Stock Styles Spotting

Published: 19 February 2024| Version 1 | DOI: 10.17632/k5373kc4s9.1
Mauricio Vargas Escobar


Research Hypothesis: The research aims to employ machine learning (ML) methods for identifying distinct stock styles through fundamental analysis. The hypothesis posits that by analyzing a comprehensive dataset comprising various stocks listed on major global stock exchanges, discernible patterns in stock characteristics and behaviors can be identified. These patterns may reflect underlying market dynamics, investor sentiments, and fundamental attributes influencing stock performance. Data Description: The dataset consists of 20 variables collected for 5,686 stocks listed on prominent global stock exchanges, including the Chicago Stock Exchange (CHX), Frankfurt Stock Exchange (FWB), London Stock Exchange (LSE), New York Stock Exchange (NYSE), and Tokyo Stock Exchange (TSE). The data were retrieved from Eikon's Refinitiv on August 28, 2023. The variables cover a range of fundamental indicators, financial metrics, and market-related parameters for each stock, providing a comprehensive overview of their characteristics and performance drivers. Notable Findings: Preliminary analysis of the dataset reveals a diverse array of stock styles across different industries and market segments. Significant variations in fundamental metrics such as earnings per share (EPS), price-to-earnings (P/E) ratio, market capitalization, and dividend yield indicate distinct patterns in stock behavior and valuation methodologies. Additionally, exploratory data analysis suggests potential correlations between certain fundamental indicators and stock performance, which can be further investigated through ML techniques. Interpretation and Utility: The dataset serves as a valuable resource for researchers, investors, and financial analysts interested in understanding and categorizing stock styles based on fundamental attributes. By applying ML algorithms to this dataset, it is possible to develop predictive models that can effectively identify and classify stocks into various investment styles. Such insights can inform investment strategies, portfolio construction, and risk management practices, enabling stakeholders to make informed decisions in the dynamic and complex landscape of financial markets.


Steps to reproduce

The data set was obtained from Refinitiv - Eikon's platform using Screener application. The Cluster column was added after thorough ML processes applied to the initial data set.


Universidad Privada Boliviana


Finance, Financial Investment, Applied Machine Learning