Eickhoff, Matthias

[["dc.bibliographiccitation.artnumber","312"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","10"],["dc.contributor.author","Eickhoff, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2018-08-27T14:14:35Z"],["dc.date.available","2018-08-27T14:14:35Z"],["dc.date.issued","2016"],["dc.description.abstract","In this study, we examine stock analyst information processing behaviour on the example of information transfer between analyst conference calls and analyst reports. From a theoretical perspective, the study contributes to an understanding of analysts’ recommendation biases resulting from their information processing. It provides new insights on how information is actually used by analysts, while practical implications for both sides of conference calls and other market participants are examined. Results indicate that analysts are exposed to new information during conference call events, which they conse-quently incorporate in their reporting."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15581"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Association for Information Systems AIS Electronic Library (AISeL)"],["dc.relation.conference","The 20th Pacific Asia Conference on Information Systems"],["dc.relation.eventend","2016-07-01"],["dc.relation.eventlocation","Chiayi, Taiwan"],["dc.relation.eventstart","2016-06-27"],["dc.relation.isbn","978-986-04-9102-9"],["dc.relation.ispartof","Proceeding of the 20th Pacific Asia Conference on Information Systems (PACIS 2016)"],["dc.title","They Talk but What do they Listen to? Analyzing Financial Analysts' Information Processing using Latent Dirichlet Allocation"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","835"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Information & Management"],["dc.bibliographiccitation.lastpage","845"],["dc.bibliographiccitation.volume","53"],["dc.contributor.author","Eickhoff, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2018-08-23T14:26:24Z"],["dc.date.available","2018-08-23T14:26:24Z"],["dc.date.issued","2016"],["dc.description.abstract","We examine the drivers of crowd wisdom in the financial domain by relating analyst report and social media sentiment via Granger causality (GC) testing based on the wisdom of crowds (WoC) theory. The significance of a large number of the tested time series indicates that analyst reports and social media content are suitable for mutual prediction. We elaborate on the conditions under which crowd cognitive diversity matters, and we derive related measures. The results suggest that the WoC theory can partially explain the GC between the two media types and that both professional analysts and the crowd can outperform one another under favorable circumstances."],["dc.identifier.doi","10.1016/j.im.2016.03.008"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15512"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.title","Stock Analysts vs. the Crowd: Mutual Prediction and the Drivers of Crowd Wisdom"],["dc.title.subtitle","Mutual prediction and the drivers of crowd wisdom"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","144"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Pacific Asia Conference on Information Systems"],["dc.bibliographiccitation.lastpage","14"],["dc.contributor.author","Eickhoff, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2018-08-27T14:48:22Z"],["dc.date.available","2018-08-27T14:48:22Z"],["dc.date.issued","2016"],["dc.description.abstract","The predictive power of stock analyst reports has been used to relate report contents to stock returns or describe herding behavior of analysts themselves. In this paper, the sentiment of analyst reports is related to that of a large social media data set via Granger Causality testing on the basis of wisdom of crowds theory based considerations, in order to investigate whether the two types of content are inherently related or not. Results show strong significance for a large number of the tested time series, indicating that the two types of content are indeed suitable for mutual prediction. In addition, we elaborate on the conditions under which cognitive diversity of the crowd matters. Furthermore, a second analysis stage provides evidence for which type of company and news environment a particular direction of granger cause arises between the two types of content."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15584"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Association for Information Systems AIS Electronic Library ( AISeL)"],["dc.relation.conference","The 19th Pacific Asia Conference on Information Systems"],["dc.relation.eventend","2016-07-09"],["dc.relation.eventlocation","Singapore"],["dc.relation.eventstart","2016-07-06"],["dc.relation.ispartof","Proceedings of the 19th Pacific Asia Conference on Information Systems"],["dc.title","Stock Analysts Vs. The Crowd"],["dc.title.subtitle","A Study on Mutual Prediction"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","317"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","16"],["dc.contributor.author","Eickhoff, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2018-08-27T14:02:23Z"],["dc.date.available","2018-08-27T14:02:23Z"],["dc.date.issued","2016"],["dc.description.abstract","The ever rising