Classifying stocks by measuring the similarity between them can provide investors with a reliable reference and help them earn more profits than before. This paper attempts to explore a convincing method to measure the similarity of international stocks. We selected the daily closing prices of 18 stocks from the Americas, Asia, Europe, and Australia, and mapped them as points into a three-dimensional space. In order to measure the similarity of stocks, we recommend calculating the Hurst surface distance as a distance matrix to classify stocks through the multidimensional scaling (MDS) method. We compare the classification results with classical MDS using Euclidean distance as similarity measure and MDS based on the (the detrending partial cross-correlation (DPXA) coefficient). The research results show that using Hurst surface distance as a reflection of similarity can not only provide more relevant information, but also distinguish the differences of economic fluctuations in different regions, while lays more emphasis on the similarities and differences within the same region. Both the two improved techniques for MDS are superior to the classic method based on Euclidean distance. In addition, the two methods can provide more detailed and clearer information.

通过衡量股票之间的相似性对股票进行分类，可以为投资者提供可靠的参考，帮助他们获得比以前更多的利润。本文试图探索一种令人信服的方法来衡量国际股票的相似性。我们选取了美洲、亚洲、欧洲和澳大利亚的 18 只股票的每日收盘价，并将它们作为点映射到一个三维空间中。为了衡量股票的相似性，我们建议将 Hurst 表面距离计算为距离矩阵，通过多维尺度 (MDS) 方法对股票进行分类。我们将分类结果与使用欧几里德距离作为相似性度量的经典 MDS 和基于（去趋势部分互相关 (DPXA) 系数）。研究结果表明，用Hurst面距离作为相似度的反映，不仅可以提供更多的相关信息，而且可以区分不同地区经济波动的差异，同时更加强调同一地区内的异同。MDS 的两种改进技术均优于基于欧几里德距离的经典方法。此外，这两种方法可以提供更详细、更清晰的信息。