The verbal side of financial data analysis – a study on machine learning capabilities
- AS A MATTER OF COURSE, QUANTITATIVE DATA SUCH AS TIME SERIES AND QUARTERLY FIGURES ARE FREQUENTLY USED IN FINANCIAL ANALYSIS. SUCH DATA CAN BE PROCESSED AUTOMATICALLY AND INTERPRETED RATHER EFFICIENTLY. HOWEVER, A SIGNIFICANT PERCENTAGE OF RELEVANT INFORMATION ORIGINATES FROM UNSTRUCTURED SOURCES, PRIMARILY TEXTUAL DATA, WHICH REQUIRE MANUAL (HUMAN) INTERPRETATION. WE EXPLORE EMPIRICALLY HOW MACHINE LEARNING TECHNIQUES CAN PROVIDE SUPPORT FOR ANALYZING AND INTERPRETING SUCH TEXTUAL DATA SOURCES.
Author: | Jan MuntermannORCiDGND |
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URN: | urn:nbn:de:hebis:30:3-578853 |
ISSN: | 1866-1238 |
Parent Title (English): | EFL quarterly : an E-Finance Lab publication |
Publisher: | E-Finance Lab e.V. |
Place of publication: | Frankfurt am Main |
Document Type: | Article |
Language: | English |
Year of Completion: | 2010 |
Year of first Publication: | 2010 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2021/01/28 |
Volume: | 2010 |
Issue: | 1 |
Page Number: | 2 |
First Page: | 4 |
Last Page: | 5 |
HeBIS-PPN: | 47719284X |
Institutes: | Angeschlossene und kooperierende Institutionen / E-Finance Lab e.V. |
Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Sammlungen: | Universitätspublikationen |
Licence (German): | Deutsches Urheberrecht |