Musa Subasi Directory Page
Musa Subasi
Associate Professor
Ph.D., University of Texas at Dallas
Musa Subasi, PhD, Associate Professor of Accounting and Information Assurance, joined the Smith School in 2015. He earned a master’s degree in economics from the University of Texas at Austin and a PhD in accounting from the University of Texas at Dallas. Prior to joining the Smith School, Subasi was a faculty member at the University of Missouri-Columbia. He has taught undergraduate intermediate accounting classes. His research interests include economic consequences of investor conferences and institutional/individual trading activity around various corporate events. His research has been published in leading journals, including the Journal of Accounting Research, the Journal of Accounting and Economics and the Journal of Financial Economics.
Publications
Broker-hosted investor conferences (with C. Green, R. Jame, and S. Markov), 2014, Journal of Accounting and Economics 58, 142-166.
Are trade size based inferences about traders reliable? Evidence from institutional earnings related trading (with W. Cready and A. Kumas), 2014, Journal of Accounting Research 52, 877-909.
Access to management and the informativeness of analyst research (with C. Green, R. Jame, and S. Markov), 2014, Journal of Financial Economics 114, 239-255.
Determinants and consequences of information processing delay: Evidence from Thomson Reuters' Institutional Brokers' Estimate System (with F. Akbas, S. Markov, and E. Weisbrod), 2018, Journal of Financial Economics 127, 366-388.
An Empirical Analysis of Analysts' Capital Expenditure Forecasts: Evidence from Corporate Investment Efficiency (with J. Choi, R. Hann, and Y. Zheng), 2020, Contemporary Accounting Research 37, 2615-2648.
Investor conferences, stock liquidity, and firm performance (with P. Brockman and C. Uzmanoglu), 2017, The Financial Review 52,661-699. Recipient of the Financial Review Readers' Choice Best Paper Award in 2017
Analyst tipping: Additional evidence (with S. Markov and V. Muslu), 2017, Journal of Business Finance & Accounting 44, 94-115.
One of the primary data sources for our research is the Alpha Vantage Open-Access Stock API.
Research
What can capital expenditure predictions tell us about our investments?
Insights
Academic Publications
Do Credit Rating Agencies Learn from the Options Market?
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
AI-powered Analysts
We explore how brokerage firms’ investments in artificial intelligence (AI) affect their analysts’ information production. We find that analysts employed at brokerage firms with greater AI integration issue more accurate earnings forecasts. Cross-sectional analyses reveal that AI’s benefits are more pronounced for analysts with less firm-specific experience and when the firm’s disclosures are more readable. Further tests indicate that a key mechanism driving the improvement in forecast accuracy is that AI adoption helps mitigate the adverse effects of analyst decision fatigue and optimism bias. Finally, we find that forecast revisions made by AI-powered analysts are more informative to capital markets. Overall, our evidence points to the advantageous impact of AI on information production capabilities of financial analysts.
Michael Kimbrough, Musa Subasi, Yang Liu