Aiming at supporting the process of investment portfolio diversification by using a data-driven approach, the present methodological paper proposes a new cluster analysis, which compares publicly traded companies, mainly in times of high volatility (e.g. crisis times). The main goal of the proposed method is to provide a less arbitrary analysis to support financial investors to precisely measure the degree of similarity between equity stocks, unveiling equity market clustering patterns by applying analytic geometry solutions and calculating an overall clustering pattern indicator. Empirical results on synthetic data demonstrate either that the proposed method has conceptual superiority over traditional cluster analyses and its potential practical usefulness to asset allocation, portfolio strategy, asset pricing, among other related purposes. Finally, the outputs of the proposed cluster analysis are presented through an intuitive and easily understandable mathematical visualization. •It is proposed a new method to calculate risk-similarity and clustering patterns.•The method unveils clustering patterns through a data-driven process.•Portfolio diversification can benefit from sphere-sphere intersection calculations.