Industry classification has been rigorously utilized in academic research and business analytics. The existing classification schemes, however, have been constructed and maintained manually by domain experts, which require exhaustive time and human effort while vulnerable to subjectivity. Hence, the existing classification systems do not properly reflect the fast-changing trends of the firms and the capital market. As a remedy to such shortcomings, this paper proposes a new classification scheme, Business Text Industry Classification (BTIC), namely, that automatically clusters securities based on the textual information from the corporate disclosures. BTIC exploits the business section of the Form 10-Ks, in which firms provide their self-identities in a rich context. We employ doc2vec for document embedding and apply Ward's hierarchical clustering method to categorize securities into BTIC groups. Evaluation results using 12 financial ratios commonly found in financial research show that BTIC performs just as good as SIC and GICS in terms of inter-and intra-industry homogeneity, especially for the higher level of clustering. Given that, we claim that BTIC outperforms SIC and GICS in four aspects: process automation, objectivity, clustering flexibility, and result interpretability.