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 % of the data for training and the remaining 20 % for out-of-sample test and performance assessment, we find that the best method is XGBoost, with a Recall of 72 % and F1-score of 82 %. Other methods have relatively 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.

中文翻译：

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 Polytope 欺诈理论


Polytope 欺诈理论 （PFT） 扩展了现有的会计欺诈三角和菱形理论，通过欺诈公司可能触发的十个异常金融实践警报。这些警告信号是通过评估老练的激进卖空者的卖空行为来识别的，这些行为用于训练几种受监督的机器学习方法，以使用已发布的会计数据检测财务报表欺诈。我们的贡献包括系统地手动收集和标记被专业激进卖空者做空的公司。我们还在财务特征选择中将众所周知的资产定价因素与会计危险信号相结合。使用 80% 的数据进行训练，将剩余的 20% 用于样本外测试和性能评估，我们发现最好的方法是 XGBoost，召回率为 72%，F1 分数为 82%。其他方法的性能相对较低，证明了我们结果的稳健性。这表明，算法正在向他们学习的老练的激进卖空者具有出色的会计洞察力、丰富的法医分析知识和敏锐的商业头脑。我们的特征重要性分析表明，潜在卖空标的具有许多相似的财务特征，例如破产或财务困境风险、部分行业聚集、盈利能力不一致、高应计和不合理的业务运营。我们的结果表明，高级财务报表分析可能实现自动化，这既可以改进审计流程，又可以有效提高投资业绩。 最后，我们提出了统一投资者保护框架，从宏观到微观对投资者保护相关理论进行了总结和分类。