Topic modeling of financial accounting research over 70 years

Abstract

Abstract I utilize latent Dirichlet allocation and dynamic topic model that are machine learning algorithms across a data set encompassing 25,990 financial accounting articles issued from 1956 to 2023 in 16 accounting journals, and impartially ascertain 20 research topics. The topics of mergers and acquisitions, disclosure and internal control, and political connection exhibited the most rapid expansion, whereas management control systems, earnings management, and valuation experienced the greatest contraction from 2014 to 2023. I also catalog the most referenced papers for each topic and highlight the most swiftly expanding and contracting topics within the realm of 21,620 SSRN working papers. Additionally, my analysis reveals a declining trend in the concentration of research interests within published articles over the preceding seven decades. This research on topic classification itself will aid accounting investigators in bypassing superfluous efforts and fostering increased interdisciplinary research.

Keywords

• accounting research
• machine learning
• topic modeling