News Analytics for Financial Decision Support

This PhD thesis contributes to the newly emerged, growing body of scientific work on the use of News Analytics in Finance. Regarded as the next significant development in Automated Trading, News Analytics extends trading algorithms to incorporate information extracted from textual messages, by translating it into actionable, valuable knowledge. The thesis addresses one main theme: the incorporation of news into trading algorithms. This relates to three main tasks: i) the extraction of the information contained in news, ii) the representation of the information contained in news, and iii) the aggregation of this information into actionable knowledge. We validate our approach by designing and implementing three semantic systems: a system for the computational content analysis of European Central Bank statements, a system for incorporating news in stock trading strategies, and a time-aware system for trading based on analyst recommendations. The approach we choose for addressing these tasks is an interdisciplinary one. For the extraction of information from news we rely on approaches borrowed from Computer Science and Linguistics. The representation of the information contained in news is realized by using, and extending, the state-of-the-art in Semantic Web technology. We do this by bringing together insights from Logics, Metaphysics, and Computational Semantics. The aggregation of information is done by using techniques and results from Computational Intelligence and Financ

Systèmes interactifs auto-adaptatifs par systèmes multi-agents auto-organisateurs : application à la personnalisation de l'accès à l'information

Les réseaux de systèmes d'information tendent à devenir de plus en plus complexes en raison de leur hétérogénéité, de leur dynamique et de leur croissance permanente. Afin de gérer cette complexité et ces problèmes de surcharge informationnelle, les moteurs de recherche actuels s'appuient sur la notion de profil usager qui représente les centres d'intérêts, les préférences et les besoins d'un individu. Or, ces techniques dérivées de la recherche d'information et de l'apprentissage artificiel ne proposent pas de solution réellement adaptative pour la prise en compte de l'aspect évolutif du profil et le respect de la vie privée de l'utilisateur. Nous proposons d'exploiter le paradigme des systèmes multi-agents, et plus spécifiquement l'approche par AMAS (Adaptive Multi-Agent System), pour apporter une solution distribuée à la personnalisation et à l'adaptation des services offerts aux utilisateurs. Nos contributions portent tout d'abord sur l'évaluation adaptative et personnalisée du feedback implicite de l'utilisateur, puis sur la construction adaptative de son profil à partir de documents textuels représentant ses intérêts. Elles proposent également une plateforme nommée SWAPP dédiée à la recherche d'information personnalisée sur le Web. Ce cadre applicatif a permis d'expérimenter nos deux premières contributions individuellement, puis conjointement. Cette évaluation simultanée a mis en évidence un nouveau problème théorique : le couplage de deux AMAS conçus de manière totalement indépendante. Ce travail propose ainsi une première approche pour la conception de systèmes de systèmes auto-adaptatifs.Networks of information systems are becoming more and more complex due to their heterogeneity, their dynamics and their continuous growing. In order to cope with this information overload and this complexity, nowadays search engines make use of the notion of user profile that aim to model main interests, preferences and user's needs. Nevertheless, these techniques, derived from information retrieval and artificial learning research field, does not represent truly adaptive solutions able to cope with user profiles evolutions and to ensure user's privacy. Faced to these challenges, we propose to use the multi-agent system paradigm, and more specifically the AMAS approach (Adaptive Multi-Agent System), in order to provide a distributed solution for the personalisation and the adaptation of services and information access. Our first contribution consists in the adaptive and personalised evaluation of user implicit feedback. The second contribution studies the adaptive modelling of user profile from textual documents that represents its interests. We also propose the SWAPP platform which is dedicated to the evaluation of our approach to the web personalised information retrieval. After the individual experimentation and validation of these two first contributions within this applicative framework, they have been evaluated together. This last evaluation underlined a new theoretical problem : the coupling of two AMAS that were independently designed. Thus, this study proposes a first approach for the design of systems of self-adaptive systems