Polytope Fraud Theory

Dongshuai Zhao, Zhongli Wang, Florian Schweizer-Gamborino and Didier Sornette
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Dongshuai Zhao: ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)
Zhongli Wang: Bielefeld University
Florian Schweizer-Gamborino: Price Waterhouse Coopers (PwC)
Didier Sornette: ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Swiss Finance Institute; Southern University of Science and Technology; Tokyo Institute of Technology

No 22-41, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 (search for similar items in EconPapers)
JEL-codes: C45 C53 M40 M41 (search for similar items in EconPapers)
Pages: 64 pages
Date: 2022-05
New Economics Papers: this item is included in nep-acc, nep-big and nep-cmp
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