Digital Transformation

Our scholars in this research cluster are dedicated to investigating the influence of digital technology on various aspects of corporate decisions, including investment, financing, and operating decisions. Furthermore, we explore the implications of digital transformation for financial reporting and disclosure, with particular emphasis on the role of social media in shaping the corporate information environment. Our research aims to deepen the understanding of the complex and dynamic interactions between digital technology and accounting and finance practices, while contributing to the development of effective strategies for businesses and policymakers to navigate this rapidly evolving landscape.

We highlight a selection of our faculty’s research into digital transformation below:

Misinformation Regulation and Capital Market Quality

The research project evaluates the effectiveness of misinformation regulations, particularly on the capital market, using natural language processing tools.

The study aims to broaden the views of regulators on the potential effects of anti-fake news laws by highlighting how such laws affect not only political processes but also capital markets. By focusing on social media and outcomes on capital markets, specifically, the effect of anti-fake news policies can be evaluated in a measurable and meaningful way by researchers.

Author(s): Liandong Zhang, Lee Kong Chian Professor of Accounting and Deputy Dean of School of Accountancy, Yun Lou, Associate Professor of Accounting, Samuel Tan, Assistant Professor of Accounting, Richard Crowley, Assistant Professor of Accounting

Published in/Presented at: Research@SMU, Awarded MOE Academic Research Fund (AcRF) Tier 2 Grant

The Value of Blockchain Applications – Early Evidence from Asset-Backed Securities

Blockchain applications show immense benefits in ABS investments.
The number of Asset-Backed Security (ABS) issued using blockchain applications thus far has been relatively small, meaning that it's unknown whether the benefits are similar in a wider context.
When the number of users of a blockchain is relatively small, the cost of running and maintaining the blockchain is low. The cost of using blockchain in a large setting may outweigh the benefits.

Author(s): Cheng Qiang, Lee Kong Chian Chair Professor of Accounting and Dean of the School of Accountancy

Published in/Presented at: Conference on Digital Transformation of Financial Markets 2021

What are You Saying? Using Topic to Detect Financial Misreporting

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10-K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of SEC enforcement actions (AAERs) and irregularities identified from financial restatements and 10-K filing amendments. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.

Author(s): Richard Crowley, Assistant Professor of Accounting, SMU, Nerissa C. Brown, University of Illinois at Urbana-Champaign, W. Brooke Elliott, University of Illinois at Urbana-Champaign

Published in/Presented at: Journal of Accounting Research, Forthcoming, 27th Annual Conference on Financial Economics and Accounting