Optimal investment and hedging under partial and inside information

This article concerns optimal investment and hedging for agents who must use trading strategies which are adapted to the filtration generated by asset prices, possibly augmented with some inside information related to the future evolution of an asset price. The price evolution and observations are taken to be continuous, so the partial (and, when applicable, inside) information scenario is characterised by asset price processes with an unknown drift parameter, which is to be filtered from price observations. We first give an exposition of filtering theory, leading to the Kalman-Bucy filter. We outline the dual approach to portfolio optimisation, which is then applied to the Merton optimal investment problem when the agent does not know the drift parameter of the underlying stock. This is taken to be a random variable with a Gaussian prior distribution, which is updated via the Kalman filter. This results in a model with a stochastic drift process adapted to the observation filtration, and which can be treated as a full information problem, and an explicit solution to the optimal investment problem is possible. We also consider the same problem when the agent has noisy knowledge at time $0$ of the terminal value of the Brownian motion driving the stock. Using techniques of enlargement of filtration to accommodate the insider's additional knowledge, followed by filtering the asset price drift, we are again able to obtain an explicit solution. Finally we treat an incomplete market hedging problem. A claim on a non-traded asset is hedged using a correlated traded asset. We summarise the full information case, then treat the partial information scenario in which the hedger is uncertain of the true values of the asset price drifts. After filtering, the resulting problem with random drifts is solved in the case that each asset's prior distribution has the same variance, resulting in analytic approximations for the optimal hedging strategy

Arquitecturas de Generación de Contenido Colaborativo para sistemas basados en Realidad Aumentada Móvil

La evolución actual de los terminales móviles ha propiciado el surgimiento de un nuevo campo de investigación relacionado con las aplicaciones móviles colaborativas basadas en Realidad Aumentada. Debido a su inmadurez, es necesaria una conceptualización de términos que aclaren un entorno hasta el momento complejo y poco estructurado. Este artículo propone una nueva taxonomía llamada “Pirámide de Generación de Contenido Colaborativo” que clasifica este tipo de aplicaciones en tres niveles: aisladas, sociales y en tiempo real. Dicha clasificación describe las diferentes arquitecturas que se deben tener en cuenta para conseguir sistemas de cada uno de estos niveles, teniendo en cuenta la forma en que el contenido aumentado es generado y cómo se lleva a cabo la colaboración. Por tanto, el principal objetivo es clarificar terminología relativa a este nuevo paradigma, a la vez que se propone un marco para identificar y clasificar futuras investigaciones relativas a este entorno

Generating Awareness from Collaborative Working Environment using Social Data

Nowadays, Internet is a place where social networks have reached an important impact in collaboration among people over the world in different ways. This paper proposes a new paradigm of building CSCW tools for business world following these new ideas provided by the social web to collaborate and generate awareness. An implementation of these concepts is described, including the components we provide to collaborate in workspaces, (such as videoconference, chat, desktop sharing, forums or temporal events), and the way we generate awareness from these complex social data structures. We also present figures and validation results in the paper to stress that this architecture has been defined to support awareness generation via joining current and future social data from business and social networks worlds, based on the idea of using social data stored in the cloud

Knowledge Management and Information Systems based on Workflow Technology

Knowledge management is critical for the success of virtual communities, especially in the case of distributed working groups. A representative example of this scenario is the distributed software development, where it is necessary an optimal coordination to avoid common problems such as duplicated work. In this paper the feasibility of using the workflow technology as a knowledge management system is discussed, and a practical use case is presented. This use case is an information system that has been deployed within a banking environment. It combines common workflow technology with a new conception of the interaction among participants through the extension of existing definition languages

Collaborative Content Generation Architectures for the Mobile Augmented Reality Environment

The increasing adoption of smartphones by the society has created a new research area in mobile collaboration. This new domain offers an interesting set of possibilities due to the introduction of augmented reality techniques, which provide an enhanced collaboration experience. As this area is relatively immature, there is a lack of conceptualization, and for this reason, this paper proposes a new taxonomy called Collaborative Content Generation Pyramid that classifies the current and future mobile collaborative AR applications in three different levels: Isolated, Social and Live. This classification is based on the architectures related to each level, taking into account the way the AR content is generated and how the collaboration is carried out. Therefore, the principal objective of this definition is to clarify terminology issues and to provide a framework for classifying new researches across this environment

