Data Mining Applications in Accounting and Finance Context

Wikil Kwak, Yong Shi and Cheng Few Lee

Chapter 21 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 823-857 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This chapter shows examples of applying several current data mining approaches and alternative models in an accounting and finance context such as predicting bankruptcy using US, Korean, and Chinese capital market data. Big data in accounting and finance context is a good fit for data analytic tool applications like data mining. Our previous study also empirically tested Japanese capital market data and found similar prediction rates. However, overall prediction rates depend on different countries and time periods (Mihalovic, 2016). These results are an improvement on previous bankruptcy prediction studies using traditional probit or logit analysis or multiple discriminant analysis. The recent survival model shows similar prediction rates in bankruptcy studies. However, we need longitudinal data to use the survival model. Because of computer technology advances, it is easier to apply data mining approaches. In addition, current data mining methods can be applied to other accounting and finance contexts such as auditor changes, audit opinion prediction studies, and internal control weakness studies. Our first paper shows 13 data mining approaches to predict bankruptcy after the Sarbanes–Oxley Act (SOX) (2002) implementation using 2008–2009 US data with 13 financial ratios and internal control weaknesses, dividend payout, and market return variables. Our second paper shows application of a Multiple Criteria Linear Programming Data Mining Approach using Korean data. Our last paper shows bankruptcy prediction models using Chinese firm data via several data mining tools and compared with those of traditional logit analysis. Analytic Hierarchy Process and Fuzzy Set also can be applied as an alternative method of data mining tools in accounting and finance studies. Natural language processing can be used as a part of the artificial intelligence domain in accounting and finance in the future (Fisher et al., 2016).

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9789811202391_0021 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9789811202391_0021 (text/html)
Ebook Access is available upon purchase.

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:wsi:wschap:9789811202391_0021

Ordering information: This item can be ordered from

Access Statistics for this chapter

More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().