Intangible assets, influence on the return on equity - evidence from S&P 100 Index

In the 21st century, the most valuable strategic resources for business enterprises will no longer be physical assets such as land and machines, as was the case at the beginning of the 20th century, but rather intangible assets (IA) such as knowledge, patents, and intellectual property rights. This shift responds to a transition towards a knowledge-based economy (Ipate & Pârvu, 2016; Park, 2015). Nowadays, as companies are acquiring and developing more non-physical assets, the question arises: what is the effect of IA in companies’ performance? Therefore, this study aims to analyze the effect of IA (exclusively those that are recognized and shown in the balance sheet) on the return on equity (ROE). In order to analyze the influence of IA on ROE, the study used components of the Standard and Poor 100 Index (S&P100). The S&P100 index comprises 101 companies across multiple industry groups; however, due to the research restrictions, only 68 companies were selected as the study’s sample. The Three-Step DuPont Model, comprising of three factors - net profit margin, asset turnover and equity multiplier - was used to analyze the effect of IA on ROE. For the research purposes, the Dupont model was modified to isolate IA. The analysis was done using book and market values for the dependent variable. The research results were obtained using the Ordinary Least Square (OLS) method. According to our findings, with ROE at book value the influence of IA on ROE is 34% excluding goodwill and 31% including goodwill. At market value, the influence of IA on ROE is almost 20%. Additionally, the results indicated a significant gap between the financial Information and the market assessment of shareholders equity.No século XXI, os recursos estratégicos mais valiosos para as empresas não serão mais os ativos físicos, como a terra e as máquinas, como ocorreu no início do século XX (Bratianu, 2017), mas sim os ativos intangíveis (AI), como o conhecimento, patentes e direitos de propriedade intelectual. Este comportamento corresponde a uma transição para uma economia baseada no conhecimento (Ipate & Pârvu, 2016; Park, 2015). Atualmente, as empresas vêm adquirindo e desenvolvendo cada vez mais ativos intangíveis, o que faz surgir a questão de qual é o efeito destes ativos no desempenho das empresas. Este estudo considera os AI relevantes para o desempenho das empresas, no entanto, tem como objetivo analisar o efeito dos AI reconhecidos contabilisticamente nos balanços das empresas sobre a rentabilidade dos capitais próprios (ROE). Para analisar a influência dos AI no ROE, o estudo utilizou um conjunto de empresas pertencentes ao índice “Standard &Poor 100” Index (S&P100). O índice S&P100 é composto por 101 empresas de vários sectores de atividade; no entanto, devido aos requisitos do estudo, apenas 68 empresas foram selecionadas para comporem a amostra. O modelo de três etapas DuPont foi utilizado para analisar o efeito dos IA no ROE. O modelo inclui três fatores: a rentabilidade das vendas, a rotação dos ativos face às vendas (vendas/ativo total) e a relação do ativo total face aos capitais próprios (ativo total/capital próprio). O referido modelo foi modificado para introduzir como fator os AI e foram utilizados valores contabilísticos e valores de mercado na variável dependente. O método dos mínimos quadrados foi utilizado na análise. Os resultados obtidos demostram uma influência de 34% dos AI no ROE, utilizando valores contabilísticos, das empresas quando o goodwill esta a ser excluído e dum 31% quando está o goodwill incluído, , e uma influência de 20% dos AI no ROE, utilizando valores de mercado e excluindo o goodwill. Por outro lado, os resultados indicam um desfasamento significativo em termos contabilísticos e de mercado na avaliação no âmbito do ROE.En el siglo XXI, los recursos estratégicos más valiosos para las empresas comerciales ya no serán los activos físicos como la tierra y las máquinas, como fue el caso a principios del siglo XX, más bien activos intangibles (AI) como el conocimiento, patentes y derechos de propiedad intelectual, este comportamiento responde a la transición hacia una economía basada en el conocimiento (Ipate & Pârvu, 2016; Park, 2015). Hoy en día, las empresas están adquiriendo y desarrollando más activos no físicos, entonces surge una pregunta: ¿cuál es el efecto de los AI en el desempeño de las empresas? Por lo tanto, este estudio tiene como objetivo analizar el efecto de los AI (exclusivamente aquellos que se reconocen y muestran en el balance general) en el rendimiento del capital (ROE). Con el fin de analizar la influencia de los AI en el ROE, el estudio utilizó empresas que componen en el índice Standard and Poor 100 (S & P100). El índice S&P 100 comprende 101 compañías en múltiples grupos industriales; sin embargo, debido a los requisitos del estudio solo fueron seleccionadas 68 empresas como muestra para este estudio. El modelo de tres pasos de DuPont se usó para analizar el efecto de AI en el ROE. El modelo comprende tres factores: el margen de beneficio neto, la rotación de activos y el multiplicador del patrimonio. Para fines de esta investigación el multiplicador del patrimonio fue modificado para aislar los AI. El análisis fue realizado usando el valor en libros y el valor de mercado para las variables dependientes. Los resultados de esta investigación fueron obtenidos usando el método del Mínimo Cuadrado Ordinario (MCO). En concordancia con nuestros hallazgos, la influencia de los AI en el ROE es del 34% excluyendo el goodwill (fondo de comercio) y 31% incluyendo el goodwill (fondo de comercio), cuando el ROE utiliza el valor en libros; cuando el ROE utiliza el valor del mercado la influencia de los AI es cerca del 20%. Adicionalmente, los resultados también mostraron una brecha significativa entre la información financiera y la valuación de mercado en el capital

