Conducting fundamental analysis within subsets of comparable firms has been demonstrated to provide more reliable inferences and increase the prediction quality in equity research. However, incorporating and representing both firm- specific information and common economic determinants has been widely recognized as the key challenge. This paper investigates how to leverage Semantic Web technologies to assist fundamental analysis by generating flexible and meaningful selections of comparable firms at low costs. We approach the problem by proposing Linked Open Financial Data as the data organization model and ontology modeling for knowledge representation. Results are verified in terms of efficiency with examples of quick mashups, and feasibility by adapting to existing valuation models.