What do they mean? Using Media Richness as an Indicator for the Information Value of Stock Analyst Opinion regarding post-earnings Firm Performance

In this research the impact of media-richness on the investor reaction to earnings announcements is investigated. To this end, unstructured (high-richness) sources of analyst opinion are subjected to text-mining and combined with structured (low-richness) sources of analyst opinion, as well as other commonly used structured data relevant to company performance. Results indicate that equivocality is a major problem faced by investors, while uncertainty as understood by media-richness theory appears to be less dominant

Understanding Topic Models in Context: A Mixed-Methods Approach to the Meaningful Analysis of Large Document Collections

In recent years, we have witnessed an unprecedented proliferation of large document collections. This development has spawned the need for appropriate analytical means. In particular, to seize the thematic composition of large document collections, researchers increasingly draw on quantitative topic models. Among their most prominent representatives is the Latent Dirichlet Allocation (LDA). Yet, these models have significant drawbacks, e.g. the generated topics lack context and thus meaningfulness. Prior research has rarely addressed this limitation through the lens of mixed-methods research. We position our paper towards this gap by proposing a structured mixed-methods approach to the meaningful analysis of large document collections. Particularly, we draw on qualitative coding and quantitative hierarchical clustering to validate and enhance topic models through re-contextualization. To illustrate the proposed approach, we conduct a case study of the thematic composition of the AIS Senior Scholars' Basket of Journals

TOPIC MODELLING METHODOLOGY: ITS USE IN INFORMATION SYSTEMS AND OTHER MANAGERIAL DISCIPLINES

Over the last decade, quantitative text mining approaches to content analysis have gained increasing traction within information systems research, and related fields, such as business administration. Recently, topic models, which are supposed to provide their user with an overview of themes being dis-cussed in documents, have gained popularity. However, while convenient tools for the creation of this model class exist, the evaluation of topic models poses significant challenges to their users. In this research, we investigate how questions of model validity and trustworthiness of presented analyses are addressed across disciplines. We accomplish this by providing a structured review of methodological approaches across the Financial Times 50 journal ranking. We identify 59 methodological research papers, 24 implementations of topic models, as well as 33 research papers using topic models in In-formation Systems (IS) research, and 29 papers using such models in other managerial disciplines. Results indicate a need for model implementations usable by a wider audience, as well as the need for more implementations of model validation techniques, and the need for a discussion about the theoretical foundations of topic modelling based research

HOW TO CONQUER INFORMATION OVERLOAD? SUPPORTING FINANCIAL DECISIONS BY IDENTIFYING RELEVANT CONFERENCE CALL TOPICS

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 topic-models 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

COEUR: developing business creativity and Europreneurship in European university networks.

This paper analyses the operating process and participants' feedback of a network of European universities that was set up in 2004, initially to organise annual week-long conferences for the development of students' entrepreneurial competences within a European context and in intercultural teams. Named COEUR - Competence in EuroPreneurship - the project builds on three assumptions: (1) open change and process-orientation require entrepreneurial competences rather than managerial qualifications; (2) business planning builds on a frequently neglected prerequisite: business creativity; and (3) entrepreneurial culture may exist on an intermediate level: EuroPreneurship. Soon the concept was extended to be integrated into regular university curricula as a full semester course - the Business Creativity Module (BCM) - which was developed and implemented with the support of the European Union between 2006 and 2008. Until now around 1,000 European students have participated in various COEUR/BCM programmes. A recent survey among former participants confirmed that not only was their immediate impression genuinely positive, but also, with the benefit of hindsight and after the first experiences in their professional lives, students judged the core values of the concept positively and believed that they had profited from it substantially. By exposing the process and results of the programme, this paper aims to contribute to the awareness of what higher-education institutions can do to enhance the creative and entrepreneurial potential of their students, and possibly serve as an inspiration too

Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition

In recent research, in the domain of speech processing, large End-to-End (E2E) systems for Automatic Speech Recognition (ASR) have reported state-of-the-art performance on various benchmarks. These systems intrinsically learn how to handle and remove noise conditions from speech. Previous research has shown, that it is possible to extract the denoising capabilities of these models into a preprocessor network, which can be used as a frontend for downstream ASR models. However, the proposed methods were limited to specific fully convolutional architectures. In this work, we propose a novel method to extract the denoising capabilities, that can be applied to any encoder-decoder architecture. We propose the Cleancoder preprocessor architecture that extracts hidden activations from the Conformer ASR model and feeds them to a decoder to predict denoised spectrograms. We train our pre-processor on the Noisy Speech Database (NSD) to reconstruct denoised spectrograms from noisy inputs. Then, we evaluate our model as a frontend to a pretrained Conformer ASR model as well as a frontend to train smaller Conformer ASR models from scratch. We show that the Cleancoder is able to filter noise from speech and that it improves the total Word Error Rate (WER) of the downstream model in noisy conditions for both applications.Comment: Submitted and accepted for ICANN 2023 (32nd International Conference on Artificial Neural Networks

Correlative analysis on InGaN/GaN nanowires: structural and optical properties of self-assembled short-period superlattices

: The influence of self-assembled short-period superlattices (SPSLs) on the structural and optical properties of InGaN/GaN nanowires (NWs) grown by PAMBE on Si (111) was investigated by STEM, EDXS, µ-PL analysis and k·p simulations. STEM analysis on single NWs indicates that in most of the studied nanostructures, SPSLs self-assemble during growth. The SPSLs display short-range ordering of In-rich and In-poor InxGa1-xN regions with a period of 2-3 nm that are covered by a GaN shell and that transition to a more homogenous InxGa1-xN core. Polarization- and temperature-resolved PL analysis performed on the same NWs shows that they exhibit a strong parallel polarized red-yellow emission and a predominantly perpendicular polarized blue emission, which are ascribed to different In-rich regions in the nanostructures. The correlation between STEM, µ-PL and k·p simulations provides better understanding of the rich optical emission of complex III-N nanostructures and how they are impacted by structural properties, yielding the significant impact of strain on self-assembly and spectral emission

The IronChip evaluation package: a package of perl modules for robust analysis of custom microarrays

<p>Abstract</p> <p>Background</p> <p>Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available.</p> <p>Results</p> <p>The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls.</p> <p>Conclusions</p> <p>ICEP is a stand-alone Windows application to obtain optimal data quality from custom-designed microarrays and is freely available here (see "Additional Files" section) and at: <url>http://www.alice-dsl.net/evgeniy.vainshtein/ICEP/</url></p