Selecting Valuable Mask Topic Stocks through Ontology Reasoning

Abstract

Abstract Due to COVID-19, masks are in short supply. Accordingly, mask topic stocks have surged as well. However, faced with various mask topic stocks, plenty of individual investors can only blindly follow the trend, but lack of objective judgment. In light of this, an ontology-based stocks selection framework was proposed. Different from most prior methods, the proposed framework starts from fundamental analysis and combines qualitative knowledge and quantitative data. Concretely, qualitative knowledge refers to news, information of executives and industry chain partners, while qualitative data are the financial ratios from the financial statements of companies. Notably, supply chain information was also introduced to address the delay of statements disclosure. Moreover, with the risk preference coefficient, the proposed framework can adapt to investors with different risk preference. Lastly, the results of case study are basically consistent with the research results from four investment institutions, which proves the practicality and effectiveness of the proposed framework.