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Revealing Financial Insights: An Analytical Approach to S&P500 Companies Using Unsupervised Classification Techniques

Author

Listed:
  • Alexandra-Georgiana Sima

    (Bucharest University of Economic Studies)

  • Gheorghe Hurduzeu

    (Bucharest University of Economic Studies)

  • Stefan-Alexandru Ionescu

    (University of Bucharest)

  • Cristina Veith

    (University of Bucharest)

Abstract

This analysis delves into the financial dynamics of S&P 500 companies through the application of unsupervised classification techniques. Utilizing companies’ financial statements, our study aims to identify distinct financial patterns and scrutinize offshore profit retention practices. We group S&P 500 companies based on similarities in financial performance, unveiling common trends and potential outliers. Concurrently, Principal Component Analysis (PCA) reduces dimensionality, revealing key financial variables that significantly influence variability among these corporations. A focal point of our investigation is the examination of profits retained in offshore financial centers, providing insights into the strategies employed by SP500 companies. This analysis is crucial for investors, policymakers, and financial analysts seeking a deeper understanding of global financial strategies and their implications on corporate financial health. In conclusion, our study offers concise insights into the financial landscape of the S&P 500, utilizing advanced analytics to uncover patterns and trends. The findings contribute to informed decision-making for stakeholders while shedding light on offshore profit retention practices, emphasizing their broader impact on corporate financial strategies.

Suggested Citation

  • Alexandra-Georgiana Sima & Gheorghe Hurduzeu & Stefan-Alexandru Ionescu & Cristina Veith, 2025. "Revealing Financial Insights: An Analytical Approach to S&P500 Companies Using Unsupervised Classification Techniques," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-78179-7_21
    DOI: 10.1007/978-3-031-78179-7_21
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