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Clustering and Regression Analysis of Financial Health and Stock Performance
Unlocking Insights for Better Investment Decisions and Risk Management
Introduction
Understanding a company’s financial health is crucial for making informed investment decisions. This project explores how clustering and regression analysis can be applied to financial data to assess companies’ resilience and predict stock performance. By grouping companies based on their financial metrics and analyzing stock price trends, we uncover valuable insights that can drive better investment strategies and risk management.
Problem Statement
The financial health of companies is a critical determinant of their stock performance and overall market stability. Investors and risk managers rely on comprehensive analyses of financial metrics to make informed decisions. Predicting stock prices and understanding financial resilience are key components of financial risk management. However, identifying meaningful patterns within financial data remains a challenge.
Research Questions
- What financial metrics can accurately assess a company’s financial health and predict future performance?
- How can companies be grouped based on their financial profiles, and…
