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"]