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Research: Integrating Textual Analysis and Financial Ratios for Detecting Financial Sta...

Field: Finance Type: Research project Bloom: Create / Evaluate Level: Final-year / PG capstone Inspired by: MIT / Stanford / Oxford research agendas

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About this project

Research: Integrating Textual Analysis and Financial Ratios for Detecting Financial Statement Fraud in Public Firms

Research question: Can combining textual analysis of financial disclosures with ratio analytics improve the detection accuracy of financial-statement fraud in public companies?

Background & Motivation: Financial statement fraud undermines market integrity and investor confidence, costing billions annually. Traditional detection methods rely heavily on quantitative ratio analysis, often missing subtle cues embedded in corporate disclosures.

Research Gap: Recent advances in natural language processing (NLP) enable automated analysis of textual content, yet few studies rigorously integrate text analytics with financial ratios to identify fraudulent reporting. The effectiveness of such a combined approach remains under-explored.

Approach & Contribution: This project will construct a dataset of public firm filings, apply NLP to annual reports and managerial commentary, and combine these features with established anomaly-detection techniques using financial ratios. Statistical and machine-learning models will evaluate whether integrated analytics outperform standalone methods.

Why It Matters: Enhanced fraud detection can help regulators, auditors, and investors better identify misleading reporting, improving financial transparency and reducing systemic risk.

Milestones

1. Literature Review & Problem Definition

15 marks 21d

Survey academic and industry publications on fraud detection, text analytics, and financial ratios, and clearly define the research problem.

2. Research Proposal & Hypotheses

10 marks 14d

Develop a structured research proposal, including specific hypotheses regarding the effectiveness of integrated analytics.

3. Methodology & Experimental Design

15 marks 21d

Design the methodology, detailing data sources, NLP techniques, ratio calculations, and statistical evaluation methods.

4. Data Collection / Experimentation

15 marks 21d

Acquire SEC filings, preprocess texts, calculate ratios, and generate integrated features for model training/testing.

5. Analysis & Results

25 marks 28d

Analyze model performance, compare detection accuracy, and interpret statistical significance of findings.

6. Thesis Write-up & Defense

20 marks 21d

Compile the research report, synthesize findings, and prepare for oral defense before examiners.

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Upcoming sessions

Session	Window	Enrolled
Research: Integrating Textual Analysis and Financial Rati...	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

ResearchFinanceSystematic literature reviewHypothesis formationQuantitative and qualitative research designData preprocessing and feature engineeringStatistical analysis and machine learningNatural language processing (NLP)Critical evaluation of resultsAcademic writing and presentation

Tools used

Python (NumPypandasscikit-learnNLTKspaCy)R for statistical analysisEDGAR or SEC filings datasetFinancial ratio calculation toolsText mining and NLP techniques (TF-IDFsentiment analysistopic modeling)Machine learning classifiers (Random ForestLogistic Regression)Confusion matrices and ROC-AUC evaluation

Prerequisites

Financial AccountingStatistics and EconometricsProgramming for Data Science (Python or R)Introduction to Machine LearningCorporate Finance

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