Bannier, Christina E.
[Verfasser:in]
;
Pauls, Thomas
[Sonstige Person, Familie und Körperschaft];
Walter, Andreas
[Sonstige Person, Familie und Körperschaft]
Anmerkungen:
In: Journal of Business Economics (2018). doi: 10.1007/s11573-018-0914-8
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 1, 2018 erstellt
Beschreibung:
Computer-aided text analyses have gained a lot of attention recently. Applied to different types of business communication such as earnings announcements, analyst reports, or IPO prospectuses, they have been used to extract relevant information for financial market participants. A large number of studies employ dictionary-based approaches by referring to specific word lists. Since these lists have been predominantly compiled for the English language, the respective analyses have focused on English business texts. In order to amplify the application of content analyses to other languages, we create a German dictionary designed to measure the textual sentiment of business communication. Our dictionary is based on the English dictionary by Loughran and McDonald (J Finance 66:35–65. doi: 0.1111/j.1540-6261.2010.01625.x, 2011), which is commonly used for examining finance- and accounting-specific texts. We discuss the set-up of our dictionary and extensively test its quality. We further compare our dictionary to German general language dictionaries and to a machine-learning procedure and provide evidence for its ability to capture market-relevant textual sentiment of German business communication