1641-1653. https://doi.org/10.9770/jesi.2019.7.2(59)S164116537