PhD Defence: Automated Detection of Financial Events in News Text
In his dissertation, ‘Automated Detection of Financial Events in News Text’, ERIM’s Frederik Hogenboom investigates the inextricable link between financial events and financial markets, and develops a practical, semi-automatic, self-updating model which can exploit such insights in financial trading. Frederik Hogenboom’s model recognizes the ubiquity of financial events and the difficult of extraction due to the unstructured nature of such information, and responds with simple, expressive extraction rules which can be updated to reflect new terms and references.
Frederik Hogenboom defended his dissertation in the Senate Hall at Erasmus University Rotterdam on Thursday, 11 December 2014. His supervisors were Professor Uzay Kaymak and Professor Franciska de Jong and his co-supervisor was Dr. Flavius Fransincar. Other members of the Doctoral Committee included Professor Rommert Dekker (ERIM), Professor Philipp Cimiano (Universitat Bielefeld) and Professor Arjen de Vries (CWI Amsterdam).
About Frederik Hogenboom
Frederik Hogenboom (April 13, 1987) obtained cum laude the M.Sc. degree in Economics and Informatics from the Erasmus University Rotterdam, the Netherlands, in 2009, specializing in Computational Economics. Already during his Bachelor’s and Master’s programmes, he published research that mainly focused on the Semantic Web and learning agents.
Under the auspices of the Erasmus Research Institute of Management (ERIM) and the Econometric Institute at the Erasmus School of Economics, Frederik continued his line of research in a Ph.D. candidacy supported by the Netherlands Organization for Scientific Research (NWO) – under the Physical Sciences Free Competition project 612.001.009: Financial Events Recognition in News for Algorithmic Trading (FERNAT) – and the Dutch national program COMMIT, where his work was linked to the Infiniti project. In his years at the Erasmus University Rotterdam, Frederik was additionally affiliated to the Erasmus Center of Business Intelligence (ECBI), Erasmus Studio, and the Dutch Research School for Information and Knowledge Systems (SIKS).
Frederik’s current research is primarily targeted toward the multidisciplinary field of business intelligence, where the main focus is on ways to employ financial event discovery in emerging news for algorithmic trading, hereby combining techniques from various disciplines, amongst which Semantic Web, text mining, artificial intelligence, machine learning, linguistics, and finance. Over the years, Frederik has published many papers at prestigious international conferences, contributed to numerous national conferences, and actively ventured to other outlets such as Economie Opinie. Moreover, he has written a handful of book chapters and published a substantial amount of articles in renowned scientific journals.
Additionally, Frederik has reviewed submissions for many international conferences and journals, and has served as a local organizer, program committee member, and session chair in multiple tracks and editions of a few international conferences. Moreover, Frederik was involved in teaching over a dozen programming and other IT-related courses and the supervision of many Bachelor’s and Master’s theses. Last, he has served multiple years in the University Ph.D. Council (EPAR) during his Ph.D. candidacy, cooperating with departmental and national Ph.D. councils and giving Erasmus Ph.D. candidates a face and voice at the university level.
Today’s financial markets are inextricably linked with financial events like acquisitions, profit announcements, or product launches. Information extracted from news messages that report on such events could hence be beneficial for financial decision making. The ubiquity of news, however, makes manual analysis impossible, and due to the unstructured nature of text, the (semi-)automatic extraction and application of financial events remains a non-trivial task. Therefore, the studies composing this dissertation investigate 1) how to accurately identify financial events in news text, and 2) how to effectively use such extracted events in financial applications.
Based on a detailed evaluation of current event extraction systems, this thesis presents a competitive, knowledge-driven, semi-automatic system for financial event extraction from text. A novel pattern language, which makes clever use of the system’s underlying knowledge base, allows for the definition of simple, yet expressive event extraction rules that can be applied to natural language texts. The system’s knowledge-driven internals remain synchronized with the latest market developments through the accompanying event-triggered update language for knowledge bases, enabling the definition of update rules.
Additional research covered by this dissertation investigates the practical applicability of extracted events. In automated stock trading experiments, the best performing trading rules do not only make use of traditional numerical signals, but also employ news-based event signals. Moreover, when cleaning stock data from disruptions caused by financial events, financial risk analyses yield more accurate results. These results suggest that events detected in news can be used advantageously as supplementary parameters in financial applications.
Photos: Chris Gorzeman / Capital Images