amount of business communications results in a growing amount of qualitative data relevant to many decision situations. This increase in information volume and velocity threatens to overburden decision makers. We provide a structured approach towards this problem using topicmodels to reduce information overload by filtering content and by providing context-relevant information to decision makers. Building upon theoretical considerations related to phases of the decision process established by Herbert A. Simon, we implement the proposed approach on the example of a large document collection of stock analyst reports and analyst conference calls using Latent Dirichlet Allocation (a topic model). Thereby, we extract investment-relevant topics from the model and discuss the opportunities for decision support resulting from the chosen approach."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/15580"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Association for Information Systems AIS Electronic Library (AISeL)"],["dc.relation.conference","The 20th Pacific Asia Conference on Information Systems"],["dc.relation.eventend","2016-07-01"],["dc.relation.eventlocation","Chiayi, Taiwan"],["dc.relation.eventstart","2016-06-27"],["dc.relation.isbn","978-986-04-9102-9"],["dc.relation.ispartof","Proceeding of the 20th Pacific Asia Conference on Information Systems (PACIS 2016)"],["dc.title","How to conquer Information Overload?"],["dc.title.subtitle","Supporting financial Decisions by identifying relevant Conference Call Topics"],["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.artnumber","22"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.lastpage","19"],["dc.contributor.author","Eickhoff, M."],["dc.contributor.author","Muntermann, Jan"],["dc.contributor.author","Weinrich, Timo"],["dc.date.accessioned","2020-01-30T12:59:37Z"],["dc.date.available","2020-01-30T12:59:37Z"],["dc.date.issued","2017"],["dc.description.abstract","FinTechs are companies that combine technological and financial attributes in their business models. In recent years, the rise of FinTechs has attracted much attention since they challenge incumbent financial service companies including the traditional banking model. In this paper, we aim to contribute to a better understanding of this phenomenon. Therefore, we develop a taxonomy of FinTech business models following a theoretically grounded and empirically validated approach for identifying and defining underlying business model elements. After developing our taxonomy, we use a clustering-based approach to identify business model archetypes on which to showcase our results, reexamine the assumptions made during taxonomy development, and validate the presented findings. Based on the gained insights, we discuss implications for research, practice and policy makers, as well as directions for future research."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62905"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","Association for Information Systems AIS Electronic Library ( AISeL )"],["dc.relation.conference","The 38th International Conference on Information Systems"],["dc.relation.eventend","2017-12-13"],["dc.relation.eventlocation","Seoul, South Korea"],["dc.relation.eventstart","2017-12-10"],["dc.relation.ispartof","Proceeding of the 38th International Conference on Information Systems"],["dc.title","What do FinTechs actually do? A Taxonomy of FinTech Business Models"],["dc.title.subtitle","A Taxonomy of FinTech Business Models"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","317"],["dc.contributor.author","Eickhoff, Matthias"],["dc.contributor.author","Muntermann, Jan"],["dc.date.accessioned","2020-01-29T12:54:47Z"],["dc.date.available","2020-01-29T12:54:47Z"],["dc.date.issued","2016"],["dc.description.abstract","The ever rising amount of business communications results in a growing amount of qualitative data relevant to many decision situations. This increase in information volume and velocity threatens to overburden decision makers. We provide a structured approach towards this problem using topicmodels to reduce information overload by filtering content and by providing context-relevant information to decision makers. Building upon theoretical considerations related to phases of the decision process established by Herbert A. Simon, we implement the proposed approach on the example of a large document collection of stock analyst reports and analyst conference calls using Latent Dirichlet Allocation (a topic model). Thereby, we extract investment-relevant topics from the model and discuss the opportunities for decision support resulting from the chosen approach."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62895"],["dc.language.iso","en"],["dc.relation.conference","20th Pacific Asia Conference on Information Systems (PACIS 2016)"],["dc.relation.eventend","2016-07-01"],["dc.relation.eventlocation","Chiayi, Taiwan"],["dc.relation.eventstart","2016-06-27"],["dc.relation.isbn","978-986-04-9102-9"],["dc.relation.ispartof","Pacific Asia Conference on Information Systems, PACIS 2016 - Proceedings"],["dc.title","How to conquer information overload? Supporting financial decisions by identifying relevant conference call topics"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]