Generación de Contexto Colaborativo a partir de herramientas CSCW 2.0

Actualmente Internet se ha convertido con toda probabilidad en el medio de colaboración más extendido en el mundo, permitiendo diferentes vías de llevarla a cabo. Este artículo propone un nuevo paradigma de diseño de aplicaciones CSCW orientadas al mundo empresarial siguiendo las nuevas ideas surgidas de la Web Social para, permitir una colaboración completa y además proporcionar un contexto colaborativo detallado a sus usuarios. Una implementación real de estos conceptos se detalla incluyendo descripciones de las herramientas ofrecidas para colaborar en espacios de trabajo, además de una explicación de cómo se realiza la generación de contexto colaborativo a partir de estructuras de datos sociales complejas. Adicionalmente presentamos resultados que validan esta nueva arquitectura para soportar la generación de contexto colaborativo usando datos sociales extraídos tanto de aplicaciones personales, como de aplicaciones empresariales, reforzando por tanto la idea de utilizar como entradas datos sociales alojados en la nube

Generating context-aware recommendations using banking data in a mobile recommender system

The increasing adoption of smartphones by the society has created a new area of research in recommender systems. This new domain is based on using location and context-awareness to provide personalization. This paper describes a model to generate context-aware recommendations for mobile recommender systems using banking data in order to recommend places where the bank customers have previously spent their money. In this work we have used real data provided by a well know Spanish bank. The mobile prototype deployed in the bank Labs environment was evaluated in a survey among 100 users with good results regarding usefulness and effectiveness. The results also showed that test users had a high confidence in a recommender system based on real banking data

Proactividad y contextualización: futuro del diseño de sistemas recomendadores

En el entorno tecnosocial actual la importancia de ofrecer contenidos personalizados a los usuarios de las diferentes plataformas sociales existentes, que se ajusten a sus necesidades en cada momento, es un factor clave para el éxito de las mismas. Los sistemas recomendadores juegan un papel crucial, pero en muchos casos su nivel de personalización es escaso o demasiado generalista. Se analizan dos aspectos claves para la evolución de estos sistemas: la proactividad y la contextualización. Se propone un modelo teórico de referencia para la creación de sistemas de recomendación proactivos basados en información contextual, y se comprueba su viabilidad en dos escenarios reales donde han sido implementados con éxito: el bancario y el de las redes personales de aprendizaje. Finalmente se destacan líneas de actuación futuras siguiendo las aportaciones expuestas con especial atención a su aplicación en entornos educativos

Incorporating proactivity to context-aware recommender systems for e-learning

Recommender systems in e-learning have proved to be powerful tools to find suitable educational material during the learning experience. But traditional user request-response patterns are still being used to generate these recommendations. By including contextual information derived from the use of ubiquitous learning environments, the possibility of incorporating proactivity to the recommendation process has arisen. In this paper we describe methods to push proactive recommendations to e-learning systems users when the situation is appropriate without being needed their explicit request. As a result, interesting learning objects can be recommended attending to the user?s needs in every situation. The impact of this proactive recommendations generated have been evaluated among teachers and scientists in a real e-learning social network called Virtual Science Hub related to the GLOBAL excursion European project. Outcomes indicate that the methods proposed are valid to generate such kind of recommendations in e-learning scenarios. The results also show that the users' perceived appropriateness of having proactive recommendations is high

Enhanced recommendations for e-learning authoring tools based on a proactive context-aware recommender

Authoring tools are powerful systems in the area of e-Learning that make easier for teachers to create new learning objects by reusing or editing existing educational resources coming from learning repositories or content providers. However, due to the overwhelming number of resources these tools can access, sometimes it is difficult for teachers to find the most suitable resources taking into account their needs in terms of content (e.g. topic) or pedagogical aspects (e.g. target level associated to their students). Recommender systems can take an important role trying to mitigate this problem. In this paper we propose a new model to generate proactive context-aware recommendations on resources during the creation process of a new learning object that a teacher carries out by using an authoring tool. The common use cases covered by the model for having recommendations in online authoring tools and details about the recommender model itself are presented