Abstract
Extracting up-to-date information from financial documents can be important in making investment decisions. However, the unstructured nature and enormity of the volume of such data makes manual analysis tedious and time consuming. Information extraction technology can be applied to automatically extract the most relevant and precise financial information. This paper introduces a rule-based information extraction methodology for the extraction of highly accurate financial information to aid investment decisions. The methodology includes a rule-based symbolic learning model trained by the Greedy Search algorithm and a similar model trained by the Tabu Search algorithm. The methodology has been found very effective in extracting financial information from NASDAQ-listed companies. Also, the Tabu Search based model performed better than some well-known systems. The simple rule structure makes the system portable and it should make parallel processing implementations easier.
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