Is the predictability of emerging and developed stock markets really exploitable?.

A number of recent papers have analyzed the degree of predictability of stock markets. In this paper, we firstly study whether this predictability is really exploitable and secondly, if the economic significance of predictability is higher or lower in the emerging stock markets than in the developed ones. We use a variety of linear and nonlinear – Artificial Neural Networks – models and perform a computationally demanding forecasting experiment to assess the predictability of returns. Since we are interested in comparing the predictability in economic terms we also propose a modification in the nets’ loss function for market trading purposes. In addition, we consider both explicit and implicit trading costs for emerging and developed stock markets. Our conclusions suggest that, in contrast to some previous studies, if we consider total trading costs both the emerging as well as the developed stock returns are clearly nonpredictable. Finally, we find that Artificial Neural Networks do not provide superior performance than the linear models.Finance; Forecasting; Emerging stock markets; Artificial neural networks;

Self-organizing maps could improve the classification of Spanish mutual funds.

In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from this alternative classification also attain better performances in terms of return/risk and include a smaller number of assets.Finance; Mutual funds; Clustering; Self-organizing map (SOM); Investment analysis;

Risk forecasting models and optimal portfolio selection.

This study analyses, from an investor's perspective, the performance of several risk forecasting models in obtaining optimal portfolios. The plausibility of the homoscedastic hypothesis implied in the classical Markowitz model is dicussed and more general models which take into account assymetry and time varying risk are analysed. Specifically, it studies whether ARCH-type based models obtain portfolios whose risk-adjusted returns exceed those of the classical Markowitz model. The same analysis is performed with models based on the Lower Partial Moment (LPM) which take into account the assymetry in the distribution of returns. The results suggest that none of the models achieve a clearly superior average performance. It is also found that models based on semivariance perform as well as those based on the variance, but not better than, even if the evaluation criterion is based on the Reward-to-Semivariance ratio. When attention turns to the analysis of worst case performance, the results are clearly different. Models which employ LPM with a high degree of risk aversion (n>2) as the risk measure are consistently superior to those which employ a symmetric measure, either homoscedastic or heteroscedastic.

Is the predictability of emerging and developed stock markets really exploitable?

A number of recent papers have analyzed the degree of predictability of stock markets. In this paper, we firstly study whether this predictability is really exploitable and secondly, if the economic significance of predictability is higher or lower in the emerging stock markets than in the developed ones. We use a variety of linear and nonlinear – Artificial Neural Networks – models and perform a computationally demanding forecasting experiment to assess the predictability of returns. Since we are interested in comparing the predictability in economic terms we also propose a modification in the nets’ loss function for market trading purposes. In addition, we consider both explicit and implicit trading costs for emerging and developed stock markets. Our conclusions suggest that, in contrast to some previous studies, if we consider total trading costs both the emerging as well as the developed stock returns are clearly nonpredictable. Finally, we find that Artificial Neural Networks do not provide superior performance than the linear models.Publicad

Self-organizing maps could improve the classification of Spanish mutual funds

In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from this alternative classification also attain better performances in terms of return/risk and include a smaller number of assets.Publicad

Determinantes de la revelación de información sobre derivados financieros en el mercado español

En el presente trabajo estudiamos como las diferentes propuestas normativas que se han ido desarrollando en los últimos años en torno a la información contable sobre derivados financieros con la presencia cada vez más próxima de las proposiciones del IASB tuvieron una influencia significativa en la transparencia y calidad de los datos contables reportados por las entidades cotizadas. Dada la evolución normativa y el período de tiempo considerado (2000 2002), el trabajo centra su atención en información voluntaria sobre derivados financieros y actividades de riesgo, considerando como única obligación legal la consecución de la imagen fiel del patrimonio y de la situación financiera de las entidades emisoras. En función de ello el trabajo se divide en tres partes. En una primera, se analiza si ha existido un cambio en la información voluntaria facilitada por las empresas de la muestra durante el periodo de estudio considerado, evidenciando un aumento estadísticamente significativo durante el periodo de estudio. En una segunda parte del estudio, se emplean los modelos Tobit y Probit para analizar los determinantes que determinan la revelación de información sobre derivados financieros, y se encuentra evidencia de que la mejora informativa sólo se produce justificada por el tamaño de la empresa y el volumen de endeudamiento, corroborando parcialmente otros trabajos desarrollados en mercados internacionales alternativos. En la última parte del estudio hacemos uso de una técnica no paramétrica de clasificación, K-vecinos-más-próximos o Knn, buscando recoger posibles relaciones no lineales no consideradas en los trabajos precedentes

