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Sector-Level Analysis and Clustering of S&P 500 Companies Using Financial Metrics and Machine Learning

Tanishk Yadav · 2024 · ORCID: 0009-0006-2382-9411

Comprehensive analysis of sector-level performance within the S&P 500, combining financial metrics, hierarchical clustering, and neural networks. The study classified 66 sub-sectors into monopolistic, duopolistic, and oligopolistic market structures using a custom overperformance index with squared-weight allocation. A multi-layer perceptron achieved 93% classification accuracy across 4 identified sector clusters. The methodology integrates market capitalization growth, revenue growth, weighted-vs-simple variance, and short/long-term beta covariances.