Detecting Accounting Frauds in Publicly Traded U.S. Firms: A New Perspective and a New Method

48 Pages Posted: 8 Oct 2015 Last revised: 1 Sep 2019

See all articles by Yang Bao

Yang Bao

Shanghai Jiao Tong University (SJTU) - Antai College of Economics and Management

Bin Ke

National University of Singapore

Bin Li

Wuhan University - Economics and Management School

Y. Julia Yu

University of Virginia

Jie Zhang

Nanyang Technological University (NTU)

Date Written: August 29, 2019

Abstract

Prior accounting research often develops fraud prediction models based on theory-motivated financial ratios. We propose a new fraud prediction model that makes use of raw financial data as its input. In addition, we employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. Our ensemble learning model, which is based on a small set of raw data derived from theory-motivated financial ratios, outperforms two benchmark models: Dechow et al.’s [2011] logistic regression model based on financial ratios derived from the same raw data, and Cecchini et al.’s [2010] support-vector-machine model with a financial kernel that maps the same raw data into a broader set of ratios. We find no evidence that an ensemble learning model based on the same financial ratios, or on the combination of the financial ratios and raw data, outperforms our ensemble learning model. There is also no evidence that an ensemble learning model based on several hundreds of raw data from the three financial statements outperforms our ensemble learning model. Overall, our results suggest the importance of selecting both machine learning method and model input in developing powerful fraud prediction models.

Suggested Citation

Bao, Yang and Ke, Bin and Li, Bin and Yu, Yingri Julia and Zhang, Jie, Detecting Accounting Frauds in Publicly Traded U.S. Firms: A New Perspective and a New Method (August 29, 2019). Available at SSRN: https://ssrn.com/abstract=2670703 or http://dx.doi.org/10.2139/ssrn.2670703

Yang Bao

Shanghai Jiao Tong University (SJTU) - Antai College of Economics and Management ( email )

No.1954 Huashan Road
Shanghai Jiao Tong University
Shanghai, Shanghai 200030
China

Bin Ke

National University of Singapore ( email )

Mochtar Riady Building, BIZ 1, #07-54
15 Kent Ridge Drive
Singapore, 119245
Singapore
+6566013133 (Phone)

Bin Li

Wuhan University - Economics and Management School ( email )

Wuhan, Hubei 430072
China
+86-68756536 (Phone)

HOME PAGE: http://ems.whu.edu.cn/info/1694/10956.htm

Yingri Julia Yu (Contact Author)

University of Virginia ( email )

P.O. Box 400173
Charlottesville, VA 22904-4173
United States

Jie Zhang

Nanyang Technological University (NTU) ( email )

S3 B2-A28 Nanyang Avenue
Singapore, 639798
Singapore

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