To What Factors do University Students Attribute Their Academic Success?

This study explores the attributions to which undergraduate university students ascribe academic achievement. Attribution theory was used as a means to understand scholastic success-failure. The questions that guided the study were the following: What are the causal attributions that predominate in students' academic achievement? Is there a difference between male and female students? Is there a difference if average grades and the number of failed subjects, factored as benchmarks of academic achievement, are considered? Do the measured attributions have any weight when predicting students’ grades? A Likert scale measuring eight different attributions of academic achievement was applied to 165 students. The results showed that the most important attribution for academic achievement was intelligence. Sex-related differences were found in two attributes: calm and effort. In general, students with four failed subjects were those with the lowest averages measured in attributions. The variables that predicted good grades for male students were effort and good teachers, for female students, a liking for teachers, luck, and attention

Discurso leido en la solemne apertura del curso de 1883 á 1884 en el Instituto de Segovia

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2009-201

Universality in quantum chaos and the one parameter scaling theory

We adapt the one parameter scaling theory (OPT) to the context of quantum chaos. As a result we propose a more precise characterization of the universality classes associated to Wigner-Dyson and Poisson statistics which takes into account Anderson localization effects. Based also on the OPT we predict a new universality class in quantum chaos related to the metal-insulator transition and provide several examples. In low dimensions it is characterized by classical superdiffusion or a fractal spectrum, in higher dimensions it can also have a purely quantum origin as in the case of disordered systems. Our findings open the possibility of studying the metal insulator transition experimentally in a much broader type of systems.Comment: 4 pages, 2 figures, acknowledgment added, typos correcte

Usability and Feasibility Study of a Remote Cognitive Behavioral Therapy System with Long-Term Unemployed Women

We present the results of the use of a cognitive behavioral therapeutic intervention tool to improve the mental, physical, and social health of a group of long-term unemployed women in Spain. Method: We sent automated text messages (SMS) to the mobile phones of long-term unemployed women selected at random from public social services. During a 28-day intervention period, women received four daily automated text messages on her mobile phone on a predetermined hourly schedule. We measured depression symptoms at the start and end of the intervention and we analyzed qualitative data to determine the acceptability of a remote SMS program. Results: Depression symptoms using the Personal Health Questionnaire-9 (PHQ-9), went from an average of 13.8 at baseline to 4.9 at the end of 28 days (p = 0.89). One hundred percent of the women reported that they liked receiving the text messages and most found them helpful