Polytope Fraud Theory

64 Pages Posted: 24 May 2022

See all articles by Dongshuai Zhao

Dongshuai Zhao

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)

Zhongli Wang

Bielefeld University; Catholic University of Milan

Florian Schweizer-Gamborino

PwC Switzerland

Didier Sornette

Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Tokyo Institute of Technology

Date Written: May 20, 2022

Abstract

Polytope Fraud Theory (PFT) extends the existing triangle and diamond theories of accounting fraud with ten abnormal financial practice alarms that a fraudulent firm might trigger. These warning signals are identified through evaluation of the shorting behavior of sophisticated activist short sellers, which are used to train several supervised machine-learning methods in detecting financial statement fraud using published accounting data. Our contributions include a systematic manual collection and labeling of companies that are shorted by professional activist short sellers. We also combine well-known asset pricing factors with accounting red flags in financial features selections. Using 80 percent of the data for training and the remaining 20 percent for out-of-sample test and performance assessment, we find that the best method is XGBoost, with a Recall of 79 percent and F1-score of 85 percent. Other methods have only slightly lower performance, demonstrating the robustness of our results. This shows that the sophisticated activist short sellers, from whom the algorithms are learning, have excellent accounting insights, tremendous forensic analytical knowledge, and sharp business acumen. Our feature importance analysis indicates that potential short-selling targets share many similar financial characteristics, such as bankruptcy or financial distress risk, clustering in some industries, inconsistency of profitability, high accrual, and unreasonable business operations. Our results imply the possible automation of advanced financial statement analysis, which can both improve auditing processes and effectively enhance investment performance. Finally, we propose the Unified Investor Protection Framework, summarizing and categorizing investor-protection related theories from the macro-level to the micro-level.

Keywords: fraud risk assessment, financial fraud, fraud detection, machine learning

JEL Classification: C45, C53, M40, M41

Suggested Citation

Zhao, Dongshuai and Wang, Zhongli and Schweizer-Gamborino, Florian and Sornette, Didier, Polytope Fraud Theory (May 20, 2022). Swiss Finance Institute Research Paper No. 22-41, Available at SSRN: https://ssrn.com/abstract=4115679 or http://dx.doi.org/10.2139/ssrn.4115679

Dongshuai Zhao

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

LEE G104
Leonhardstrasse 21
Zurich
Switzerland
+61451104668 (Phone)

Zhongli Wang

Bielefeld University ( email )

Universitätsstraße 25
Bielefeld, NRW 33613
Germany

Catholic University of Milan

1 Largo A. Gemelli
Milano (Milan), MI Milano 20123
Italy

Florian Schweizer-Gamborino

PwC Switzerland ( email )

Birchstrasse 160
Zurich, 8050
Switzerland

Didier Sornette (Contact Author)

Risks-X, Southern University of Science and Technology (SUSTech) ( email )

1088 Xueyuan Avenue
Shenzhen, Guangdong 518055
China

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

ETH Zürich - Department of Management, Technology, and Economics (D-MTEC) ( email )

Scheuchzerstrasse 7
Zurich, ZURICH CH-8092
Switzerland
41446328917 (Phone)
41446321914 (Fax)

HOME PAGE: http://www.er.ethz.ch/

Tokyo Institute of Technology ( email )

2-12-1 O-okayama, Meguro-ku
Tokyo 152-8550, 52-8552
Japan

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