Accounting-Based Models

Fraud Detection and Expected Returns ArXiv ID: ssrn-1998387 “View on arXiv” Authors: Unknown Abstract An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probabilit Keywords: Accounting-Based Models, Fraud Detection, Cross-Sectional Returns, Predictive Analytics, Financial Statement Analysis, Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses an accounting-based predictive model (high empirical data focus) with statistical validation and out-of-sample testing, but the mathematics described are primarily regression-based and do not involve advanced calculus or complex theoretical derivations. flowchart TD A["Research Goal: Does an accounting-based model<br>predict fraud AND future returns?"] --> B["Methodology: Predictive Analytics<br>Logistic Regression & Cross-Validation"] B --> C["Data Inputs:<br>Financial Statements & Stock Returns"] C --> D["Computational Process:<br>Estimate Prob(Fraud) using Accounting Ratios"] D --> E{"Key Findings"} E --> F["Strong Out-of-Sample Fraud Detection"] E --> G["Predict Cross-Sectional Returns"]