Dissertation Topic:
Valuation Accuracy in U.S. Mergers and Acquisitions: A Comparative Analysis of Pre- and Post-Merger Performance in the IT and Healthcare Sectors Using Financial Ratios and CAPM-Based OLS Models
Objective:
To assess whether M&A transactions lead to accurate pre-merger valuations by comparing financial performance before and after the deal. The study will analyze 20 publicly listed U.S.-based companies—10 from the IT sector and 10 from the Healthcare sector—using quantitative methods.
Key Requirements:
• Word Count: 13,500–16,500 (excluding appendices, references, and tables)
• Structure: Must follow Newcastle Business School’s official dissertation format, including:
Title page
Abstract (200–300 words)
Introduction
Literature Review
Methodology (including ethical considerations)
Findings
Analysis/Discussion
Conclusions and Recommendations
References (APA 7th Edition)
Appendices (reflective statement, data tables, etc.)
Methodology Details:
• Quantitative research using financial ratios and OLS regression analysis
• Use of CAPM-based variables such as alpha, beta, expected returns
• Inclusion of a constant term and dummy variables for sector
• Statistical testing (t-tests, regression significance, etc.) to test hypotheses
Hypotheses to be Tested:
1. Significant difference in financial performance between pre- and post-merger periods.
2. Post-merger valuation outcomes differ significantly between IT and Healthcare sectors.
3. Financial ratios and CAPM variables significantly explain post-merger valuation.
Data Requirements:
• U.S.-based and publicly listed companies
• Data from 2019–2024
• Sources: Bloomberg, Yahoo Finance, company reports, etc.
Expectations:
• Critical academic literature engagement (30+ sources from 2019–2024)
• Formal academic writing style with APA referencing
• No plagiarism (Turnitin checked)
• Share data files and regression outputs if applicable