Financial misstatement detection: a realistic evaluation

ArXiv ID: 2305.17457 “View on arXiv”

Authors: Unknown

Abstract

In this work, we examine the evaluation process for the task of detecting financial reports with a high risk of containing a misstatement. This task is often referred to, in the literature, as ``misstatement detection in financial reports’’. We provide an extensive review of the related literature. We propose a new, realistic evaluation framework for the task which, unlike a large part of the previous work: (a) focuses on the misstatement class and its rarity, (b) considers the dimension of time when splitting data into training and test and (c) considers the fact that misstatements can take a long time to detect. Most importantly, we show that the evaluation process significantly affects system performance, and we analyze the performance of different models and feature types in the new realistic framework.

Keywords: Financial Reporting, Misstatement Detection, Evaluation Framework, Class Imbalance, Time-Series Splitting

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper’s math is mostly standard statistical classification metrics (AUC, NDCG) and dataset handling, not heavy theoretical derivations. However, it demonstrates high empirical rigor by compiling large datasets, proposing a realistic temporal evaluation framework, and conducting systematic experiments on real financial reports (10-K), making it highly backtest-ready.

  flowchart TD
    A["Research Goal<br>Evaluate misstatement detection<br>with realistic constraints"] --> B["Methodology<br>Review literature &<br>propose new framework"]
    B --> C["Data/Inputs<br>Historical financial reports &<br>confirmed misstatements"]
    C --> D{"Evaluation Splits"}
    D --> E["Standard Random Split"]
    D --> F["Time-Series Split<br>Trains on past, tests on future"]
    E & F --> G["Computational Process<br>Train models: Logistic Regression,<br>RF, XGBoost, LSTM"]
    G --> H["Key Findings/Outcomes<br>Rarity of misstatements &<br>time-based splitting significantly<br>affect performance; Feature type<br>impacts model effectiveness"]