[rede.APPIA] DaSSWeb – Data Science and Statistics Webinar – 6 Fev – Jorge Caiado- Classification and Clustering of Financial Data

This paper (Bastos and Caiado, 2021) introduces a concise set of 10 features that effectively capture key empirical facts in financial markets. Employing both supervised and unsupervised machine learning techniques, the study demonstrates that this feature set outperforms the widely acknowledged 22 canonical features proposed by Lubba et al. (2019) in discriminating between different asset types. The empirical study is conducted using two datasets: one comprising international equity market indices classified as “developed” and “emerging,” and another involving large capitalization stock indices and foreign exchange rates. The research aims to assess the discriminatory power of the proposed features in distinguishing between emerging and developed markets, comparing their performance against the canonical features. Additionally, the study extends its analysis to differentiate between stock indices and foreign exchange rates, highlighting the potential applications of the feature set in diverse financial contexts.