Palmér, Matthias

[["dc.bibliographiccitation.firstpage","1221"],["dc.bibliographiccitation.lastpage","1232"],["dc.contributor.author","Roeder, Jan"],["dc.contributor.author","Cardona, Davinia Rodríguez"],["dc.contributor.author","Palmér, Matthias"],["dc.contributor.author","Werth, Oliver"],["dc.contributor.author","Muntermann, Jan"],["dc.contributor.author","Breitner, Michael H."],["dc.date.accessioned","2018-08-27T13:26:20Z"],["dc.date.available","2018-08-27T13:26:20Z"],["dc.date.issued","2018"],["dc.description.abstract","In recent years, the phenomenon of rapidly proliferating FinTech companies along diverse segments of the financial services value chain has attracted considerable interest in academic research and practice. So far, various factors of FinTech venture success have been explored, but there is little empirical insight through the lens of business model theory. To close this gap, we build on a FinTech business model taxonomy and examine 221 FinTech companies in order to statistically infer crucial business model determinants responsible for FinTech venture success. Our findings show that the business model component “Product/Service Offering” is the most important determinant for the success of a FinTech venture."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15578"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.status","final"],["dc.relation.conference","Multikonferenz Wirtschaftsinformatik (MKWI)"],["dc.relation.eventend","2018-03-09"],["dc.relation.eventlocation","Lüneburg"],["dc.relation.eventstart","2018-03-06"],["dc.relation.ispartof","Proceedings of the Multikonferenz Wirtschaftsinformatik (MKWI), Lüneburg, Germany"],["dc.title","Make or Break. Business Model Determinants of FinTech Venture Success"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0219770"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","15"],["dc.contributor.author","Palmér, Matthias"],["dc.contributor.author","Eickhoff, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2018-08-27T13:06:59Z"],["dc.date.available","2018-08-27T13:06:59Z"],["dc.date.issued","2018"],["dc.description.abstract","The phenomenon of herding behavior can be observed throughout human societies and has been discussed from different perspectives in research areas such as information systems or finance. In this context, financial analysts have been found to exhibit a disposition towards herding behavior. We identify the topic selection within analyst reports as a potential source for the detection of herding behavior among financial analysts. In this paper, we utilize a qualitative dataset of about 140,000 analyst reports and 1,500 conference call transcripts. Building on the topic modeling technique latent Dirichlet allocation, we calculate similarity scores between the respective documents. Potential herding behavior is observable by revealing exceptional topic structures within groups of analyst reports. In this study, these are found especially during a phase of economic recession: the financial crisis spanning from 2008 to 2010. Viewing prospects, this approach might complement existing research in herding behavior by quantifying qualitative data not only within the financial domain but also in other research areas."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15577"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.conference","The 26th European Conference on Information Systems"],["dc.relation.eventend","2018-06-28"],["dc.relation.eventlocation","Portsmouth, UK"],["dc.relation.eventstart","2018-06-23"],["dc.relation.ispartof","Proceedings of the 26th European Conference on Information Systems, Portsmouth, UK"],["dc.title","Detecting Herding Behavior Using Topic Mining. The Case of Financial Analysts"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","131"],["dc.bibliographiccitation.lastpage","145"],["dc.bibliographiccitation.volume","345"],["dc.contributor.author","Röder, Jan Eike"],["dc.contributor.author","Palmer, Matthias"],["dc.contributor.editor","Mehandjiev, N."],["dc.contributor.editor","Saadouni, B."],["dc.date.accessioned","2019-07-17T13:14:07Z"],["dc.date.available","2019-07-17T13:14:07Z"],["dc.date.issued","2019"],["dc.description.abstract","The automated analysis of unstructured data that is directly or indirectly relevant to developments on financial markets has attracted attention from researchers and practitioners alike. Recent advances in natural language processing enable a richer representation of textual data with respect to semantical and syntactical characteristics. Specifically, distributed representations of words and documents, commonly referred to as embeddings, are a promising alternative. Consequently, this paper investigates the utilization of these approaches for text analytics in finance. To this end, we synthesize traditional and more recent text representation techniques into a coherent framework and provide explanations of the illustrated methods. Building on this distinction, we systematically analyze the hitherto usage of these methods in the financial domain. The results indicate a surprisingly rare application of the outlined techniques. It is precisely for this reason that this paper aims to connect both finance and natural language processing research and might therefore be helpful in applying new methods at the intersection of the respective research areas."],["dc.identifier.doi","10.1007/978-3-030-19037-8_9"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61717"],["dc.language.iso","en"],["dc.publisher","Springer"],["dc.publisher.place","Cham"],["dc.relation.conference","9th International Workshop, FinanceCom 2018"],["dc.relation.crisseries","Lecture Notes in Business Information Processing"],["dc.relation.eventend","2018-06-22"],["dc.relation.eventlocation","Manchester, UK"],["dc.relation.eventstart","2018-06-22"],["dc.relation.isbn","978-3-030-19036-1"],["dc.relation.isbn","978-3-030-19037-8"],["dc.relation.ispartof","Enterprise Applications, Markets and Services in the Finance Industry"],["dc.relation.ispartofseries","Lecture Notes in Business Information Processing;"],["dc.relation.issn","1865-1348"],["dc.relation.issn","1865-1356"],["dc.title","Document Representation for Text Analytics in Finance"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","113770"],["dc.bibliographiccitation.journal","Decision Support Systems"],["dc.bibliographiccitation.volume","158"],["dc.contributor.author","Roeder, Jan"],["dc.contributor.author","Palmer, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2022-07-01T07:35:43Z"],["dc.date.available","2022-07-01T07:35:43Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1016/j.dss.2022.113770"],["dc.identifier.pii","S0167923622000410"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112244"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-581"],["dc.relation.issn","0167-9236"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Data-driven decision-making in credit risk management: The information value of analyst reports"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]