Feeding a financial decision support system with textual information
Many expert systems need a lot of data. For a long time this point has appeared to be a bottleneck for the growth of Artificial Intelligence applications. A major way to provide an expert system with knowledge is to enter it by hand. With the maturity of Natural Language Processing (NLP), a new way has been opened with automatic Information Extraction (IE) from text. This paper briefly presents a financial decision support system, named SAPE, connected with an IE system. This application is used by Caisse des depots et consignations (CDC) in order to anticipate takeover bids on the European stock markets. It provides ways to manage the highly complex and moving network of European shareholdings. SAPE is available on CDC group's intranet and is used by fund managers as part of their everyday work. This paper also describes how our NLP system, Exoseme, uses the economic newswire from the Agence France-Presse (French press agency) in order to extract information on shareholdings and how this information is managed by the user to provide SAPE database with the large amount of information needed for its computations. After months of use, IE has appeared to be a powerful concrete solution. Moreover, if the economic value of takeover bids has lead us to pay particular attention to shareholdings, this approach can be extended to other events. In fact, with IE, new possibilities for portfolio decision support systems are coming. This paper presents the improvements we plan, and discusses those, though tempting, that are still out of reach due to the lack of adaptive tools.
decision support system natural language processing information extraction multi-agents system knowledge based system