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Indiana University Bloomington

Alva L. Prickett Chair and Professor of Accounting

Messod Daniel Beneish

Photo of Daniel Beneish

Hodge Hall 5100
(812) 855-2628
dbeneish [at] indiana [dot] edu

My educational background consists of Masters in Public Accounting from McGill University in 1980 and an MBA in 1984 and a PhD in 1987 from the University of Chicago. Before attending graduate school, I held a Chartered Accountant license (the equivalent of a CPA) and worked as an auditor and as a consultant for Coopers & Lybrand (the “C” in PwC) in Montreal, Canada. I have had the opportunity to visit, teach and do research at Buffalo, Chicago, Duke, HEC Paris, IE Madrid, INSEAD and Michigan. At Indiana University, where I have been on faculty since 1996, I have taught Financial Accounting, Financial Statement Analysis, Detecting Earnings Management at the Masters’ level and a doctoral seminar on empirical research. Details of my teaching are available in my curriculum vitae. I have published 33 articles in leading accounting, economics and finance journals (see vitae). My research interests are in the area of corporate governance, earnings management, fraud detection, and insider trading. I recently served as co-editor at the European Accounting Review for a Special Issue themed “New Directions in Earnings Management and Fraud Research” which was published in 2018. I have also served as keynote speaker at the Inaugural Conference of the U.K. Society of Certified Financial Analysts (July 2015, London, U.K.), and at the 40th Journal of Accounting and Public Policy in June 2022 (university of Maryland, College Park, MD).

One of my research articles describes a screening model that helps auditors, investors, and lenders detect companies most likely to create misleading financial reports. The model has gained acceptance among accounting and investing professionals as model to detect earnings manipulation (for example, its output is featured in Bloomberg’s and in Audit Analytics). The screening model flags many companies before the public discovers the earnings manipulation. This turns out to be somewhat useful in avoiding the losses that invariably accompany such revelations.

I have recently expanded the predictive validity of the M-Score from the individual firm (micro) level to a macroeconomic level. In a forthcoming 2023 paper in The Accounting Review my co-authors and I show that an economy-wide measure of the M-Score constructed by value-weighting the M-Scores of individual firms improves predictions about future recessions and economic slowdowns. This aggregate M-Score predicts recessions five to eight quarters ahead and is significantly associated with lower future growth in real GDP, real investment, consumption, and industrial production. This extends the impact of the M-Score from predicting individual firm outcomes to economy-wide outcomes.

View my C.V.