Driving a More Prosperous Future
Do Credit Rating Agencies Learn from the Options Market?
Management Science, November 2024
Do credit rating agencies (CRAs) learn from the options market? We examine this question by exploring the relation between options trading activity and credit rating accuracy. We find that as options trading volume increases, credit ratings become more responsive to expected credit risk and exhibit greater ability to predict future defaults. We also find that CRAs rely more on the options market as a source of ratings-related information when firm default risk is higher, options trading is more informative, manager-provided information is of lower quality, and firm uncertainty is higher. Our results are robust to a number of sensitivity tests, including alternative measures of options trading and credit rating accuracy. We reach similar inferences using various approaches to address endogeneity issues, including difference-in-difference analyses and an instrumental variables approach. Overall, our findings are consistent with the view that CRAs incorporate unique information from the options market into their rating decisions which, in turn, improves credit rating accuracy.
Musa Subasi, University of Maryland-College Park
Paul Brockman, Lehigh University
Jeff Wang, San Diego State University
Eliza Zhang, University of Washington-Tacoma
The Theory-Based View and Strategic Pivots: The Effects of Theorization and Experimentation on the Type and Nature of Pivots
Strategy Science
We examine how formalization in cognitive processes (theorization) and evidence evaluation (experimentation) influence the type (frequency and radicalness) and nature (impetus, clarity, and coherence) of entrepreneurial pivots. We use a mixed-method research design to analyze rich data from over 1,600 interviews with 261 entrepreneurs within a randomized control trial in London. A quantitative analysis that complements human-coded and machine learning-coded measures reveals that conditional on pivoting, theorization and experimentation are complementary in their association with making single radical pivots. The extensive qualitative-case comparison further elucidates interactions between theorization and experimentation that generate differences in the nature of pivots that range from purposeful (clear and coherent rationale deriving from articulated theory and experimentation), postulatory (informed by articulated theory but not incorporating nuances or surprises generated from experimentation), and remedial (stemming from adjustments to preformed theories that drew on prior experiences) to reactive (driven by environmental stimuli absent a clear theory of value). These insights contribute to the theory-driven strategic decision-making literature and offer practical insights for entrepreneurs, incubators, and policymakers on the benefits of a scientific approach to entrepreneurship.
Valentine, Jacob (Doctoral Candidate, University of Maryland); Novelli, Elena (Professor, Bayes Business School); Agarwal, Rajshree (Lamone Professor of Strategy and Entrepreneurship, University of Maryland)
How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI
The Review of Financial Studies, March 2023
Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.
Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America