REDES NEURONALES ARTIFICIALES: PREDICCIÓN DE LA VOLATILIDAD DEL TIPO DE CAMBIO DE LA PESETA

In this work, we propose the use of Artificial Neural Networks (ANNs), with theobjective of predicting the volatility of peseta exchange rate. Firstly, we perform anexhaustive analysis of the forecasting ability of ANNs by comparing them against otherARCH-type models. The results suggest that ANN are, on average, better than ARCHmodels. Finally, we also propose new hybrid prediction models of volatility, based onANNs, which use the forecasts of different parametric models. Our results show that themodel is generally better, in mean, than other parametric models as well as a linearaggregation of forecasts. El presente trabajo propone el empleo de las Redes Neuronales Artificiales (RNA) al objeto de predecir la volatilidad del tipo de cambio de la peseta. En primer lugar, realizamos una comparación exhaustiva de la capacidad predictiva de las RNA en relación con otros modelos de la clase ARCH. Los resultados sugieren que, en media, las RNA se comportan mejor que los modelos tipo ARCH. Finalmente, también proponemos nuevos modelos híbridos para predecir la volatilidad que, basados en la técnica de las RNA, utilizan las predicciones de diferentes modelos paramétricos. Nuestros resultados muestran que el modelo híbrido que proponemos, en media, por lo general se comporta mejor que los otros modelos paramétricos y que la agregación lineal de las predicciones.Volatilidad, predicción, no linealidad, modelos no paramétricos, redes neuronales artificiales (RNA). Volatility, prediction, no linearity, non parametric models, artificial neural networks (ANNs)

An assessment of the sea breeze energy potential using small wind turbines in peri-urban coastal areas

From wind speed data recorded hourly at 2 m high during 18 years (1993-2010) in the Llobregat Delta (15 km south of Barcelona city; northeast of the Iberian Peninsula), wind speed distributions at 10 m high were computed for the whole year and for the sea breeze period (from March 1 to September 30, from 10 to 19 local time). Weibull probability density functions fitted to the distributions were used to assess the wind energy generated by two off-grid small wind turbines: the IT-PE-100 and the HP-600W. Results from FAST and AeroDyn simulation tools were compared with those obtained by applying measured wind speeds to manufacturer power curves. Using manufacturer data, the IT-PE-100 would deliver 132 kWh during the whole year (70 kWh during the sea breeze period). From the simulations, the IT-PE-100 would deliver 155 kWh during the whole year (80 kWh during the sea breeze period). It is concluded that the sea-breeze is an interesting wind energy resource for micro-generation, not only in the Mediterranean basin but in other areas of the world with similar wind regimes, and particularly in peri-urban coastal areas where large-scale wind farms cannot be implemented.Peer ReviewedPostprint (published version

Assessment of an Adaptive Load Forecasting Methodology in a Smart Grid Demonstration Project

This paper presents the implementation of an adaptive load forecasting methodology in two different power networks from a smart grid demonstration project deployed in the region of Madrid, Spain. The paper contains an exhaustive comparative study of different short-term load forecast methodologies, addressing the methods and variables that are more relevant to be applied for the smart grid deployment. The evaluation followed in this paper suggests that the performance of the different methods depends on the conditions of the site in which the smart grid is implemented. It is shown that some non-linear methods, such as support vector machine with a radial basis function kernel and extremely randomized forest offer good performance using only 24 lagged load hourly values, which could be useful when the amount of data available is limited due to communication problems in the smart grid monitoring system. However, it has to be highlighted that, in general, the behavior of different short-term load forecast methodologies is not stable when they are applied to different power networks and that when there is a considerable variability throughout the whole testing period, some methods offer good performance in some situations, but they fail in others. In this paper, an adaptive load forecasting methodology is proposed to address this issue improving the forecasting performance through iterative optimization: in each specific situation, the best short-term load forecast methodology is chosen, resulting in minimum prediction errors.This work has been partly funded by the Spanish Ministry of Economy and Competitiveness through the National Program for Research Aimed at the Challenges of Society under the project OSIRIS (RTC-2014-1556-3). The authors would like to thank all of the partners in the OSIRIS project: Unión Fenosa Distribución S.A., Tecnalia, Orbis , Neoris, Ziv Metering Solutions, Telecontrol STM and Universidad Carlos III de Madrid. The authors would also like to thank Charalampos Chelmis (University at Albany-SUNY) for the valuable discussion

Métrica para la evaluación de la accesibilidad en Internet: propuesta y testeo

Tools for automatic evaluation of Web accessibility are fast and responsive, but in order to ascertain the degree of actual compliance with design recommendations, they must be complemented with the personal opinion of an expert. What is required is a methodology combining manual and automatic techniques to assess accurately the level of accessibility of Web platforms. This metric should be thorough in order to follow the Web Accessibility Initiative guidelines, precise, and unambiguous. This article deals with a new questionnaire for experts offering greater objectivity and clarity in the formulation of variables, which will provide a more realistic assessment of Web accessibility and be geared to legislative requirements. Finally, it will be implemented to validate its consistency.Las herramientas de evaluación automática de la accesibilidad Web son un método rápido y oportuno; pero para conocer el grado de cumplimiento real de las recomendaciones de diseño es necesario complementarlo con el juicio personal de un experto. Se requiere una propuesta metodológica que combine técnicas manuales y automáticas para evaluar fidedignamente el nivel de accesibilidad de las plataformas Web. Esta métrica debe ser exhaustiva, ya que debe introducir las pautas que contempla la Web Accessibility Initiative, precisa y carente de ambigüedad. En el presente artículo, se abordará el planteamiento de un nuevo cuestionario dirigido a expertos, dotado de mayor objetividad y claridad en la formulación de las variables, que permitirá aportar una valoración más real de la accesibilidad Web y acorde con la normativa legislativa exigida. Finalmente, será implementado para ratificar su validez y consistencia