The field of investing has historically been examined through the lens of theories that were developed in the mid 20th century, such as Modern Portfolio Theory (MPT) and the Capital Asset Pricing Model (CAPM). Within the last 15 to 20 years, these theories have received increasing criticism, particularly since the mid 2010s. With the increased volatile nature of the financial market, due to natural crises or political factors, it became clear that new concepts are needed to complement the traditional theories to address the volatile market. In this study, we introduce a new approach for analyzing the financial markets that is based on the notion of population analysis. The proposed approach consists of leveraging the powerful tools associated with graph modeling and network algorithms to analyze the financial market from a different perspective. Graph theoretic approaches have been employed successfully in various fields of study, and here we demonstrate the value of exploring how such tools can be used to assess the performance of various financial sectors. We describe the strengths of this approach using a model constructed from the recent financial data of the top companies in the US stock market. The obtained results suggest that the proposed approach can complement traditional approaches in providing a more holistic assessment mechanism and serve as a launching pad for next-generation AI based methods.