The application of deep learning to predict corporate growth

Sumitaka Ushio and Nobuhisa Yamamoto

International Journal of Economics and Accounting, 2021, vol. 10, issue 2, 248-263

Abstract: This study examines the use of deep learning to predict corporate growth. An algorithmic model is constructed to identify growing (as well as non-growing) companies based on a snapshot (single year) of financial data without a time series. The binary classification model predicts whether sales will increase in the following year for 353 retail companies in the Tokyo Stock Exchange 33 sector category in Japan, by utilising all available items in their balance sheets and profit/loss statements (308 numerical values) as well as the size of the companies. As a result, the model achieves 74.79% classification accuracy. The area under the curve (AUC) of the model is 0.75, which shows moderate accuracy of prediction regardless of its cut-off point. This study also debates the methodological significance of applying deep learning to accounting research in comparison with traditional (frequentism) statistics.

Keywords: deep learning; growth prediction; accounting research; artificial intelligence; AI; algorithmic modelling. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=115209 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijecac:v:10:y:2021:i:2:p:248-263

Access Statistics for this article

More articles in International Journal of Economics and Accounting from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().