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Assessing Corporate Growth and Bankruptcy Risk Using Public Data Proxies
No full textThis thesis applies natural language processing (NLP) to job listing data as a novel predictive and explanatory tool for evaluating bankruptcy risk and corporate growth. Unlike models based on traditional financial ratios, which are limited by the sparsity of data for small and private companies, job postings provide abundant, real-time information relevant for nearly all corporations and enhance published corporate evaluation methods.
In this report, we demonstrate that the textual context within job listing data offers a meaningful signal for both predictive and descriptive purposes. Our analysis is applied to a corpus of approximately 51.8 million job listings generated from 6764 unique corporations over the roughly decade-long period from 2010 to 2020.
For bankruptcy prediction, our research presents models with robust predictive performances (accuracy 0.8652; specificity 0.8653; sensitivity 0.8042; ROC-AUC 0.9162) that mirrors or exceeds the predictive capabilities of reference baseline models from literature. We then discuss the potential of topic models as both a predictive and descriptive tool for the overall economy as a whole; though our work indicates limited performance of the topics as a predictive feature. Instead, their descriptive potential lies with the ability to track desired employment and thematic distribution across fields such as remote work-- concentrated in tech and management--or entry-level positions--concentrated in retail or delivery. Finally, we also present models evaluating corporate growth, where our predictions reflect the theoretical economic behaviors of debt-based and cyclical investment in the corporate bond and commodity-driven sectors.Computer Scienc
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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