Business Taxonomy Construction Using Concept-Level Hierarchical Clustering
This provides a tool for investors to analyze emerging markets, but it is incremental as it adapts existing clustering techniques to a new domain.
The authors tackled the problem of constructing business taxonomies for emerging markets by proposing an automatic method using concept-level hierarchical clustering on corporate annual reports, which showed advantages in experiments on the Chinese NEEQ market.
Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate classification for small companies with innovative business models. Second, emerging markets are fast-developing, thus the static business taxonomies cannot promptly reflect the new features. In this article, we propose a new method to construct business taxonomies automatically from the content of corporate annual reports. Extracted concepts are hierarchically clustered using greedy affinity propagation. Our method requires less supervision and is able to discover new terms. Experiments and evaluation on the Chinese National Equities Exchange and Quotations (NEEQ) market show several advantages of the business taxonomy we build. Our results provide an effective tool for understanding and investing in the new growth companies.