Journal of Business Economics, forthcoming. doi: 10.1007/s11573-018-0914-8   

Abstract - 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 (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.

Finance Research Letters, forthcoming. doi: 10.1016/j.frl.2018.12.007   

Abstract - We document a significant gender gap in over-indebtedness, as we find women to be less likely to become over-indebted even after controlling for risk attitude, financial literacy and socio-demographic characteristics. However, once we account for loan purposes the gender gap diminishes. Our findings highlight the importance to account for loan purposes when analyzing individuals’ debt behavior.


Analyst herding and investor protection: a cross-country study, with Alexander Kerl
Applied Financial Economics, Vol. 24, No. 8, pp. 533–542 (2014). doi: 10.1080/09603107.2014.889800  

Abstract - Using a multi-national dataset, we investigate the herding behavior of financial analysts. Our results across a range of different countries suggest that analysts consistently deviate from their true forecasts and issue earnings forecasts that are biased by anti-herding. Furthermore, the level of bias (i.e. anti-herding) seems to be systematically higher for forecasts on companies from European countries compared to the US or Japan. We argue that such differences might stem from diverse levels of investor protection and corporate governance as analysts deviate less from true forecasts when the overall information environment is more transparent and company disclosures are of higher quality. Thereby, we proxy investor protection based on the company-level share of institutional ownership as well as on country-level investor protection measures. Our results show that increasing levels of investor protection and corporate governance mitigate the anti-herding behavior. Especially, when companies that are located in high investor protection countries are held by an increasing number of institutional investors, analysts are most reluctant to issue biased forecasts.

Five Essays in Empirical Finance, Dissertation, Gießen.

Abstract - This thesis consists of five independent research papers empirically addressing several questions relating to topics in "Analyst-Research", "Household Finance" and "Content-Analysis". The first paper elaborates on the herding/ anti-herding behavior of financial analysts. The second paper analyzes the role of narrow-scope and broad-scope trust in financial advice.  The third paper analyzes the role of gender and financial literacy in households' debt behavior. The fourth paper describes the adaptation of an English dictionary for content-analyses. The fifth paper analyzes the effect of textual sentiment in CEO speeches held at companies annual general meetings.